World Electric Vehicle Journal doi: 10.3390/wevj15040140
Authors: Meiling He Qipeng Li Tianhe Lin Jiangyang Fan Xiaohui Wu Xun Han
The strategic development of reverse logistics networks is crucial for addressing the common challenge of low recovery rates for end-of-life vehicles (ELVs) in China. To minimize the total cost of the reverse logistics network for ELVs, this paper proposes a mixed-integer linear programming (MILP) model. The model considers the recycling volume of different vehicle types, facility processing capacity, and the proportions of parts and materials. Building on this foundation, a fuzzy mixed-integer nonlinear programming (FMINLP) model is developed to account for the inherent uncertainty associated with recycling volumes and facility processing capacities. The model was solved using Lingo, and its effectiveness was validated using Jiangsu Province of China as a case study, followed by a sensitivity analysis. The results indicate that dismantling and machining centers incur the highest processing costs. Variations in recycling volume and facility handling capacity significantly impact total costs and site selection, with the former having a more pronounced effect. Increasing facility processing capacity effectively increases the recovery rate. Moreover, a higher confidence level corresponds to higher total costs and a greater demand for facilities.
]]>World Electric Vehicle Journal doi: 10.3390/wevj15040139
Authors: Jianwei Ma Fengyi Gu Liwei Wang Shilun Ma Amir-Mohammad Golmohammadi Shaohang Zhang
Silicon rectifier generators, which are single-excitation generators, are commonly used in vehicles. However, a traditional single-excitation generator cannot satisfy the requirements of modern vehicles due to its low efficiency, high failure rate and large excitation loss; a hybrid excitation generator is more suitable for a wide range of applications in vehicles because of its many advantages. In this study, a novel, high-efficiency and energy-saving hybrid excitation generator with a claw-pole series magnetic circuit for vehicles was designed. The magnetic circuit and principle of operation were analyzed. The structure parameters of the hybrid excitation generator were initially designed according to motor design theory. The model of the hybrid excitation generator was built based on the finite element method, and the no-load characteristic was analyzed. Furthermore, the influences of the permanent magnet thickness and core slot width on the performance of the generator were analyzed. According to the results, the structural parameters were optimized. The no-load output characteristics and load characteristics were compared between the generator and a silicon rectifier generator, and the test results show that the design, simulation and optimization methods were reasonable. This provides theoretical support and research methods for the development of a hybrid excitation generator.
]]>World Electric Vehicle Journal doi: 10.3390/wevj15040138
Authors: Qifeng Qian Ronghui Xiang Xiaohua Zeng Dafeng Song Xuanming Zhang
With the electrification and connectivity of vehicles in transportation, traditional vehicles with single drivetrains are being replaced by pure electric or hybrid electric vehicles (HEVs). This evolution has given rise to diverse electromechanical coupling drivetrains. There is a pressing need to update simulation software to assess the economic performance of vehicles in various environments, and promote sustainable development and energy conservation. This paper presents a unified framework for the construction and automated operation of large-scale automated vehicle simulations with multiple drivetrain types, facilitating synchronous information exchange among vehicles. Central to the framework is the development of a plug-and-play vehicle model based on a modular component design, facilitating the rapid assembly of vehicles with varied drivetrain configurations and standardizing simulation file management. Additionally, a standardized simulation process construction is established to accommodate the automated operation of simulations. Furthermore, a data scheduling method among vehicles is introduced to achieve multi-vehicle interconnection simulation. Finally, the effectiveness of the framework is demonstrated through a case study involving queue-following control for HEVs. This framework aims to provide a comprehensive solution for quickly establishing automated simulation environments for multi-vehicle interaction, enhancing model reusability and scalability.
]]>World Electric Vehicle Journal doi: 10.3390/wevj15040137
Authors: Xingzheng Zhu Hua Fan Shiyao Zhang Jiao Du
The Electric Bus Dynamic Wireless Charging (EB-DWC) system is a bus charging system that enables electric buses to receive power wirelessly from ground-based electromagnetic induction devices. In this system, how to optimally configure the charging infrastructures while considering the unpredictable nature of bus movement is a great challenge. This paper presents an optimization problem for an EB-DWC system in urban settings, addressing stochastic elements inherent in the vehicle speed, initial charging state, and dwell time at bus stops. We formulate a stochastic planning problem for the EB-DWC system by integrating these uncertainties and apply Monte Carlo sampling techniques to effectively solve this problem. The proposed method can improve the system’s robustness effectively.
]]>World Electric Vehicle Journal doi: 10.3390/wevj15040136
Authors: Omar Boubker Marwan Lakhal Youssef Ait Yassine Hicham Lotfi
In recent years, many countries have actively promoted sustainable mobility as part of their efforts to decarbonize transportation through automotive electrification. Therefore, identifying the factors that influence individuals’ interest in using electric cars (ECs) is crucial for guiding public opinion toward choosing this sustainable mode of transportation. Consequently, the present study mobilized the theory of planned behavior and the technology acceptance model to interpret the various factors influencing the intention to adopt ECs in a developing country. Following the developed model, data were collected from individuals using cars in Morocco through an online questionnaire. Data analysis using structural equation modeling revealed a positive influence of relative advantage on both the perceived ease of use and green perceived usefulness. Furthermore, the perceived ease of use, green perceived usefulness, environmental concern, and social influence positively affected attitudes toward using ECs. Similarly, these results confirmed that green perceived usefulness and individual attitudes positively enhance ECs adoption intention. These findings contribute to the literature related to ECs adoption and offer guidance to policymakers on promoting ECs adoption in developing countries.
]]>World Electric Vehicle Journal doi: 10.3390/wevj15040135
Authors: Xinghui Chen Xinghua Hu Ran Wang Jiahao Zhao
Transit priority control is not only an important means for improving the operating speed and reliability of public transport systems, but it is also a key measure for promoting green and sustainable urban transportation development. A review of signal intersection transit priority control strategy in a connected vehicle environment is conducive to discovering important research results on transit priority control at home and abroad and will promote further developments in urban public transport. This study analyzed and reviewed signal intersection transit priority control at four levels: traffic control sub-area divisions, transit signal priority (TSP) strategy, speed guidance strategy, and the impacts of intersection signal control on carbon emissions. In summary, the findings were the following: (1) In traffic control sub-area divisions, the existing methods were mainly based on the similarity of traffic characteristics and used clustering or search methods to divide the intersections with high similarity into the same control sub-areas. (2) The existing studies on the TSP control strategy have mainly focused on transit priority control based on fixed phase sequences or phase combinations under the condition of exclusive bus lanes. (3) Studies on speed guidance strategy were mainly based on using constant bus speeds to predict bus arrival times at intersection stop lines, and it was common to guide only based on bus speed. (4) The carbon emissions model for vehicles within the intersection mainly considered two types of vehicles, namely, fuel vehicles and pure electric vehicles. Finally, by analyzing deficiencies in the existing studies, future development directions for transit priority control are proposed.
]]>World Electric Vehicle Journal doi: 10.3390/wevj15040134
Authors: Giuliano Rancilio Alessia Cortazzi Giacomo Viganò Filippo Bovera
The diffusion of electric vehicles is fundamental for transport sector decarbonization. However, a major concern about electric vehicles is their compatibility with power grids. Adopting a whole-power-system approach, this work presents a comprehensive analysis of the impacts and benefits of electric vehicles’ diffusion on a national power system, i.e., Italy. Demand and flexibility profiles are estimated with a detailed review of the literature on the topic, allowing us to put forward reliable charging profiles and the resulting flexibility, compatible with the Italian regulatory framework. Distribution network planning and power system dispatching are handled with dedicated models, while the uncertainty associated with EV charging behavior is managed with a Monte Carlo approach. The novelty of this study is considering a nationwide context, considering both transmission and distribution systems, and proposing a set of policies suitable for enabling flexibility provision. The results show that the power and energy demand created by the spread of EVs will have localized impacts on power and voltage limits of the distribution network, while the consequences for transmission grids and dispatching will be negligible. In 2030 scenarios, smart charging reduces grid elements’ violations (−23%, −100%), dispatching costs (−43%), and RES curtailment (−50%).
]]>World Electric Vehicle Journal doi: 10.3390/wevj15040133
Authors: Edward Heath Rick Wolbertus Renée Heller
The ever-increasing electrification of society has been a cause of utility grid issues in many regions around the world. With the increased adoption of electric vehicles (EVs) in the Netherlands, many new charge points (CPs) are required. A common installation practice of CPs is to group multiple CPs together on a single grid connection, the so-called charging hub. To further ensure EVs are adequately charged, various control strategies can be employed, or a stationary battery can be connected to this network. A pilot project in Amsterdam was used as a case study to validate the Python model developed in this study using the measured data. This paper presents an optimisation of the battery energy storage capacity and the grid connection capacity for such a P&R-based charging hub with various load profiles and various battery system costs. A variety of battery control strategies were simulated using both the optimal system sizing and the case study sizing. A recommendation for a control strategy is proposed.
]]>World Electric Vehicle Journal doi: 10.3390/wevj15040132
Authors: Kamal Rsetam Jasim Khawwaf Yusai Zheng Zhenwei Cao Zhihong Man
The modern steer-by-wire (SBW) systems represent a revolutionary departure from traditional automotive designs, replacing mechanical linkages with electronic control mechanisms. However, the integration of such cutting-edge technologies is not without its challenges, and one critical aspect that demands thorough consideration is the presence of nonlinear dynamics and communication network time delays. Therefore, to handle the tracking error caused by the challenge of time delays and to overcome the parameter uncertainties and external perturbations, a robust fast finite-time composite controller (FFTCC) is proposed for improving the performance and safety of the SBW systems in the present article. By lumping the uncertainties, parameter variations, and exterior disturbance with input and output time delays as the generalized state, a scaling finite-time extended state observer (SFTESO) is constructed with a scaling gain for quickly estimating the unmeasured velocity and the generalized disturbances within a finite time. With the aid of the SFTESO, the robust FFTCC with the scaling gain is designed not only for ensuring finite-time convergence and strong robustness against time delays and disturbances but also for improving the speed of the convergence as a main novelty. Based on the Lyapunov theorem, the closed-loop stability of the overall SBW system is proven as a global uniform finite-time. Through examination across three specific scenarios, a comprehensive evaluation is aimed to assess the efficiency of the suggested controller strategy, compared with active disturbance rejection control (ADRC) and scaling ADRC (SADRC) methods across these three distinct driving scenarios. The simulated results have confirmed the merits of the proposed control in terms of a fast-tracking rate, small tracking error, and strong system robustness.
]]>World Electric Vehicle Journal doi: 10.3390/wevj15040131
Authors: Feng Zhao Yun Guo Baoming Chen
With the advancement of machine-learning and deep-learning technologies, the estimation of the state of charge (SOC) of lithium-ion batteries is gradually shifting from traditional methodologies to a new generation of digital and AI-driven data-centric approaches. This paper provides a comprehensive review of the three main steps involved in various machine-learning-based SOC estimation methods. It delves into the aspects of data collection and preparation, model selection and training, as well as model evaluation and optimization, offering a thorough analysis, synthesis, and summary. The aim is to lower the research barrier for professionals in the field and contribute to the advancement of intelligent SOC estimation in the battery domain.
]]>World Electric Vehicle Journal doi: 10.3390/wevj15040130
Authors: Jun Ma Yuqi Gong Wenxia Xu
The increasing level of intelligence in automobiles is driving a shift in the human–machine relationship. Users are paying more attention to the intelligent cabin and showing a tendency toward customization. As culture is considered to be an important factor in guiding user behavior and preference, this study innovatively incorporates cultural and human factors into the model to understand how individual cultural orientation influences user preference for innovative human-machine interaction (HMI) features. Firstly, this study considered five Hofstede cultural dimensions as potential impact factors and constructed a prediction model through the random forest algorithm so as to analyze the influence mechanism of culture. Subsequently, K-means clustering was used to classify the sample into three user groups and then predict their preferences for the innovative features in the intelligent cabin. The results showed that users with a higher power distance index preferred a sense of ceremony and show-off-related features such as ambient lighting and welcome mode, whereas users with high individualism were keen on a more open and personalized in-vehicle information system. Long-term orientation was found to be associated with features that help to improve efficiency, and users with a lower level of uncertainty avoidance and restraint were more likely to be attracted to new features and were also more willing to use entertainment-related features. The methodology developed in this study can be widely applied to people in different countries, thus effectively exploring the personal requirements of different individuals, guiding further user experience design and localization when breaking into a new market.
]]>World Electric Vehicle Journal doi: 10.3390/wevj15040129
Authors: Mahipal Bukya Bhukya Padma Rajesh Kumar Akhilesh Mathur Nisha Prasad
As the adoption of electric vehicles (EVs) continues to rise, attention has switched to ensuring the safety of EV operations. The exponential growth in battery technology over the past several years has changed the face of energy storage and sparked a revolution in several industries. The degradation of battery insulation during regular use is a significant concern. The high voltage (HV) and current levels in HV electric vehicles pose a significant electrical threat.The advancement of electric vehicle technology has led to an increasing presence of HV electric equipment throughout the vehicle. The insulation strength and early health status detection of the batteries are essential in ensuring safety in EVs. This paper studies the different insulation detection techniques and the development of adaptive filter (AF) algorithms based on field-programmable gate arrays (FPGAs) for insulation detection. FPGAs are amongst the most accurate and fast detection techniques among all the insulation detection techniques used so far in electric vehicles. This study proposes an FPGA-based VFF-RLS algorithm for effectively implementing insulation detection in EVs. The experimental test results using FPGAs demonstrate that the proposed method can rapidly monitor changes in insulation resistance (IR). The VFFRLS-based FPGA technique works sufficiently well to reduce errors when dealing with variations in voltage and resistance conditions at the battery terminals.
]]>World Electric Vehicle Journal doi: 10.3390/wevj15040128
Authors: Léa D’amore Daniele Costa Maarten Messagie
Further advances in hardware and software features are needed to optimize battery and thermal management systems to allow for the execution of longer trips in electric vehicles. This paper assesses the economic and environmental impacts of the following features: eco-charging, eco-driving, smart fast charging, predictive thermal powertrain and cabin conditioning, and an advanced heat pump system. A Total Cost of Ownership (TCO) and externalities calculation is carried out on two passenger cars and one light commercial vehicle (LCV). The energy consumption data from the vehicles are based on experiments. The analysis shows more benefits for the LCV, while the smart fast-charging feature on the car shows a slight increase in TCO. However, negative results did not contribute significantly compared to the ability to install a smaller battery capacity for similar use.
]]>World Electric Vehicle Journal doi: 10.3390/wevj15040127
Authors: Mariana de F. Ramos Dener A. de L. Brandao Diogo P. V. Galo Braz de J. Cardoso Filho Igor A. Pires Thales A. C. Maia
This work presents a study of the performance of prime mover and hydraulic implement electrification in a backhoe loader. The results are validated through simulation and experimental tests. The construction and agriculture sector has grown in recent years with the aid of compact non-road mobile machines. However, as is common in fossil fuel-powered vehicles, they significantly contribute to increasing emissions. Previous research has primarily relied on powertrain electrification to address the low-efficiency drawbacks. Notably, compact off-road vehicles comprise implements less discussed in the literature. A hybrid series topology is employed, where the rear implement is driven by an electrical drive and the Diesel engine is coupled to a generator. A rule-based energy management strategy is applied. The operation of the Diesel engine and electrical machines in optimal points of the efficiency maps are the basis of the analysis. The design is validated using simulations and experimental tests in a commercial backhoe loader as a benchmark. Experimental and simulation results obtained from the hybrid series backhoe loader applied to the hydraulic implement show a 33% reduction in fuel consumption, demonstrating the effectiveness of electrification in reducing emissions and fuel consumption of compact non-road mobile machines.
]]>World Electric Vehicle Journal doi: 10.3390/wevj15040126
Authors: Weizhen Zhu Yuhao Zhang Weiwei Kong Fachao Jiang Pengxiao Ji
This article aims to address the unnecessary stopping and low efficiency issues present in existing multi-machine cooperative steering control methods. To tackle this challenge, a novel cooperative control approach for multiple agricultural machines is proposed, considering two typical steering modes of farm machinery. This approach encompasses a multi-machine cooperative control framework suitable for both steering modes. Based on the established lateral and longitudinal kinematics models of the farm machines, the method includes a path-tracking controller designed using the pure pursuit and Stanley algorithms, a formation-keeping controller based on PID control, and a T-turn cooperative-steering controller based on a problem-solving approach. To assess the method’s viability, a collaborative simulation platform utilizing CarSim and Simulink was constructed, which conducted simulations for both U-turn and T-turn cooperative steering controls. The simulation results indicate that the proposed control framework and methodology can effectively ensure no collision risk during the U-turn and T-turn cooperative steering processes for three farm machines, eliminating stopping in T-turn, enhancing safety, and improving fuel economy. Compared with traditional sequential control methods, the proposed approach reduced operation time by 17.47 s and increased efficiency by 15.29% in the same scenarios.
]]>World Electric Vehicle Journal doi: 10.3390/wevj15040125
Authors: DaiBin Tang Fei Lu Siaw Tzer Hwai Gilbert Thio
The utilization of photovoltaic (PV) generation to charge storage batteries in recreational vehicles (RVs) is becoming increasingly prevalent. However, the performance of PV generation systems is hindered by the mismatch caused by different module types and varying environmental conditions. This discrepancy negatively impacts the output performance of PV modules, resulting in reduced system efficiency. To address this issue, this paper explored the series–parallel output characteristics of different types of PV modules and summarized the methods for configuring PV modules in a mixed-structure PV generation system for RV energy supplementation. Building upon this foundation, a novel equalization scheme based on extremum-seeking control (ESC) is introduced. The scheme initially employs a forward–flyback converter (FFC) to equalize the current among series-connected PV modules, followed by matching the voltage between parallel-connected PV module strings. Finally, the ESC is utilized to optimize the real-time output power of the PV generation system, thereby enhancing overall system efficiency. Through simulation experiments conducted on a PV generation system with four types of mixed-connection PV modules employing the PLECS simulation platform, simulated results demonstrate the effectiveness of the proposed scheme in improving PV module output performance and maximum power tracking efficiency. The simulation data reveal that the proposed scheme achieves an impressive average tracking efficiency of 99.15%, surpassing the efficiency of the global maximum power point tracking scheme based on an enhanced perturb and observe algorithm.
]]>World Electric Vehicle Journal doi: 10.3390/wevj15040124
Authors: Guangye Li Shouming Lv Renming Yang
The three-phase voltage type Pulse Width Modulation (PWM) rectifier is widely used in the front-end power factor of electric vehicle wireless charging systems due to its simple control structure and easy implementation. The system often adopts a double closed-loop PI control method based on voltage and current, which inevitably leads to a significant starting current surge and poses significant risks to the safe operation of the equipment. On the basis of establishing a mathematical model for PWM rectifiers, this article analyzes in detail the causes of starting over-current and designs a starting strategy with a voltage outer proportional and integral separated active current directly given. Simulation experiments show that this method can reduce the starting over-current of PWM rectifiers and the excessive DC voltage surge towards normal operation during the starting process.
]]>World Electric Vehicle Journal doi: 10.3390/wevj15030123
Authors: Di Wu Zhihao Ma Weiping Xu Haifeng He Zhenlin Li
To address the problem of system instability during vehicle low-speed driving, we propose improving the visual odometer using ORB (Oriented FAST and Rotated BRIEF) features. The homogeneity of ORB features leads to poor corner point properties of some feature points. When the environmental texture lacks richness, it leads to poor matching performance and low matching accuracy of the feature points. We solve the problem of the corner point properties of feature points using weight calculation for regions with different textures. When the vehicle speed is too low, the continuous frames captured by the camera will overlap significantly, causing large fluctuations in the system error. We use motion model estimation to solve this problem. Meanwhile, experimental validation using the KITTI dataset achieves good results.
]]>World Electric Vehicle Journal doi: 10.3390/wevj15030122
Authors: Feng Zhao Jiexin An Qiang Chen Yong Li
Intelligentization is the development trend of the future automobile industry. Intelligentization requires that the dynamic control of the vehicle can complete the trajectory tracking according to the trajectory output of the decision planning the driving state of the vehicle and ensure the driving safety and stability of the vehicle. However, trajectory limit planning and harsh road conditions caused by emergencies will increase the difficulty of trajectory tracking and stability control of unmanned vehicles. In view of the above problems, this paper studies the trajectory tracking and stability control of distributed drive unmanned vehicles. This paper applies a hierarchical control framework. Firstly, in the upper controller, an adaptive prediction time linear quadratic regulator (APT LQR) path following algorithm is proposed to acquire the desired front-wheel-steering angle considering the dynamic stability performance of the tires. The lateral stability of the DDAUV is determined based on the phase plane, and the sliding surface, in the improved sliding mode control (SMC), is further dynamically adjusted to obtain the desired additional yaw moment for coordinating the path following and lateral stability. Then, in the lower controller, considering the slip and the working load of four tires, a comprehensive cost function is established to reasonably distribute the driving torque of four in-wheel motors (IWMs) for producing the desired additional yaw moment. Finally, the proposed control algorithm is verified by the hardware-in-the-loop (HIL) experiment platform. The results show the path following and lateral stability can be coordinated effectively under different driving conditions.
]]>World Electric Vehicle Journal doi: 10.3390/wevj15030121
Authors: Divyakumar Bhavsar Ramesh Kaipakam Jaychandra Mayank Mittal
Data acquisition from a vehicle operating in real driving conditions is extremely useful for analyzing the real-time behavior of the vehicle and its components. A few studies have measured the real-time data for a four-wheeler electric vehicle. However, no attempts have been reported to measure the real-time data and find the inverter efficiency for a two-wheeler electric vehicle. The present work has accomplished successful real-time data acquisition from a two-wheeler electric vehicle. The real-time current and voltage coming in and going out from the inverter, frequency of the motor operation, power factor, distance covered, and velocity have been measured. The inverter efficiency is found to be over 95% for over 80% of the total drive time, and the power factor for the motor is over 0.8 for almost 50% of the total drive time. A few insights on driver behavior and finally the torque-speed characteristics and two quadrant operation of the motor are discussed.
]]>World Electric Vehicle Journal doi: 10.3390/wevj15030120
Authors: Weijian Jia Xixia Liu Chuanqing Zhang Dabing Xue Shaoliang Zhang
A position estimation method for unmanned tracked vehicles based on a steering dynamics model was developed during this study. This method can be used to estimate the position of a tracked vehicle in real time without relying on a high-precision positioning system. First, the relationship between the shear displacement of the track relative to the ground and the speed and yaw rate of the tracked vehicle during the steering process was analyzed. Next, the steering force of the tracked vehicle was calculated by using the shear force–displacement theory, and a steering dynamics model considering the acceleration of the vehicle was established. The experimental results show that this steering dynamics model produced more accurate position estimations for an unmanned tracked vehicle than did the kinematics model. This method can serve as a reference for the positioning of unmanned tracked vehicles working in special environments that cannot use precise positioning systems.
]]>World Electric Vehicle Journal doi: 10.3390/wevj15030119
Authors: Nima Arish Maarten J. Kamper Rong-Jie Wang
The evolution of electric propulsion systems in the maritime sector has been influenced significantly by technological advancements in power electronics and machine design. Traditionally, these systems have employed surface-mounted permanent magnet synchronous motors (PMSMs) in podded configurations. However, the advent of permanent magnet Vernier motors (PMVMs), which leverage magnetic gearing effects, presents a novel approach with promising potential. This study conducts a comparative analysis between PMVMs and conventional PMSMs at a power level of 5 MW for podded ship propulsion, with a particular focus on the impact of gear ratios (Gr). An objective function was developed that integrates motor dimension constraints and the power factor (PF), a critical yet frequently neglected parameter in existing research. The findings indicate that PMVMs with lower Gr have lower mass and cost compared to those with higher Gr and traditional PMSMs, at a PF level of 0.7, which is high for Vernier machines. Moreover, PMVMs with lower Gr achieve efficiencies exceeding 99%, outperforming both their higher Gr counterparts and conventional PMSMs. The superior performance of PMVMs is attributed to lower current density and reduced copper loss, which contribute to their enhanced thermal performance. These details are elaborated on further in the paper. Consequently, these findings suggest that PMVMs with lower Gr are particularly well suited for high-power maritime propulsion applications, offering advantages in terms of compactness, efficiency (EF), cost-effectiveness, and thermal performance.
]]>World Electric Vehicle Journal doi: 10.3390/wevj15030118
Authors: Mohammad Rabih Maen Takruri Mohammad Al-Hattab Amal A. Alnuaimi Mouza R. Bin Thaleth
This study compiles, reviews, and discusses the relevant history, present status, and growing trends in wireless electric vehicle charging. Various reported concepts, technologies, and available literature are discussed in this paper. The literature can be divided into two main groups: those that discuss the technical aspects and those that discuss the operations and systems involved in wireless electric vehicle charging systems. There may be an overlap of discussion in some studies. However, there is no single study that combines all the relevant topics into a guide for researchers, policymakers, and government entities. With the growing interest in wireless charging in the electric vehicle industry, this study aims to promote efforts to realize wireless power transfer in electric vehicles.
]]>World Electric Vehicle Journal doi: 10.3390/wevj15030117
Authors: Sangmin Lee Jinhyeok Oh Minchul Kim Myongcheol Lim Keon Yun Heesun Yun Chanmin Kim Juntaek Lee
With the rapid advancement of autonomous vehicles reshaping urban transportation, the importance of innovative traffic management solutions has escalated. This research addresses these challenges through the deployment of roadside units (RSUs), aimed at enhancing traffic flow and safety within the autonomous driving era. Our research, conducted in diverse road settings such as straight and traffic circle roads, delves into the RSUs’ capacity to diminish traffic density and alleviate congestion. Employing vehicle-to-infrastructure communication, we can scrutinize its essential role in navigating autonomous vehicles, incorporating basic safety messages (BSMs) and probe vehicle data (PVD) to accurately monitor vehicle presence and status. This paper presupposes the connectivity of all vehicles, contemplating the integration of on-board units or on-board diagnostics in legacy vehicles to extend connectivity, albeit this aspect falls beyond the work’s current ambit. Our detailed experiments on two types of roads demonstrate that vehicle behavior is significantly impacted when density reaches critical thresholds of 3.57% on straight roads and 34.41% on traffic circle roads. However, it is important to note that the identified threshold values are not absolute. In our experiments, these thresholds represent points at which the behavior of one vehicle begins to significantly impact the flow of two or more vehicles. At these levels, we propose that RSUs intervene to mitigate traffic issues by implementing measures such as prohibiting lane changes or restricting entry to traffic circles. We propose a new message set in PVD for RSUs: road balance. Using this message, RSUs can negotiate between vehicles. This approach underscores the RSUs’ capability to actively manage traffic flow and prevent congestion, highlighting their critical role in maintaining optimal traffic conditions and enhancing road safety.
]]>World Electric Vehicle Journal doi: 10.3390/wevj15030116
Authors: Dilshad Mohammed Balázs Horváth
Adaptive Cruise Control (ACC) systems have emerged as a significant advancement in automotive technology, promising safer and more efficient driving experiences. However, the performance of ACC systems can vary significantly depending on their type and underlying algorithms. This research presents a comprehensive comparative analysis of car-following distances in different types of Adaptive Cruise Control systems. We evaluate and compare three distinct categories of ACC systems using three different commercial vehicles brands. The study involves extensive real-world testing at Zalazone Proving Ground, to assess the performance of these systems under various driving conditions, including driving at multiple speeds and applying different car following scenarios. The study investigates how each ACC system manages the minimum following distances according to the type of ACC sensors in each tested vehicle. Our findings revealed that at low to medium ranges of constant driving speeds, there was an approximate linear increase in the average clearances between the two following vehicles for all applied scenarios, with comparatively shorter clearances obtained by the vision-based ACC system, while unstable measurements with a high level of dispersion for all ACC systems were observed at high range of driving speeds.
]]>World Electric Vehicle Journal doi: 10.3390/wevj15030115
Authors: Theodoros Kalogiannis Md Sazzad Hosen Joeri Van Mierlo Peter Van Den Bossche Maitane Berecibar
In this study, we present an innovative, fully automated, and digitalized methodology to optimize the energy efficiency and cost effectiveness of Li-ion battery modules. Advancing on from today’s optimization schemes that rely on user experience and other limitations, the mechanical and thermal designs are optimized simultaneously in this study by coupling 3D multi-physical behavior models to multi-objective heuristic optimization algorithms. Heat generation at various loading and ambient conditions are estimated with a physics-based, fractional-order battery cell-level model, which is extrapolated to a module that further accounts for the interconnected cells’ heat transfer phenomena. Several key performance indicators such as the surface temperature increase, the temperature variations on the cells, and heat uniformity within the module are recorded. For the air-cooled study case, the proposed coupled framework performs more than 250 module evaluations in a relatively short time for the whole available electro-thermal-mechanical design space, thereby ensuring global optimal results in the final design. The optimal module design proposed by this method is built in this work, and it is experimentally evaluated with a module composed of 12 series-connected Li-ion NMC/C 43Ah prismatic battery cells. The performance is validated at various conditions, which is achieved by accounting the thermal efficiency and pressure drop with regard to power consumption improvements. The validations presented in this study verify the applicability and overall efficiency of the proposed methodology, as well as paves the way toward better energy and cost-efficient battery systems.
]]>World Electric Vehicle Journal doi: 10.3390/wevj15030114
Authors: Anne Magdalene Syré Pavlo Shyposha Leonard Freisem Anton Pollak Dietmar Göhlich
Addressing the pressing challenge of global warming, reducing greenhouse gas emissions in the transportation sector is a critical imperative. Battery and fuel cell electric vehicles have emerged as promising solutions for curbing emissions in this sector. In this study, we conducted a comprehensive life cycle assessment (LCA) for typical passenger vehicles, heavy-duty trucks, and city buses using either proton-exchange membrane fuel cells or Li-ion batteries with different cell chemistries. To ensure accuracy, we supplemented existing studies with data from the literature, particularly for the recycling phase, as database limitations were encountered. Our results highlight that fuel cell and battery systems exhibit large emissions in the production phase. Recycling can significantly offset some of these emissions, but a comparison of the technologies examined revealed considerable differences. Overall, battery electric vehicles consistently outperform fuel cell electric vehicles regarding absolute greenhouse gas emissions. Hence, we recommend prioritizing battery electric over fuel cell vehicles. However, deploying fuel cell electric vehicles could become attractive in a hydrogen economy scenario where other factors, e.g., the conversion and storage of surplus renewable electricity via electrolysis, become important.
]]>World Electric Vehicle Journal doi: 10.3390/wevj15030113
Authors: Philipp Hoth Ludger Heide Alexander Grahle Dietmar Göhlich
Recent developments in vehicle-integrated photovoltaics (VIPV) offer prospects for enhancing electric vehicle range, lowering operating costs, and supporting carbon-neutral transport, particularly in urban settings. This study evaluates the solar energy potential of parking spaces in Berlin, considering challenges like building and tree shading using digital surface models and weather data for solar simulations. Utilizing open datasets and software, the analysis covered 48,827 parking spaces, revealing that VIPV could extend vehicle range by 7 to 14 km per day, equating to a median annual increase of 2527 km. The findings suggest median yearly cost savings of 164 euros from reduced grid charging. However, the environmental benefits of solar vehicle charging were found to be less than those of traditional grid-connected photovoltaic systems. The study introduces a method to pinpoint parking spaces that are most suitable for solar charging.
]]>World Electric Vehicle Journal doi: 10.3390/wevj15030112
Authors: Mingfei Li Fabian Kai-Dietrich Noering Yekta Öngün Michael Appelt Roman Henze
To ensure the precise dimensioning and effective testing of drivetrain components, it is crucial to have a thorough understanding of customer requirements, with a particular emphasis on customer stress on these components. An accurate interpretation of customer data is essential for determining representative customer requirements, such as load collectives. The automobile industry has faced challenges in analyzing large amounts of customer driving data to obtain representative load collectives as target values in durability design. However, due to technical limitations and cost constraints, collecting data from a large sample size is not feasible. The ongoing digitalization of the automotive industry, driven by an increasing number of connected vehicles, enhances data-based and customer-oriented development. This paper investigates representative customer load collectives using cloud data from over 40,000 customer vehicles to lay the groundwork for realizing robust requirement engineering. A systematic method for analyzing big data on the cloud was introduced. The derived component-specific damage distribution from these collectives adopts a unique approach, utilizing the 1% vehicle term instead of the common 1% customer term to represent typical customer stress. This study shows that the driven mileage and the number of vehicles are crucial factors in 1% vehicle analysis. An analysis of the characteristics of the 1% vehicle is conducted, followed by an exploration to determine the required vehicle quantity for obtaining stable results. The shape parameter of the damage distribution determines the necessary number of vehicles for a reliable conclusion. Additionally, a comparative analysis of market-specific customer requirements between the US and Europe is presented, and real usage differences in customer operations are explained using an operating point frequency heatmap. The information presented in this paper provides valuable input for optimizing durability design and conducting efficient, customer-oriented tests, resulting in significant reductions in development time and costs.
]]>World Electric Vehicle Journal doi: 10.3390/wevj15030111
Authors: John Robin R. Uy Ardvin Kester S. Ong Josephine D. German
The wide-scale integration of electric vehicles (EVs) in developed countries represents a significant technological innovation and a step toward reducing carbon emissions from transportation. Conversely, in developing nations like the Philippines, the adoption and availability of EVs have not been as rapid or widespread compared to other countries. In identifying this gap, this study delved into the preferences and factors influencing Filipino consumers’ willingness to purchase EVs. The study gathered 311 valid responses utilizing conjoint analysis with an orthogonal approach to assess the attributes influencing customers’ purchase decisions. Conjoint analysis tools such as IBM SPSS v25 statistics were utilized to infer consumer preference. The results determined that cost is the primary concern for consumers by a considerable margin; followed by battery type and charging method; along with the type of EV, driving range, and charging speed; and most minor concern is regenerative brakes. Therefore, there is an apparent sensitivity to price and technology. This study is the first to apply conjoint analysis to the Philippine market, delivering in-depth consumer preference insights that can help manufacturers and policymakers customize their approach to making EVs more attractive and more viable in less developed markets. The results suggest that a targeted effort to overcome cost barriers and improve technological literacy among prospective buyers should be productive for speeding up EV adoption in the Philippines. The results could be extended in future research to a broader assessment of socioeconomic and environmental benefits, laying out a broader plan for promoting sustainable solutions in transportation.
]]>World Electric Vehicle Journal doi: 10.3390/wevj15030110
Authors: Vekil Sari
Wireless power transfer (WPT) systems, which have been around for decades, have recently become very popular with the widespread use of electric vehicles (EVs). In this study, an inductive coupling WPT system with a series–series compensation topology was designed and implemented for use in EVs. Initially, a 3D Maxwell (ANSYS Electromagnetics Suite 18) model of the system was generated. The impact of individual parameters on the coupling coefficient was analyzed through systematic variations in each parameter’s values. As a result, a system with a higher coupling coefficient was obtained. Using this system, three distinct load cases were investigated for their efficiency in the Simplorer (ANSYS Electromagnetics Suite 18) circuit. Subsequently, a prototype of the system was constructed, and the experimental results were compared with the model’s results. This study shows that both the output power and the efficiency of the system increase as the load resistance increases. The results obtained in this study are anticipated to offer valuable insights for the enhancement of WPT system design.
]]>World Electric Vehicle Journal doi: 10.3390/wevj15030109
Authors: Carlos Santana Luis Reyes-Osorio Jesus Orona-Hinojos Lizbeth Huerta Alfredo Rios Patricia Zambrano-Robledo
The steering knuckle is a critical component of the suspension and steering drive systems of electric vehicles. The electrification of last-mile vehicles presents a challenge in terms of cost, driving range and compensation of battery weight. This work presents a numerical methodology to evaluate 60XX series aluminum metal matrix composites (AMMCs) with reinforcement ceramic particles for steering knuckle components in medium heavy-duty last-mile cargo vehicles. The use of AMMCs provides lightweight knuckles with sufficient strength, stiffness and safety conditions for electrical vehicle cargo configurations. The numerical study includes three aluminum alloys, two AMMC alloys and an Al 6061-T6 alloy as reference materials. The medium-duty heavy vehicle class < 12 t, such as electrical vehicle cargo configurations, is considered for the numerical study (class 1–4). The maximum von Mises stress for class 4 AMMC alloys exceeds 350 MPa, limited by fracture toughness. The weight reduction is about 65% when compared with commercial cast iron. Moreover, Al 6061-T6 alloys exhibit stress values surpassing 300 MPa, constraining their suitability for heavier vehicles. The study proposes assessing the feasibility of implementing AMMC alloys in critical components like steering knuckles and suggests solutions to enhance conventional vehicle suspension systems and overcome associated challenges. It aims to serve as a lightweight design guide, offering insights into stress variations with differing load conditions across various cargo vehicles.
]]>World Electric Vehicle Journal doi: 10.3390/wevj15030108
Authors: Abdulmohsen A. Al-fouzan Radwan A. Almasri
This study aimed to uncover the attitudes, preferences, and perceptions of Hail residents toward electric vehicles (EVs) by employing a real-life survey-based approach. This paper presents an in-depth analysis of the potential adoption and impact of EVs to clarify the picture of the transition from using traditional vehicles to using EVs in Hail City, Saudi Arabia. Hail is rapidly becoming a more urbanized city; in the past few decades, the city’s area has expanded from 3242 to 17,526 hectares, and its population has increased dramatically from 82,900 in 1984 to 344,111 in present day. As a result, the city is facing increasingly difficult challenges related to rising vehicle emissions and environmental degradation. A survey was conducted among a diverse group of 346 participants. The survey results show an average of 3.15 cars per family, which indicates a strong connection with personal vehicles. The survey provides a comprehensive picture of the respondents’ socioeconomic background, indicating an average household size of 5.8 people and an average monthly income of SR 13,350. The key findings from the survey reveal that approximately 52.3% of the respondents have 3–4 family members, and nearly half of the families own one or two cars. Government employees formed a major proportion of the respondents. The results show a significant inclination toward EVs, with 78.6% of the participants being aware of EV charging stations and 37.9% expressing a positive attitude towards switching to electric vehicles. Despite this, a large majority (88.7%) have never driven an electric car. The respondents’ driving habits are further explored in the survey, which reveals an average of 2.1 h of daily driving. Furthermore, the respondents disclosed an average weekly fuel expenditure of SR 235. The results also highlight that the average weekly cost for fuel varies among the respondents, with most respondents spending no more than SR 300 per week. Additionally, this study examined the daily vehicle mileage, revealing that 37.9% of the respondents have a daily mileage of 51 to 100 km, which impacts the planning of charging station capacities and locations. The findings suggest a growing interest in EVs and highlight the need for strategic infrastructure development to support the anticipated surge in EV adoption.
]]>World Electric Vehicle Journal doi: 10.3390/wevj15030107
Authors: Qun He Xinning Li Wenjie Mao Xianhai Yang Hu Wu
The efficient development of electric vehicles is essential to drive society towards sustainable development. Designing a lightweight frame is a key strategy to improve the economy and environment, increase energy efficiency, and reduce carbon emissions. Taking an automatic loading and unloading mixer truck as the research object, a force analysis of its frame was conducted under six typical working conditions. A size optimization method based on a hybrid model of the Kriging model and the analytic hierarchy process (AHP) is proposed. An approximate model of the mass and maximum stress of the frame was established using the Kriging model, and the Kriging model was optimized by using the multi-objective genetic optimization algorithm and the AHP method. Meanwhile, topology optimization was introduced to improve the structural performance of the frame and reduce its weight. The optimization results show that the overall weight of the frame is reduced by 11.96% compared to the pre-optimization period, though it still meets the material performance specifications. By comparing the iterative curves of the single Kriging model with those of the AHP model, it can be seen that the initial optimization efficiency of the hybrid model is about twice as much as that of the AHP model, and the final optimization result is improved by about 3.6% compared with the Kriging model. This validates the hybrid model as an effective tool for the multi-objective optimization of electric vehicle frames, providing more efficient and accurate optimization results for frame design.
]]>World Electric Vehicle Journal doi: 10.3390/wevj15030106
Authors: Indra Ferdiansyah Tsuyoshi Hanamoto
Reliable motor control is important for electric vehicle applications. The control process requires accurate measurements of the current and rotor position information to establish correct motor control design, particularly in sensorless permanent magnet synchronous motor control systems. Practical issues regarding the motor control circuit, such as the effects of parasitic element behavior on the switching components in the insulated gate bipolar transistor-driven inverter, were discussed in this study. It analyzed the effects of parasitic elements that can cause the ringing of switching losses and affect the spike of the signal in the motor current, which must be avoided in the implementation of motor control. The gate driver circuit topology was improved to reduce this effect in motor control devices. The proposed gate driver circuit design with the ringing suppression circuit configuration achieved good performance by keeping the signal spike at less than 10% in the motor current. Furthermore, a signal spike or noise was not observed in the estimation results of rotor position when using current information as the parameter control process. Both conditions were verified by experiments on the designed motor control devices. Under these conditions, signal precision can be achieved in motor control.
]]>World Electric Vehicle Journal doi: 10.3390/wevj15030105
Authors: Yu-Sen Chuang Hong-Ping Cheng Chin-Chi Cheng
Over the last 50 years since Whittingham created the world’s first lithium-ion battery (LIB) in 1970, LIBs have continued to develop and have become mainstream for electric vehicle (EV) batteries. However, when an LIB for an EV reaches 80% of its state of health (SOH), although it still retains about 80% of its capacity, it is no longer suitable for use in general EVs and must be retired. This is problematic because not only is a retired LIB still viable for use and not totally obsolete, if not properly disposed of, a retired LIB may cause environmental pollution on top of being a waste of resources. Therefore, the reuse of retired LIBs from EVs is increasingly important. This paper uses circular economy (CE) and extended producer responsibility (EPR) as a theoretical basis to deal with the disposal of retired LIBs from EVs in Taiwan from legal, technical, and economic perspectives, and hopes to provide suggestions for the reuse of retired LIBs from EVs in Taiwan.
]]>World Electric Vehicle Journal doi: 10.3390/wevj15030104
Authors: Xianghai Yan Bingxin Chen Mengnan Liu Yifan Zhao Liyou Xu
Unmanned tractors under ploughing conditions suffer from body tilting, violent shaking and limited hardware resources, which can reduce the detection accuracy of unmanned tractors for field obstacles. We optimize the YOLOv8 model in three aspects: improving the accuracy of detecting tilted obstacles, computational reduction, and adding a visual ranging mechanism. By introducing Funnel ReLU, a self-constructed inclined obstacle dataset, and embedding an SE attention mechanism, these three methods improve detection accuracy. By using MobileNetv2 and Bi FPN, computational reduction, and adding camera ranging instead of LIDAR ranging, the hardware cost is reduced. After completing the model improvement, comparative tests and real-vehicle validation are carried out, and the validation results show that the average detection accuracy of the improved model reaches 98.84% of the mAP value, which is 2.34% higher than that of the original model. The computation amount of the same image is reduced from 2.35 billion floating-point computations to 1.28 billion, which is 45.53% less than the model computation amount. The monitoring frame rate during the movement of the test vehicle reaches 67 FPS, and the model meets the performance requirements of unmanned tractors under normal operating conditions.
]]>World Electric Vehicle Journal doi: 10.3390/wevj15030103
Authors: Muhammed Alhanouti Frank Gauterin
An identification technique is proposed to create a relation between the accelerator pedal position and the corresponding driving moment. This step is beneficial to replace the complex physical model of the vehicle control unit, especially when the sufficient information needed to model certain functionalities of the vehicle control unit are unavailable. We utilized the nonlinear autoregressive exogenous model to regenerate the electric motor torque demand, given the accelerator pedal position, the motor’s angular speed, and the vehicle’s speed. This model proved to be extremely efficient in representing this highly complex relationship. The data employed for the identification process were chosen from an actual three-dimensional route with sudden changes of a dynamic nature in the driving mode, different speed limits, and elevations, as an attempt to thoroughly cover the driving moment scope based on the alternation of the given inputs. Analyzing the selected route data points showed the widespread coverage of the motor’s operational scope compared to a standard driving cycle. The training outcome revealed that linear modeling is inadequate for identifying the targeted system, and has a substantial estimation error. Adding the nonlinearity feature to the model led to an exceptionally high accuracy for the estimation and validation datasets. The main finding of this work is that the combined model from the nonlinear autoregressive exogenous and the sigmoid network enables the accurate modeling of highly nonlinear dynamic systems. Accordingly, the maximum absolute estimation error for the motor’s moment was less than 10 Nm during the real-world driving maneuver. The highest errors are found around the maximum motor’s moment. Finally, the model is validated with measurements from an actual field test maneuver. The identified model predicted the driving moment with a correlation of 0.994.
]]>World Electric Vehicle Journal doi: 10.3390/wevj15030102
Authors: Jiahao Qin Xiaofeng Yang Tianyi Zhang Shuilan Bi
Pavement defect detection technology stands as a pivotal component within intelligent driving systems, demanding heightened precision and rapid detection rates. Addressing the complexities arising from diverse defect types and intricate backgrounds in visual sensing, this study introduces an enhanced approach to augment the network structure and activation function within the foundational YOLOv5 algorithm. Initially, modifications to the YOLOv5′s architecture incorporate an adjustment to the Leaky ReLU activation function, thereby enhancing regression stability and accuracy. Subsequently, the integration of bi-level routing attention into the network’s head layer optimizes the attention mechanism, notably improving overall efficiency. Additionally, the replacement of the YOLOv5 backbone layer’s C3 module with the C3-TST module enhances initial convergence efficiency in target detection. Comparative analysis against the original YOLOv5s network reveals a 2% enhancement in map50 and a 1.8% improvement in F1, signifying an overall advancement in network performance. The initial convergence rate of the algorithm has been improved, and the accuracy and operational efficiency have also been greatly improved, especially on models with small-scale training sets.
]]>World Electric Vehicle Journal doi: 10.3390/wevj15030101
Authors: Ming Yao Danning Da Xinchun Lu Yuhang Wang
Electric vehicles (EVs) play a major role in the energy system because they are clean and environmentally friendly and can use excess electricity from renewable sources. In order to meet the growing charging demand for EVs and overcome its negative impact on the power grid, new EV charging stations integrating photovoltaic (PV) and energy storage systems (ESSs) have emerged. However, the output of solar PV systems and the charging demand of EVs are both characterized by uncertainty and dynamics. These may lead to large power fluctuations in the grid and frequent alternation of peak and valley loads, which are not conducive to the stability of the distribution network. The study of reasonable capacity configuration and control strategy issues is conducive to the efficient use of solar energy, fast charging of EVs, stability of the distribution network, and maximization of the economic benefits of the system. In this paper, the concept, advantages, capacity allocation methods and algorithms, and control strategies of the integrated EV charging station with PV and ESSs are reviewed. On the basis of the above research, the current problems and challenges are analyzed, and corresponding solutions and ideas are proposed.
]]>World Electric Vehicle Journal doi: 10.3390/wevj15030100
Authors: Ettore Bianco Claudio Giannuzzi Andrés Felipe Corredor Pablos Vicente Alfredo Peña Reyes Davide Berti Polato
The increasing demand for efficient and cost-effective transportation solutions has led to the exploration of unconventional modes of transportation, such as ziplines. This paper presents the development of an electric prototype for a trolley that can be used for the simultaneous transport of goods and people on ziplines. The prototype is designed with a modular system that allows for easy customization based on the cargo’s weight and size. Two lightweight Maxon motors have been integrated for traction purposes with two Maxon inverters and a low-voltage swappable battery pack. The trolley’s chassis is made of lightweight materials, such as aluminum, making it highly maneuverable and capable of traveling at high speeds. The lightweight permits the operators to detach the trolley from the zipline when needed. The prototype’s traction and braking systems are controlled through a user-friendly interface, making it easy to operate, and with the possibility of a robust and automatic routine for goods transport. In this article, we present the simulation for the design and testing of the prototype, as well as its potential applications in various industries, such as mining, agriculture, and emergency services. Our results show that the prototype is a viable solution for zipline-based transportation, with high efficiency and performance standards. Further research and development are being conducted to optimize the prototype’s performance and expand its applications.
]]>World Electric Vehicle Journal doi: 10.3390/wevj15030099
Authors: Shanzhi Chen Xinghua Hu Jiahao Zhao Ran Wang Min Qiao
Decision-making and planning are the core aspects of autonomous driving systems. These factors are crucial for improving the safety, driving experience, and travel efficiency of autonomous vehicles. Intersections are crucial nodes in urban road traffic networks. The objective of this study is to comprehensively review the latest issues and research progress in decision-making and planning for autonomous vehicles in intersection environments. This paper reviews the research progress in the behavioral prediction of traffic participants in terms of machine learning-based behavioral prediction, probabilistic model behavioral prediction, and mixed-method behavioral prediction. Then, behavioral decision-making is divided into reactive decision-making, learning decision-making, and interactive decision-making, each of which is analyzed. Finally, a comparative analysis of motion planning and its applications is performed from a methodological viewpoint, including search, sampling, and numerical methods. First, key issues and major research progress related to end-to-end decision-making and path planning are summarized and analyzed. Second, the impact of decision-making and path planning on the intelligence level of autonomous vehicles in intersecting environments is discussed. Finally, future development trends and technical challenges are outlined.
]]>World Electric Vehicle Journal doi: 10.3390/wevj15030098
Authors: Kirstin Ganz Timo Kern Michael Hinterstocker
The shift to electric transportation is crucial to fighting climate change. However, Germany’s goal of 15 million electric vehicles (EVs) by 2030 remains distant. Therefore, enhancing their economic viability is essential to promoting EV adoption. One promising option to increase the economics for the user is PV self-consumption optimization using smart charging EVs. Yet, more research is needed to explore the use case’s impacts on the German/European energy systems. Therefore, PV self-consumption optimization using EVs is integrated into an energy system model, assessing its impact on the energy system in 2030. For this purpose, the use case is modeled for different groups of people—personas—which are defined in a way that creates a diverse set of personas reflecting the distribution of different statistical values within Germany. The modified (dis)charging profiles are then aggregated and integrated into the energy system model. With a high implementation of PV self-consumption optimization in Germany in 2030, a positive system effect (with a system cost reduction of 53 million EUR/a) can be observed with a lower need for further storage and less curtailment of renewable energies (RES). Furthermore, the market values for RES increase by 0.7%, which fosters the integration of RES.
]]>World Electric Vehicle Journal doi: 10.3390/wevj15030097
Authors: Elias Dörre Timotheus Klein Michael von Bonin
The rise of electric mobility poses a challenge and an opportunity for the housing industry to provide charging infrastructure. The housing industry can take advantage of its large roof areas to install photovoltaic (PV) systems and use the electricity generated to charge electric vehicles. This study explores how the charging demand can be allocated to specific locations based on socio-economic parameters and determines whether PV integration is economically viable for EV charging. Two models are used, one with extensive spatial and traffic data to determine the charging demand for over 300 locations, and another with a time-series-based approach for four specific locations to analyze the seasonal dependencies. The results indicate that PV integration is economically advantageous when electricity purchase prices exceed 0.15 EUR/kWh. Higher electricity prices can lead to significant additional profits through PV integration. Slow charging and charging during the day are beneficial, as they increase self-consumption, making PV systems economically viable. However, fast-charging infrastructure should be combined with PV storage systems for effective PV integration.
]]>World Electric Vehicle Journal doi: 10.3390/wevj15030096
Authors: Zhenyu Song Yubin Qian
Precise trajectory prediction is pivotal for autonomous vehicles operating in real-world traffic conditions, and can help them make the right decisions to ensure safety on the road. However, state-of-the-art approaches consider limited information about the historical movements of vehicles. On highways, drivers make their next judgments according to the behavior of the ambient vehicles. Thus, vehicles need to consider temporal and spatial interactions to reduce the risk of future collisions. In the current work, a trajectory prediction method is put forward in accordance with a graph attention mechanism. We add the absolute and relative motion information of vehicles to the input of the model to describe the vehicles’ past motion states more accurately. LSTM models are employed to process the historical motion information of vehicles, as well as the temporal correlations in interactions. The graph attention mechanism is applied to capture the spatial correlations between vehicles. Utilizing a decoder rooted in an LSTM framework, the future trajectory distribution is generated. Evaluation on the NGSIM US-101 and I-80 datasets substantiates the superiority of our approach over existing state-of-the-art algorithms. Moreover, the predictions of our model are analyzed.
]]>World Electric Vehicle Journal doi: 10.3390/wevj15030095
Authors: Shaobo Yang Yubin Qian Wenhao Hu Jiejie Xu Hongtao Sun
For continuously changing road conditions and vehicle operating states, the exactitude of vehicle path tracking has not been secured by model predictive control based on linear lateral stiffness. An amended square root cubature Kalman filter method based on the minimization of a new covariance of interest is proposed to calculate the tire lateral deflection force in real time. The ratio of the estimated tire force to the linear tire force was used as a ratio to adjust the lateral deflection stiffness, and an adaptive model predictive controller was built based on the vehicle path-tracking error model to correct the tire lateral deflection stiffness. Finally, an analysis based on the joint CarSim and Simulink simulation platform shows that compared to a conventional model predictive control (MPC) controller, a trajectory-following controller built based on this method can effectively reduce the lateral distance error and heading error of an autonomous vehicle. Especially under low adhesion conditions, the conventional MPC controllers will demonstrate large instability during trajectory tracking due to the deviation of the linear tire force calculation results, whereas the adaptive model predictive control (AMPC) controllers can correct the side deflection stiffness by estimating the tire force and still achieve stable and effective tracking of the target trajectory. This suggests that the proposed algorithm can improve the effectiveness of trajectory tracking control for autonomous vehicles, which is an important reference value for the optimization of autonomous vehicle control systems.
]]>World Electric Vehicle Journal doi: 10.3390/wevj15030094
Authors: Jianwei Ma
Extended-range electric vehicles have both a motor and an engine; the motor is used for driving, and the engine generates electricity via a range extender, which is connected to the motor. The permanent magnet generator is part of the range extender, and the output voltage is controlled by adjusting the engine’s speed; the generator’s rotating speed fluctuates, meaning that the engine’s fuel consumption increases. Meanwhile, considering the limited axial dimension of the range extender, an axial–radial disk hybrid generator that combines excitation is developed, making full use of the radial space; at the same time, the output voltage is adjusted without changing the engine’s speed. In this study, the generator’s magnetic field hybrid principle, the path of permanent magnetic circuit, and the electric excitation magnetic circuit under different loads were analyzed and verified via the finite element method. A comparative analysis method was also used, the technical index of the disk hybrid excitation generator was determined, and the main structural parameters were designed using theoretical calculations. The three-dimensional finite element model was established based on the results, and a finite element analysis was performed. An equivalent magnetic circuit model was established, and the formulas of synthetic permeability, leakage permeability, and effective permeability were determined. The finite element method (numerical method) and equivalent magnetic circuit method (analytical method) were used to calculate the synthetic magnetic fields of the air gap, rotor yoke, and rotor teeth under different excitation currents. A comparison between the two methods verified the design utility. The conclusions provide a valuable point of reference for the development of the disk hybrid excitation generator for use in range extenders in extended-range electric vehicles.
]]>World Electric Vehicle Journal doi: 10.3390/wevj15030093
Authors: Vennapusa Jagadeeswara Reddy N. P. Hariram Rittick Maity Mohd Fairusham Ghazali Sudhakar Kumarasamy
Climate change necessitates urgent action to decarbonize the transport sector. Sustainable vehicles represent crucial alternatives to traditional combustion engines. This study comprehensively compares four prominent sustainable vehicle technologies: biofuel-powered vehicles (BPVs), fuel cell vehicles (FCVs), electric vehicles (EVs), and solar vehicles. We examine each technology’s history, development, classification, key components, and operational principles. Furthermore, we assess their sustainability through technical factors, environmental impacts, cost considerations, and policy dimensions. Moreover, the discussion section addresses the challenges and opportunities associated with each technology and assesses their social impact, including public perception and adoption. Each technology offers promise for sustainable transportation but faces unique challenges. Policymakers, industry stakeholders, and researchers must collaborate to address these challenges and accelerate the transition toward a decarbonized transport future. Potential future research areas are identified to guide advancements in sustainable vehicle technologies.
]]>World Electric Vehicle Journal doi: 10.3390/wevj15030092
Authors: Juan Du Xiaozhang Zhao Xiaodong Liu Gang Liu Yanfeng Xiong
The present study proposes a fuzzy logical control-based real-time energy management strategy (EMS) for a fuel cell electrical bus (FCEB), taking into account the durability of the fuel cell system (FCS), in order to enhance both the vehicle’s economic performance and the FCS’s service life. At first, the model of the FCEB is established whilst the power-following strategy is also formulated as a benchmark for the evaluation of the proposed strategy. Subsequently, a fuzzy logical controller is designed to improve the work efficiency of the FCS, in which the battery state-of-charge (SOC) and the vehicle’s desired power are considered the inputs, whilst the power of the FCS is the output. Then, a limitation method is integrated into the fuzzy logical controller to restrict the change rate of the FCS’s power to strengthen the FCS’s service life. At last, the evaluation is accessed based on the China city bus driving cycle (CCBC). The results indicate that the proposed fuzzy logical strategy can satisfy the dynamic performance of the FCEB well. Importantly, it also has a remarkable effectiveness in terms of promoting the FCEB’s economy. Despite a slight reduction in contrast to the fuzzy logical control, the improvements of the strategy in which the FCS’s durability is considered are still acceptable. The change rate of the FCS’s power can be confined to ±10 kW. Meanwhile, the promotion of economic performance can reach up to 8.43%, 7.69%, and 6.53% in the proposed durability consideration strategy in contrast to the power-following strategy under different battery SOCs. This will significantly benefit both the energy saving and the FCS’s durability.
]]>World Electric Vehicle Journal doi: 10.3390/wevj15030091
Authors: Valerio Martini Francesco Mocera Aurelio Somà
The growing awareness about climate change and environmental pollution is pushing the industrial and academic world to investigate more sustainable solutions to reduce the impact of anthropic activities. As a consequence, a process of electrification is involving all kind of vehicles with a view to gradually substitute traditional powertrains that emit several pollutants in the exhaust due to the combustion process. In this context, fuel cell powertrains are a more promising strategy, with respect to battery electric alternatives where productivity and endurance are crucial. It is important to replace internal combustion engines in those vehicles, such as the those in the sector of Non-Road Mobile Machinery. In the present paper, a preliminary analysis of a fuel cell powertrain for a telehandler is proposed. The analysis focused on performance, fuel economy, durability, applicability and environmental impact of the vehicle. Numerical models were built in MATLAB/Simulink and a simple power follower strategy was developed with the aim of reducing components degradation and to guarantee a charge sustaining operation. Simulations were carried out regarding both peak power conditions and a typical real work scenario. The simulations’ results showed that the fuel cell powertrain was able to achieve almost the same performances without excessive stress on its components. Indeed, a degradation analysis was conducted, showing that the fuel cell system can achieve satisfactory durability. Moreover, a Well-to-Wheel approach was adopted to evaluate the benefits, in terms of greenhouse gases, of adopting the fuel cell system. The results of the analysis demonstrated that, even if considering grey hydrogen to feed the fuel cell system, the proposed powertrain can reduce the equivalent CO2 emissions of 69%. This reduction can be further enhanced using hydrogen from cleaner production processes. The proposed preliminary analysis demonstrated that fuel cell powertrains can be a feasible solution to substitute traditional systems on off-road vehicles, even if a higher investment cost might be required.
]]>World Electric Vehicle Journal doi: 10.3390/wevj15030090
Authors: Bo Zhu Chengwu Bao Mingyao Yao Zhengchun Qi
Electric vehicles can effectively make use of the time-of-use electricity price to reduce the charging cost. Additionally, using grid power to preheat the battery before departure is particularly important for improving the vehicle mileage and reducing the use cost. In this paper, a dynamic programming algorithm is used to optimize the battery AC (Alternating Current) charging–preheating strategy to minimize the total cost of battery charging and preheating, with the charging current and battery preheating power consumption as the control variables. The cost difference between the optimized control strategy and the conventional preheating strategy was analyzed under different ambient temperatures (−20~0 °C) and different target travel times (7:00~12:00). The simulation results show that the optimized control strategy makes the state of charge (SOC) and temperature of the battery reach the set value at the user’s target departure time, and the total cost of the grid is the lowest. Compared with the conventional preheating strategy, the optimized control strategy can utilize the power grid energy in the valley price area and reduce the opening time of the positive temperature coefficient (PTC) heater in the flat and the peak price zones. Furthermore, the cost utilization rate can reach 18.41~73.96%, and the cost-saving effect is significant.
]]>World Electric Vehicle Journal doi: 10.3390/wevj15030089
Authors: Zhaoxue Deng Yangrui Zhang Shuen Zhao
To enhance the path tracking capability and driving stability of intelligent vehicles, a controller is designed that synergizes active front wheel steering (AFS) and direct yaw moment (DYC), specifically tailored for distributed-drive electric vehicles. To address the challenge of determining the weight matrix in the linear quadratic regulator (LQR) algorithm during the path tracking design for intelligent vehicles on conventional roads, a genetic algorithm (GA)-optimized LQR path tracking controller is introduced. The 2-degree-of-freedom vehicle dynamics error model and the desired path information are established. The genetic algorithm optimization strategy, utilizing the vehicle’s lateral error, heading error, and output front wheel steering angle as the objective functions, is employed to optimally determine the weight matrices Q and R. Subsequently, the optimal front wheel steering angle control (AFS) output of the vehicle is calculated. Under extreme operating conditions, to enhance vehicle dynamics stability, while ensuring effective path tracking, the active yaw moment is crafted using the sliding mode control with a hyperbolic tangent convergence law function. The control weights of the sliding mode surface related to the center-of-mass lateral declination are adjusted based on the theory of the center-of-mass lateral declination phase diagram, and the vehicle’s target yaw moment is calculated. Validation is conducted through Matlab/Simulink and Carsim co-simulation. The results demonstrate that the genetic algorithm-optimized LQR path tracking controller enhances vehicle tracking accuracy and exhibits improved robustness under conventional road conditions. In extreme working conditions, the designed path tracking and stability cooperative controller (AFS+DYC) is implemented to enhance the vehicle’s path tracking effect, while ensuring its driving stability.
]]>World Electric Vehicle Journal doi: 10.3390/wevj15030088
Authors: Mingfei Li Fabian Kai-Dietrich Noering Yekta Öngün Michael Appelt Roman Henze
The digitalization of the automotive industry presents significant potential for technical advantages, such as the online collection of customer driving data. These data can be used for customer-oriented development to improve the durability of components or systems. However, due to current limitations in data transfer, the sampling frequency is typically lower than that of classic dataloggers. This paper examines the importance of low-frequency data in the development of drivetrain durability and investigates the extent to which these data can be utilized for a drivetrain durability analysis. Real driving data were utilized as a database to demonstrate the impact of downsampling on data significance, with the deviation in damage serving as the criteria. The findings suggest that low-frequency data, when available in sufficient quantities, can provide valuable information for predicting durability in rollover and time at level classification. The deviation in the damage prediction is less than 2% for distances exceeding 5000 km. However, low-frequency data are not suitable for rainflow analysis. Finally, the database size was adjusted to assess the statistical stability of the durability prediction. A larger dataset typically reduces variance. The paper presents evidence for the quality and usability of cloud data in drivetrain durability design. Cloud data from a significant number of customer vehicles can be used for certain analyses of representative customer load collectives, which can reduce development time and costs.
]]>World Electric Vehicle Journal doi: 10.3390/wevj15030087
Authors: Mingchen Luan Yun Zhang Xiaowei Li Fenghui Xu
The purpose of this paper is to study the sensor-less rotor position estimation method for permanent magnet synchronous motors, and to achieve accurate estimation of rotor position in different conditions. Firstly, the traditional super-twisting observer algorithm is analyzed, and a new discrete variable gain sliding mode observer is designed to solve the buffeting problem in discrete systems, taking the reaction force as the disturbance signal. By estimating the back potential of the observer, the buffeting problem in the sliding mode algorithm can be effectively improved as shown by the simulation results. Then, to solve the problem of phase delay in rotor position estimation, an adaptive orthogonal phase-locked loop method is used to compensate the estimation error caused by the change in motor speed and increase the estimation accuracy of rotor position. The stability of the method can be proven by Lyapunov’s second method. Simulation experiments verify the accuracy of the proposed PMSM rotor position estimation method.
]]>World Electric Vehicle Journal doi: 10.3390/wevj15030086
Authors: Antti Lajunen Klaus Kivekäs Vincent Freyermuth Ram Vijayagopal Namdoo Kim
The objectives of this research were to develop simulation models for agricultural tractors with different powertrain technologies and evaluate the energy consumption in typical agricultural operations. Simulation models were developed for conventional, parallel hybrid electric, series hybrid electric, fuel cell hybrid, and battery electric powertrains. Autonomie vehicle simulation software (version 2022) was used for the simulations and the tractor models were simulated in two tilling cycles and in a road transport cycle with a trailer. The alternative powertrains were configured to have at least the same tractive performance as the conventional, diesel engine-powered tractor model. The simulation results showed that the potential of the parallel and series hybrid powertrains to improve energy efficiency depends heavily on the tractor size and the operating cycle conditions. The fuel cell hybrid and battery electric powertrains have a higher potential to reduce energy consumption and emissions but still have inherent technical challenges for practical operation. The battery-powered electric tractor would require improvements in the storage energy density to have a comparable operational performance in comparison to other powertrains. The fuel cell hybrid tractor already provided an adequate operating performance but the availability of hydrogen and refueling infrastructure could be challenging to resolve in the farming context.
]]>World Electric Vehicle Journal doi: 10.3390/wevj15030085
Authors: Kaiyun Yang Yunqi Cheng Zonghai Chen Jikai Wang
In recent years, Simultaneous Localization and Mapping (SLAM) systems have shown significant performance, accuracy, and efficiency gains, especially when Neural Radiance Fields (NeRFs) are implemented. NeRF-based SLAM in mapping aims to implicitly understand irregular environmental information using large-scale parameters of deep learning networks in a data-driven manner so that specific environmental information can be predicted from a given perspective. NeRF-based SLAM in tracking jointly optimizes camera pose and implicit scene network parameters through inverse rendering or combines VO and NeRF mapping to achieve real-time positioning and mapping. This paper firstly analyzes the current situation of NeRF and SLAM systems and then introduces the state-of-the-art in NeRF-based SLAM. In addition, datasets and system evaluation methods used by NeRF-based SLAM are introduced. In the end, current issues and future work are analyzed. Based on an investigation of 30 related research articles, this paper provides in-depth insight into the innovation of SLAM and NeRF methods and provides a useful reference for future research.
]]>World Electric Vehicle Journal doi: 10.3390/wevj15030084
Authors: Florian Biedenbach Kai Strunz
Battery-electric trucks offer a high battery capacity and good predictability, making them attractive for the implementation of bidirectional charging strategies. Nevertheless, most of the previous charging strategy studies focus on electric passenger cars. These charging strategies are usually formulated as separate use cases like tariff-optimized charging, arbitrage trading, peak shaving, and self-consumption optimization. By combining different use cases, their economic potential can be increased. In this paper, we introduce a model to optimize charging processes in depots for electric vehicles considering the combination of different use cases. This model is applied to a depot for battery-electric trucks. The savings obtained through optimized bidirectional charging highlight the enormous potential of this technology for the future, especially in the heavy-duty sector.
]]>World Electric Vehicle Journal doi: 10.3390/wevj15030083
Authors: Shiwei Xu Xiaopeng Zhang Yuan Jiao Lulu Wei Jingjing He Xinyu Zeng
Electric wheel-drive multi-axle heavy-duty vehicles have the characteristics of strong maneuverability and good passability, thereby they are widely used in heavy equipment transportation. However, current research on the composite braking of multi-axle heavy-duty vehicles is rare, which is not conducive to improving braking performance and braking energy utilization efficiency. This work proposes a multi-mode composite braking control strategy for the five-axle distributed electric wheel-drive heavy-duty vehicle. Firstly, given the differences in braking dynamics between two-axle vehicles and multi-axle vehicles, the brake dynamics characteristics of multi-axle vehicles are analyzed, and the vehicle dynamics model of multi-axle vehicles is constructed. Next, a multi-mode composite braking control strategy including a fully electric braking state and hybrid electro–hydraulic braking state is proposed in order to improve the braking energy recovery and braking stability. Finally, a hardware-in-the-loop simulation system is established, and the single-braking conditions and China heavy-duty commercial vehicle test cycle-heavy truck (abbreviated as CHTC-HT) are conducted to verify the performance of the braking control strategy. The results indicate that the recaptured braking energy and braking stability are significantly increased by applying the control strategy proposed in this work.
]]>World Electric Vehicle Journal doi: 10.3390/wevj15030082
Authors: Xu Han Minghui Ma Shidong Liang Jufen Yang Chaoteng Wu
Under the development of intelligent network technology, drivers can obtain the surrounding traffic situation in real time, which is conducive to improving the stability of traffic flow. Therefore, this paper proposes a new curve-car-following model considering multi-vehicle information of adjacent lanes in connected environment, and conducts linear and nonlinear stability analyses of the model to demonstrate the effectiveness of the proposed model and its ability to improve the stability of traffic system; in addition, numerical simulation experiments of traffic flow convoys are designed to analyze the effects of different parameters in the proposed model on the stability of the traffic flow and test the proposed model’s ability to maintain the following behavior in a convoy. Furthermore, numerical simulation experiments are designed to analyze the effects of different parameters in the proposed model on the stability of traffic flow, and to test the ability of the proposed model to maintain the following behavior in the convoy. The model can provide theoretical guidance to alleviate traffic congestion and improve safety, and extend the application of the following model in curved multi-lane road scenarios.
]]>World Electric Vehicle Journal doi: 10.3390/wevj15030081
Authors: Paulo J. G. Ribeiro Gabriel Dias José F. G. Mendes
In 2020, only 0.9% of buses running in European Union countries were electric, with 93.5% still being diesel-powered. The Sustainable and Smart Mobility Strategy set out by the European Commission targets a reduction of at least 55% in greenhouse gas emissions by 2023 and the achievement of climate neutrality by 2050. These targets will only be met by a shift to sustainable mobility, which comprises the introduction of electric vehicles in cities and the adoption of battery electric vehicles (BEV) for urban public transport. Thus, a literature review on “electrification of bus fleets” was conducted, focusing on the practices adopted for the replacement of polluting buses with electric-powered ones. A total of 62 documents were included in the final investigation, and their results were used to conduct a SWOT analysis. It is possible to conclude that BEBs are an important asset for cities to decarbonize the transport sector and that they are more cost-effective than diesel buses. On the other hand, some attention needs to be given to the generation of energy that will feed the charging of batteries because the use of fossil fuel energy sources can jeopardize the environmental benefits of BEBs.
]]>World Electric Vehicle Journal doi: 10.3390/wevj15030080
Authors: Meiling He Mei Yang Wenqing Fu Xiaohui Wu Kazuhiro Izui
Inspired by the practice of urban distribution of fresh products, we introduce a new electric vehicle routing problem with soft time windows. In this problem, goods with different temperature layers can be distributed in ordinary electric vehicles simultaneously based on the cold storage insulation box. The primary objective is to devise optimized distribution routes for logistics companies to minimize distribution costs, including transportation, refrigeration, and charging costs. To address this, we present a mathematical model for the problem and propose an improved ant colony optimization algorithm combined with a 2-opt algorithm. Based on Solomon dataset, we conduct numerical experiments to verify the effectiveness of the proposed model and algorithm. The numerical results demonstrate that multi-temperature co-distribution can lead to a reduction in distribution cost and an improvement in distribution efficiency.
]]>World Electric Vehicle Journal doi: 10.3390/wevj15030079
Authors: Daniel Stetter Tobias Höpfer Marc Schmid Ines Sturz Simon Falkenberger Nadja Knoll
The core goal of the BANULA research project is to combine customer-oriented and grid-compatible charging of electric vehicles. It addresses the current challenges of the e-mobility ecosystem from the perspective of grid operators and charging infrastructure users and creates added value for every mass market role involved. In the project, the idea of a virtual balancing group based on blockchain technology is implemented. Thereby, it enables extended data acquisition, a real-time data exchange between grid and market participants, proper balancing and grid node-specific load flow determination and, thus, load management.
]]>World Electric Vehicle Journal doi: 10.3390/wevj15030078
Authors: Muhammad Hasan Albana Harus Laksana Guntur Ary Bachtiar Khrisna Putra
This research proposes a novel cooling system to minimize the external rotor type of electric motor temperature by installing fan blades (wafters) on the inner housing of the electric motor. Fan blades (wafters) are made by printing using 3D printer technology and using polylactic acid (PLA) as the material. Wafters are then installed on an in-wheel motor with a power of 1500 W, having 48 poles and 52 slots. The study included thermal simulation and experimental techniques to ascertain how fan blades (wafters) affected the electric motor’s thermal properties. The motor rotated at 500 rpm during the experimental test with no load condition. The temperature of the electric motor is known using an infrared thermal imager. Using Ansys Motor-CAD 15.1 software, thermal modeling employs the lumped circuit model approach. Thermal simulation results show almost the same results as the experimental test results. Applying wafters on the in-wheel motor housing significantly reduces the winding temperature by 3.047 °C or experiences a temperature reduction of 4.34%. Using wafters in the in-wheel motor housing also speeds up the stable state temperature of the electric motor by 9 min compared to in-wheel motors without wafters.
]]>World Electric Vehicle Journal doi: 10.3390/wevj15030077
Authors: Ruixue Zong Ying Wang Juan Ding Weiwen Deng
The development of autonomous driving technology has made simulation testing one of the most important tools for evaluating system performance. However, there is a lack of systematic methods for analyzing and assessing naturalistic driving trajectory datasets. Specifically, there is a lack of comprehensive analyses on data diversity and balance in machine learning-oriented research. This study presents a comprehensive assessment of existing highway scenario datasets in the context of traffic modeling in autonomous driving simulation tests. In order to clarify the level of traffic risk, we design a systematic risk index and propose an index describing the degree of data scatter based on the principle of Euclidean distance quantization. By comparing several datasets, including NGSIM, highD, INTERACTION, CitySim, and our self-collected Highway dataset, we find that the proposed metrics can effectively quantify the risk level of the dataset while helping to gain insight into the diversity and balance differences of the dataset.
]]>World Electric Vehicle Journal doi: 10.3390/wevj15030076
Authors: Juliana Lopes José Antenor Pomilio Paulo Augusto Valente Ferreira
The combined use of batteries and supercapacitors is an alternative to reconcile the higher energy density of batteries with the high power density of supercapacitors. The optimal sizing of this assembly, especially with the minimization of mass, is one of the challenges of designing the power system of an electric vehicle. The condition of the unpredictability of the power demand determined by the vehicle driver must also be added, which must be met by the power system without exceeding safe operating limits for the devices. This article presents a methodology for minimizing the mass of the electrical energy storage system (ESS) that considers the various aspects mentioned and a variety of battery technologies and supercapacitor values. The resulting minimum mass dimensioning is verified by simulation for different driving cycles under conditions of maximum power demand. The system also includes a tertiary source, such as a fuel cell, responsible for the vehicle’s extended autonomy. In addition to sizing the ESS, the article also proposes a management strategy for the various sources to guarantee the vehicle’s expected performance while respecting each device’s operational limits.
]]>World Electric Vehicle Journal doi: 10.3390/wevj15030075
Authors: Yuqi Dong Kexin Chen Guiling Zhang Ran Li
Conducting online estimation studies of the SOH of lithium-ion batteries is indispensable for extending the cycle life of energy storage batteries. Data-driven methods are efficient, accurate, and do not depend on accurate battery models, which is an important direction for battery state estimation research. However, the relationships between variables in lithium-ion battery datasets are mostly nonlinear, and a single data-driven algorithm is susceptible to a weak generalization ability affected by the dataset itself. Meanwhile, most of the related studies on battery health estimation are offline estimation, and the inability for online estimation is also a problem to be solved. In this study, an integrated learning method based on a stacking algorithm is proposed. In this study, the end voltage and discharge temperature were selected as the characteristics based on the sample data of NASA batteries, and the B0005 battery was used as the training set. After training on the dataset and parameter optimization using a Bayesian algorithm, the trained model was used to predict the SOH of B0007 and B0018 models. After comparative analysis, it was found that the prediction results obtained based on the proposed model not only have high accuracy and a short running time, but also have a strong generalization ability, which has a great potential to achieve online estimation.
]]>World Electric Vehicle Journal doi: 10.3390/wevj15020074
Authors: Jiaming Zheng Yi Du Dachuan Chen Wucheng Ying Hui Zhao Kefu Liu Jian Qiu
In power converters with high switching frequency, drive losses constitute a significant portion of the overall power losses. Resonant gate drivers can reduce drive losses, thereby enhancing the efficiency. However, resonant drivers suffer certain challenges: parameter drifts lead to the mismatch between the resonant frequency and the control frequency, and this mismatch can cause gate-to-source voltage overshoot. Moreover, the resonant driver is susceptible to external interference. This paper proposes a resonant circuit structure and control timing scheme aimed at overcoming these limitations. By incorporating a half-bridge clamp circuit, the proposed design achieves voltage clamping, thereby insulating the system from disturbances caused by mains power fluctuations. When there is a mismatch in resonant frequencies, the strategy employs a combination of hardware circuit diodes and control system timing to prevent overvoltage issues. Additionally, the utilization of MOSFETs minimizes the loss caused by prolonged current flow through body diodes, further reducing the resonant driving losses. Simulations have demonstrated the system’s stability under varying resonant parameters and its effective anti-interference capabilities in voltage clamping. Experiments achieved a power saving of 83.3% at a 1 MHz operating frequency. Both simulations and experimental validations confirm the feasibility of the proposed solution, its effectiveness in interference suppression, handling of resonant mismatches, and its role in further augmenting power conservation.
]]>World Electric Vehicle Journal doi: 10.3390/wevj15020073
Authors: Jingyue Wang Yanchang Lv Xiaomeng Shan Haotian Wang Junnian Wang
In order to study the group cooperative control method of multiple intelligent networked vehicles, the multiple intelligent networked vehicles can move in the form of a fleet. Based on the leader–follower method, the paper optimizes the control effect of the leader–follower method by solving the error transmission phenomenon in the leader–follower method. In this paper, the modeling of the multiple intelligent connected vehicle adopts the vehicle dynamics model and the Magic Formula/Swift Magic tire model, and adopts the model predictive control (MPC) dynamics trajectory tracking controller for control. Through the CarSim–Simulink multi-vehicle dynamics co-simulation platform established in this paper, the group cooperative control experiments of multiple intelligent networked vehicles under different working conditions were carried out for simulation verification. The analysis results show that the maximum average error of the proposed method decreases from 8.802 to 0.094 in the case of straight line and 0.669 to 0.379 in the case of curve tracking, which proves that the method can effectively reduce the transmission of errors.
]]>World Electric Vehicle Journal doi: 10.3390/wevj15020072
Authors: Wenming Dai Yong Xiang Wenyi Zhou Qiao Peng
Solid-state batteries are currently developing into one of the most promising battery types for both the electrification of transport and for energy storage applications due to their high energy density and safe operating behaviour. The performance of solid-state batteries is largely determined by the manufacturing process, particularly in the production of electrodes. However, efficiently analysing the effects of key manufacturing features and predicting the mass loading of electrodes in the early stages of battery manufacturing remain a major challenge. In this study, a machine-learning-based approach is proposed to effectively analyse the importance of manufacturing features and accurately predict the mass loading of electrodes. Specifically, the importance of four key features during the manufacturing process of solid-state batteries is first quantified and analysed using a machine-learning-based method to analyse the importance of features. Then, four effective machine-learning-based regression methods, including decision tree, boosted decision tree, support vector regression and Gaussian process regression, are used to predict the mass loading of the electrodes in the mixing and coating stages. The comparative results show that the developed machine-learning-based approach is able to provide a satisfactory prediction of the electrode mass loading of a solid-state battery with 0.995 R2 while successfully quantifying the importance of four key features in the early manufacturing stages. Due to the advantages of its data-driven nature, the developed machine-learning-based approach can efficiently assist engineers in monitoring/predicting the electrode mass loading of solid-state batteries and analysing/quantifying the importance of manufacturing features of interest. This could benefit the production of solid-state batteries for further energy storage applications.
]]>World Electric Vehicle Journal doi: 10.3390/wevj15020071
Authors: Kenny Sau Kang Chu Kuew Wai Chew Yoong Choon Chang Stella Morris
Three-phase motors find extensive applications in various industries. Open-circuit faults are a common occurrence in inverters, and the open-circuit fault diagnosis system plays a crucial role in identifying and addressing these faults to enhance the safety of motor operations. Nevertheless, the current open-circuit fault diagnosis system faces challenges in precisely detecting specific faulty switches. The proposed work presents a neural network-based open-circuit fault diagnosis system for identifying faulty power switches in inverter-driven motor systems. The system leverages trained phase-to-phase voltage data from the motor to recognize the type and location of faults in each phase with high accuracy. Employing separate neural networks for each of the three phases in a three-phase permanent magnet synchronous motor, the system achieves an outstanding overall fault detection accuracy of approximately 99.8%, with CNN and CNN-LSTM architectures demonstrating superior performance. This work makes two key contributions: (1) implementing neural networks to significantly improve the accuracy of locating faulty switches in open-circuit fault scenarios, and (2) identifying the optimal neural network architecture for effective fault diagnosis within the proposed system.
]]>World Electric Vehicle Journal doi: 10.3390/wevj15020070
Authors: Pradeep Vishnuram Sureshkumar Alagarsamy
The promotion of electric vehicles (EVs) as sustainable energy sources for transportation is advocated due to global considerations such as energy consumption and environmental challenges. The recent incorporation of renewable energy sources into virtual power plants has greatly enhanced the influence of electric vehicles in the transportation industry. Vehicle grid integration offers a practical and economical method to improve energy sustainability, addressing the requirements of consumers on the user side. The effective utilisation of electric vehicles in stationary applications is highlighted by technological breakthroughs in the energy sector. The continuous advancement in science and industry is confirming the growing efficiency of electric vehicles (EVs) as virtual power plants. Nonetheless, a thorough inquiry is imperative to elucidate the principles, integration, and advancement of virtual power plants in conjunction with electric automobiles, specifically targeting academics and researchers in this field. The examination specifically emphasises the energy generation and storage components used in electric vehicles. In addition, it explores several vehicle–grid integration (VGI) configurations, such as single-stage, two-stage, and hybrid-multi-stage systems. This study also considers the various types of grid connections and the factors related to them. This detailed investigation seeks to offer insights into the various facets of incorporating electric vehicles into virtual power plants. It takes into account technology improvements, energy sustainability, and the practical ramifications for users.
]]>World Electric Vehicle Journal doi: 10.3390/wevj15020069
Authors: Li Wang Yifan Ding Zhiyuan Chen Zhiyuan Su Yufeng Zhuang
In light of the widespread use of electric vehicles for urban distribution, this paper delves into the electric vehicle routing problem (EVRP): specifically addressing multiple trips per vehicle, diverse vehicle types, and simultaneous pickup and delivery. The primary objective is to minimize the overall cost, which encompasses travel expenses, waiting times, recharging costs, and fixed vehicle costs. The focal problem is formulated as a heterogeneous and multi-trip electric vehicle routing problem with pickup and delivery (H-MT-EVRP-PD). Additionally, we introduce two heuristic algorithms to efficiently approximate solutions within a reasonable computational time. The variable neighborhood search (VNS) algorithm and the adaptive large neighborhood search (ALNS) algorithm are presented and compared based on our computational experiences with both. Through solving a series of large-scale real-world instances for the H-MT-EVRP-PD and smaller instances using an exact method, we demonstrate the efficacy of the proposed approaches.
]]>World Electric Vehicle Journal doi: 10.3390/wevj15020068
Authors: Yu Zhang Zhaozhao Hu Tiezhou Wu
In recent years, the number of new energy vehicles has increased rapidly. The online state-of-health (SOH) prediction of lithium-ion batteries, which are core components of new energy vehicles, is crucial for maintaining vehicle safety. However, existing data-driven methods encounter challenges such as the difficult application of health feature extraction methods in practice, single feature dimensions, and complex algorithm models. This study extracted the peak height of the incremental capacity (IC) curve, constant-current charging time, and time when the battery surface temperature reaches its maximum value as health features from multiple dimensions. Furthermore, by randomly generating prey, the Pelican Optimization Algorithm (POA) fundamentally overcomes the shortcomings of traditional swarm intelligence optimization algorithms which are prone to falling into local optimal solutions. POA was introduced to optimize the Deep Extreme Learning Machine (DELM), which maximally simplified the algorithm model while ensuring accuracy. The experimental results demonstrate that this method does not require extensive historical data support. Whether applied to batteries under the same or different working conditions, all four battery groups exhibit excellent prediction results, with Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE) values below 1.2%.
]]>World Electric Vehicle Journal doi: 10.3390/wevj15020067
Authors: Pannee Suanpang Pitchaya Jamjuntr
As global awareness for preserving natural energy sustainability rises, electric vehicles (EVs) are increasingly becoming a preferred choice for transportation because of their ability to emit zero emissions, conserve energy, and reduce pollution, especially in smart cities with sustainable development. Nonetheless, the lack of adequate EV charging infrastructure remains a significant problem that has resulted in varying charging demands at different locations and times, particularly in developing countries. As a consequence, this inadequacy has posed a challenge for EV drivers, particularly those in smart cities, as they face difficulty in locating suitable charging stations. Nevertheless, the recent development of deep reinforcement learning is a promising technology that has the potential to improve the charging experience in several ways over the long term. This paper proposes a novel approach for recommending EV charging stations using multi-agent reinforcement learning (MARL) algorithms by comparing several popular algorithms, including the deep deterministic policy gradient, deep Q-network, multi-agent DDPG (MADDPG), Real, and Random, in optimizing the placement and allocation of the EV charging stations. The results demonstrated that MADDPG outperformed other algorithms in terms of the Mean Charge Waiting Time, CFT, and Total Saving Fee, thus indicating its superiority in addressing the EV charging station problem in a multi-agent setting. The collaborative and communicative nature of the MADDPG algorithm played a key role in achieving these results. Hence, this approach could provide a better user experience, increase the adoption of EVs, and be extended to other transportation-related problems. Overall, this study highlighted the potential of MARL as a powerful approach for solving complex optimization problems in transportation and beyond. This would also contribute to the development of more efficient and sustainable transportation systems in smart cities for sustainable development.
]]>World Electric Vehicle Journal doi: 10.3390/wevj15020066
Authors: Tehseen Ilahi Tahir Izhar Muhammad Zahid Akhtar Rasool Kelebaone Tsamaase Tausif Zahid Ehtisham Muhammad Khan
Trending electric vehicles with different battery technologies need universally compatible and fast chargers. Present semiconductor technology is not suitable for designing high-power-rating converters. The increasing demand for high-capacity electric vehicle chargers requires efficient and optimum advanced material technology. This research presents next-generation material-based smart ultra-fast electric vehicle charging infrastructure for upcoming high-capacity EV batteries. The designed level 4 charger will be helpful for charging future heavy-duty electric vehicles with battery voltages of up to 2000 V. The designed infrastructure will be helpful for charging both EVs and heavy-duty electric trucks with a wide range of power levels. Wireless sensor-based smart systems monitor and control the overall charging infrastructure. The detailed design analysis of the proposed charger using the Simscape physical modeling tool is discussed using mathematical equations.
]]>World Electric Vehicle Journal doi: 10.3390/wevj15020065
Authors: Weijun Wang Zefeng Liu Songlin Yang Xiyan Song Yuanyuan Qiu Fengjuan Li
Most of the research on driving stability control of distributed drive electric vehicles is based on a yaw motion design controller. The designed controller can improve the lateral stability of the vehicle well but rarely mentions its changes to the roll and pitch motion of the body, and the uneven distribution of the driving force will also cause instability in the vehicle speed, resulting in wheel transition slip, wheel sideslip, and vehicle stability loss. In order to improve the spatial stability of distributed-driven electric vehicles and resolve the control instability caused by their motion coupling, a decoupled control strategy of yaw, roll, and pitch motion based on multi-objective constraints was proposed. The strategy adopts hierarchical control logic. At the upper level, a yaw motion controller based on robust model predictive control, a roll motion controller, and a pitch motion controller based on feedback optimal control are designed. In the lower level, through the motion coupling analysis of the vehicle yaw control process, based on the coupling analysis, the vehicle yaw, roll, and pitch decoupling controller based on multi-objective constraints is designed. Finally, the effectiveness of the decoupling controller is verified.
]]>World Electric Vehicle Journal doi: 10.3390/wevj15020064
Authors: Boris V. Malozyomov Nikita V. Martyushev Vladislav V. Kukartsev Vladimir Yu. Konyukhov Tatiana A. Oparina Nadezhda S. Sevryugina Valeriy E. Gozbenko Viktor V. Kondratiev
Electric vehicles are the most innovative and promising area of the automotive industry. The efficiency of a traction battery is an important factor in the performance of an electric vehicle. This paper presents a mathematical model of an electric truck, including modules for the traction battery to determine the depth of battery discharge during the operation of the electric truck, a traction electric system for the electric truck and a system for calculating traction forces on the shaft in electric motors. As a result of the modelling, the charging and discharging currents of an accumulator battery in a real cycle of movement in peak and nominal modes of operation in electric motors and at different voltages of the accumulator battery are determined. A functional scheme of a generalized model of the electric vehicle traction electrical equipment system is developed. An experimental battery charge degree, torques of asynchronous electric motors, temperature of electric motors and inverters, battery voltage and the speed of electric motors have been measured and analysed. The developed complex mathematical model of an electric vehicle including a traction battery, two inverters and two asynchronous electric motors integrated into an electric portal bridge allowed us to obtain and study the load parameters of the battery in real driving cycles. Data were verified by comparing simulation results with the data obtained during driving.
]]>World Electric Vehicle Journal doi: 10.3390/wevj15020063
Authors: Hao Lei Xinghua Hu Jiahao Zhao Dongde Deng Ran Wang
Electric buses have been vigorously promoted and implemented in major countries worldwide and have generated a huge demand for charging stations. Optimizing the daily charging experience of electric buses, adapting the daily operation scheduling, improving the utilization rate of charging stations, reducing the load on the power grid, and improving the operation efficiency of electric bus line networks require the reasonable layout of the charging stations. In this study, public transportation charging station layout and siting is the research object. We summarize the progress of analysis methods from the charging station and vehicle sides; introduce related research on the planning and layout of charging stations based on optimization models, including cost analysis and siting and layout for electric bus systems; summarize the data-driven station planning and siting research; and provide an overview of the current charging demand estimation, accuracy, and charging efficiency. Finally, we address the problems of the charging demand estimation accuracy, the mismatch between the charging station layouts for electric buses, and the charging demand on a long time scale. We suggest that research be conducted on data fusion for the temporal and spatial refinement of charging demand prediction in the context of the electrification of public transportation systems and the big data of telematics.
]]>World Electric Vehicle Journal doi: 10.3390/wevj15020062
Authors: Yixiao Zhang Yuanming Gong Xiaolong Chen
Vehicle detection and location is one of the key sensing tasks of automatic driving systems. Traditional detection methods are easily affected by illumination, occlusion and scale changes in complex scenes, which limits the accuracy and robustness of detection. In order to solve these problems, this paper proposes a vehicle detection and location method for YOLOv5(You Only Look Once version 5) based on binocular vision. Binocular vision uses two cameras to obtain images from different angles at the same time. By calculating the difference between the two images, more accurate depth information can be obtained. The YOLOv5 algorithm is improved by adding the CBAM attention mechanism and replacing the loss function to improve target detection. Combining these two techniques can achieve accurate detection and localization of vehicles in 3D space. The method utilizes the depth information of binocular images and the improved YOLOv5 target detection algorithm to achieve accurate detection and localization of vehicles in front. Experimental results show that the method has high accuracy and robustness for vehicle detection and localization tasks.
]]>World Electric Vehicle Journal doi: 10.3390/wevj15020061
Authors: Sixia Zhao Zhi Gao Xianzhe Li Yanying Li Liyou Xu
In recent years, more and more attention has been paid to fuel cell-based hybrid tractors. In order to optimize the global power distribution of tractors and further improve the fuel economy and fuel cell durability of the system, this paper designs an energy management strategy to maximize external energy efficiency based on fuel cell/lithium battery/supercapacitor hybrid tractors. This strategy aims to reduce the real-time hydrogen consumption of the system while maximizing the external energy output so as to reduce the impact of load randomness on the output power of the fuel cell. Under the typical ploughing conditions of the tractor, the simulation is compared with the state machine strategy and the equivalent hydrogen consumption minimization strategy. The results show that the proposed strategy meets the power requirements of a given ploughing condition, and compared with the two traditional strategy systems, the performance characteristics of auxiliary energy are more fully exerted. It reduces the burden on fuel cells and improves the durability of fuel cells. The hydrogen consumption of the system was reduced by 11.03 g and 16.54 g, respectively, improving the overall economy of the hybrid system.
]]>World Electric Vehicle Journal doi: 10.3390/wevj15020060
Authors: K. Karthick S. Ravivarman R. Priyanka
Electric vehicles (EVs) are becoming increasingly popular, due to their beneficial environmental effects and low operating costs. However, one of the main challenges with EVs is their short battery life. This study presents a comprehensive approach for predicting the Remaining Useful Life (RUL) of Nickel Manganese Cobalt-Lithium Cobalt Oxide (NMC-LCO) batteries. This research utilizes a dataset derived from the Hawaii Natural Energy Institute, encompassing 14 individual batteries subjected to over 1000 cycles under controlled conditions. A multi-step methodology is adopted, starting with data collection and preprocessing, followed by feature selection and outlier elimination. Machine learning models, including XGBoost, BaggingRegressor, LightGBM, CatBoost, and ExtraTreesRegressor, are employed to develop the RUL prediction model. Feature importance analysis aids in identifying critical parameters influencing battery health and lifespan. Statistical evaluations reveal no missing or duplicate data, and outlier removal enhances model accuracy. Notably, XGBoost emerged as the most effective algorithm, providing near-perfect predictions. This research underscores the significance of RUL prediction for enhancing battery lifecycle management, particularly in applications like electric vehicles, ensuring optimal resource utilization, cost efficiency, and environmental sustainability.
]]>World Electric Vehicle Journal doi: 10.3390/wevj15020059
Authors: David Wenander Mats Alaküla
A radical transformation of the transport industry is required in order to achieve a fossil-fuel-free vehicle fleet and reach the greenhouse gas emissions goals. Electrification plays a crucial role in this radical process. An electric road system (ERS) is a road that supplies power to electric vehicles as they drive on it, offering numerous advantages. These include an extended driving range, decreased reliance on batteries, and increased flexibility and convenience for drivers, eliminating the need to stop for recharging. This paper highlights the transformative potential of ERS in revolutionizing the land transport sector. Through thorough testing with a conductive ERS demonstrator, the viability of the presented technology is validated. Essential aspects like power transfer, efficiency, safety, and environmental impact showcase ERS’s adaptability and scalability across diverse vehicle types. This study recommends widespread ERS support for battery electric vehicles, emphasizing the route toward a sustainable future.
]]>World Electric Vehicle Journal doi: 10.3390/wevj15020058
Authors: Yuxin Xie Shengkun Cai Guangye Li Zhizhen Liu Yuandi Zhao Gangjie Qiao Xianglin Li
In order to improve wireless charging power and reduce heating problems, the optimal design of the high-current wireless charging coil has always been the research focus of wireless charging system research. This paper proposes a multi-branch and multi-capacitance current sharing method for series–series (SS) receiving coils. Firstly, the current sharing model with n branches that are connected parallel to multiple compensation capacitors is established. The current sharing situation of parallel coils with three branches and three capacitors with independently resonant compensation is analyzed. Then, the wireless charging system with the parallel coils of 48 V/100 A receiving coils is simulated. The results show that when one capacitor is used for compensation, the three-coil currents highly differ; when three capacitors are compensated independently, the three-coil currents are basically equal. The simulation results show that the current sharing method can effectively improve the charging power of the system and reduce the maximum temperature of the receiving coil, which proves the effectiveness of this method. Finally, through the experimental comparison, it is verified that the current sharing measure can make the current of each wire basically equal.
]]>World Electric Vehicle Journal doi: 10.3390/wevj15020057
Authors: Nafisa Mahbub Hajo Ribberink
In a simulation study, it was investigated how the costs of supplying H2 for the refuelling of long-haul trucks along highways in Canada can be minimized by optimizing the design of the refuelling infrastructure. Scenarios using local or centralized blue H2 production were evaluated using two different modes of H2 transportation (liquid H2 tanker trucks and pipelines). For each scenario, the average H2 supply costs were determined considering H2 production costs from facilities of different sizes and transportation costs for H2 that was not produced locally. Average H2 supply costs were 2.83 CAD/kg H2 for the scenario with local H2 production at each refuelling site, 3.22–3.27 CAD/kg H2 for scenarios using centralized H2 production and tanker truck transportation, and 2.92–2.96 CAD/kg H2 for centralized H2 production scenarios with pipeline transportation. Optimized scenarios using the cheaper transportation option (tanker truck or pipeline) for each highway segment had average H2 supply costs (2.82–2.88 CAD/kg H2) similar to those of using only local H2 production, with slightly lower costs for the scenario using the largest H2 production volumes. Follow-on research is recommended to include the impact of CO2 transportation (from blue H2 production) on the design of an optimum supply infrastructure.
]]>World Electric Vehicle Journal doi: 10.3390/wevj15020056
Authors: Christian Will Fabian Ocker
In addition to passenger vehicles, battery-electric trucks and buses could offer substantial flexibility to the energy system. Using a Bass diffusion model, we extrapolated the unidirectional charging needs and availability of trucks in five of eleven typical applications, as well as city buses, for Germany until 2040. Combined, these heavy-duty vehicles could provide up to 23 GW of down-regulating flexibility potential (i.e., in case of excess power supply) in 2040. The resulting revenues could contribute to reducing electricity costs for depot operators. These results illustrate the need to provide easy and automated market access to heavy-duty vehicle fleets.
]]>World Electric Vehicle Journal doi: 10.3390/wevj15020055
Authors: Francesco Cerrito Massimo Canale Massimiliana Carello
This paper presents the design of an energy management control system to improve powertrain efficiency and optimize the amount of fuel used by a hybrid fuel cell vehicle in a route-based scenario. To reach this goal, a complete tank-to-wheel model is developed under the assumption of a known scenario, the speed profile that best minimizes the energy required to complete the test is computed, and a controller able to handle the power request is designed. In particular, a Model Predictive Control architecture is used to split the power request between the primary and the secondary power source (fuel cell and supercapacitors). The effectiveness of the proposed approach is assessed through extensive simulation tests using a realistic model.
]]>World Electric Vehicle Journal doi: 10.3390/wevj15020054
Authors: Sai Bhargava Althurthi Kaushik Rajashekara Tutan Debnath
In electric vehicle fast charging systems, it is important to minimize the effect of fast charging on the grid and it is also important to operate the charging system at high efficiencies. In order to achieve these objectives, in this paper, a sinusoidal half-wave DC current charging protocol and a sinusoidal half-wave pulsed current charging protocol are proposed for the fast charging of Li-ion batteries. A detailed procedure is presented for implementing the following proposed methods: (a) a pre-defined half-sine wave current function and (b) a pulsed half-sine wave current method. Unlike the conventional full-wave sinusoidal ripple current charging protocols, the proposed study does not utilize any sinusoidal full-wave ripple. The performance of these new charging methods on Ni-Co-Al-type Li-cells is studied and compared with the existing constant current and positive pulsed current charging protocols, which have been discussed in the existing literature. In addition, the changes in the electrochemical impedance spectrograph of each cell are examined to study the effects of the applied charging methods on the internal resistance of the Li cell. Finally, the test results are presented for 250 life cycles of charging at 2C (C: charging rate) and the degradation in cell capacities are compared among the four different methods for the Ni-Co-Al-type Li cell.
]]>World Electric Vehicle Journal doi: 10.3390/wevj15020053
Authors: Badii Gmati Amine Ben Rhouma Houda Meddeb Sejir Khojet El Khil
Availability and continuous operation under critical conditions are very important in electric machine drive systems. Such systems may suffer from several types of failures that affect the electric machine or the associated voltage source inverter. Therefore, fault diagnosis and fault tolerance are highly required. This paper presents a new robust deep learning-based approach to diagnose multiple open-circuit faults in three-phase, two-level voltage source inverters for induction-motor drive applications. The proposed approach uses fault-diagnosis variables obtained from the sigmoid transformation of the motor stator currents. The open-circuit fault-diagnosis variables are then introduced to a bidirectional long short-term memory algorithm to detect the faulty switch(es). Several simulation and experimental results are presented to show the proposed fault-diagnosis algorithm’s effectiveness and robustness.
]]>World Electric Vehicle Journal doi: 10.3390/wevj15020050
Authors: Fady M. A. Hassouna Kangwon Shin
Recently, major problems related to fuel consumption and greenhouse gas (GHG) emissions have arisen in the transportation sector. Therefore, developing transportation modes powered by alternative fuels has become one of the main targets for car manufacturers and governments around the world. This study aimed to investigate the economic prospects of using hydrogen fuel cell technology in taxi fleets in Westbank. For this purpose, a model that could predict the number of taxis was developed, and the expected economic implications of using hydrogen fuel cell technology in taxi fleets were determined based on the expected future fuel consumption and future fuel cost. After analysis of the results, it was concluded that a slight annual increase in the number of taxis in Palestine is expected in the future, due to the government restrictions on issuing new taxi permits in order to get this sector organized. Furthermore, using hydrogen fuel cells in taxi fleets is expected to become more and more feasible over time due to the expected future increase in oil price and the expected significant reduction in hydrogen cost as a result of the new technologies that are expected to be used in the production and handling of hydrogen.
]]>World Electric Vehicle Journal doi: 10.3390/wevj15020052
Authors: Pg Emeroylariffion Abas Benedict Tan
Electric Vehicles (EVs) emerge as a crucial solution for alleviating the environmental footprint of the transportation sector. However, fostering their widespread adoption demands effective, targeted policies. This study introduces a versatile model, amalgamating stakeholders and policies and leveraging local data with broader market applicability. It delineates two key EV adopter groups—innovators and imitators—shedding light on their evolving impact on adoption trends. A pivotal feature of the model is the factoring of EV attractiveness, comprising Life-Cycle Cost (LCC), Driving Range, Charging Time, and infrastructure availability, all of which are expected to improve with the fast technological advancement of EVs. Financial policies, notably subsidies, prove potent in boosting EV adoption but fall short of targeted sales due to imitator lag. In response, a pragmatic solution is proposed: a government-led EV acquisition of 840 EVs, coupled with a 20% subsidy on new EV purchases and a 20% tax on new ICEV purchases, potentially realizing a 30% EV sales target by 2035. Future research avenues may delve into behavioral dynamics prompting imitators’ adoption, optimizing EV infrastructure strategies, and assessing the socio-economic impacts of EVs. Interdisciplinary approaches hold promise for enriched insights for effective EV integration policies.
]]>World Electric Vehicle Journal doi: 10.3390/wevj15020051
Authors: Weisheng Cai Chengye Liu
The thermal decay of the brake has a great impact on the long downhill braking stability of pure electric commercial vehicles. Based on the road slope and using the fuzzy control method, the motor regenerative braking force and friction braking force distribution strategies were designed to reduce the friction braking force, improve the braking stability and recover the braking energy. By establishing road driving conditions with different slopes, numerical analysis methods are used to verify the proposed control strategy. The results show that the vehicle maintains a constant speed downhill at 30 km/h under the condition of 6% constant slope driving, and the braking energy recovery rate reaches 50.93% under 60% initial battery SOC, 50.89% under 70% initial battery SOC, and 50.81% under 80% initial battery SOC. The speed of the vehicle fluctuates slightly under the driving condition of an 18 km long variable slope distance, but the power torque of the electric mechanism can still be maintained at a constant speed of 30 km/h by adjusting the electric mechanism, and the braking energy recovery rate reaches 49.96%. During the downhill driving at a constant speed, the friction braking force does not participate in braking, and the recuperation rate of braking is determined by the slope and the magnitude of braking deceleration.
]]>World Electric Vehicle Journal doi: 10.3390/wevj15020049
Authors: Parnian Fakhrooeian Volker Pitz Birgit Scheppat
In this paper, we present a comprehensive assessment of the effects of residential loads, electric vehicles (EVs), and electric heat pumps (HPs) on low-voltage (LV) grids in urban, suburban, and rural areas of Germany. Firstly, real data are used to determine the typical structures for each LV grid region. Secondly, nine scenarios are defined with different levels of EV and HP penetration. Thirdly, the Low Voltage Load Flow Calculation in the DIgSILENT PowerFactory is performed for all scenarios while taking the simultaneity factor (SF) for each load type into consideration to calculate the minimum voltage and maximum loadings of transformer and lines in each grid; this allows for the grid’s potential bottlenecks to be identified. The network simulations are carried out with the consideration of charging powers of 11 kW and 22 kW in order to evaluate how an increasing EV load in the future may affect the grid’s parameters. To the best of our knowledge, no study in the literature has simultaneously addressed all of the aforementioned topics. The results of this study provide a useful framework that distribution system operators (DSOs) may apply to anticipate the forthcoming challenges and figure out when grid reinforcement will be required.
]]>World Electric Vehicle Journal doi: 10.3390/wevj15020048
Authors: Jose A. Ruz-Hernandez Ramon Garcia-Hernandez Mario Antonio Ruz Canul Juan F. Guerra Jose-Luis Rullan-Lara Jaime R. Vior-Franco
This paper presents the design and simulation of a neural sliding mode controller (NSMC) for a regenerative braking system in an electric vehicle (EV). The NSMC regulates the required current and voltage of the bidirectional DC-DC buck–boost converter, an element of the auxiliary energy system (AES), to improve the state of charge (SOC) of the battery of the EV. The controller is based on a recurrent high-order neural network (RHONN) trained using the extended Kalman filter (EKF) and the unscented Kalman filter (UKF) as the tools to train the neural networks to obtain a higher SOC in the battery. The performance of the controller with the two training algorithms is compared with a proportional integral (PI) controller illustrating the differences and improvements obtained with the EKF and the UKF. Furthermore, robustness tests considering Gaussian noise and varying of parameters have demonstrated the outcome of the NSMC over a PI controller. The proposed controller is a new strategy with better results than the PI controller applied to the same buck–boost converter circuit, which can be used for the main energy system (MES) efficiency in an EV architecture.
]]>World Electric Vehicle Journal doi: 10.3390/wevj15020047
Authors: Dersu Çeliksöz Varlık Kılıç
This work focuses on developing a mobility control system for high-speed series-hybrid electric tracked vehicles, which operate with independent traction motors for each track. The scope of this research includes modeling a series-hybrid powertrain specific to military tracked vehicles and conducting an in-depth analysis of its dynamic behavior. Subsequently, this study conducts a critical review of mobility control approaches sourced from the literature, identifying key techniques relevant to high-inertia vehicular applications. Building on foundational models, this study proposes a robust closed-loop mobility control system aimed at ensuring precise and stable off-road vehicle operations. The system’s resilience and adaptability to a variety of driving conditions are emphasized, with a particular focus on handling maneuvers such as steering and pivoting, which are challenging operations for tracked vehicle agility. The performance of the proposed mobility control system is tested through a series of simulations, covering a spectrum of operational scenarios. These tests are conducted in both offline simulation settings, which permit meticulous fine-tuning of system parameters, and real-time environments that replicate actual field conditions. The simulation results demonstrate the system’s capacity to improve the vehicular response and highlight its potential impact on future designs of mobility control systems for the heavy-duty vehicle sector, particularly in defense applications.
]]>World Electric Vehicle Journal doi: 10.3390/wevj15020046
Authors: Ettore Bianco Sandro Rubino Massimiliana Carello Iustin Radu Bojoi
Nowadays, electric vehicles have gained significant attention as a promising solution to the environmental concerns associated with traditional combustion engine vehicles. With the increasing demand for high-performance hypercars, the need for advanced torque control strategies has become paramount. Field-Oriented Control using Current Vector Control represents a consolidated solution to implement torque control. However, this kind of control must take into account the DC link voltage variation and the variation of motor parameters depending on the magnets’ temperature while providing the maximum torque production for specific inverter current and voltage limitations. Multidimensional lookup tables are needed to provide a robust torque control from zero speed up to maximum speed under deep flux-weakening operation. Therefore, this article aims to explore the application of FOC 4D control in electrical hypercars and its impact on enhancing their overall performance and control stability. The article will delve into the principles underlying FOC 4D control and its advantages, challenges, and potential solutions to optimize the operation of electric hypercars. An electric powertrain model has been developed in the Simulink environment with the Simscape tool using a S-function block for the implementation of digital control in C-code. High-power electric motor electromagnetic parameters, derived from a Finite Element Method magnetic model, have been used in the simulation. The 4D LUTs have been computed from the motor flux maps and implemented in C-code in the S-function. The choice of FOC 4D control has been validated in the main load points of a hypercar application and compared to the conventional FOC. The final part of the research underlines the benefits of the FOC 4D on reliability, critical in motorsport applications.
]]>World Electric Vehicle Journal doi: 10.3390/wevj15020045
Authors: Benhui Zhang Yan Cao Yanjin Hou Siyu Hou Yanhua Guo Jiawei Tian Xu He
In this paper, theoretical analysis and system simulations are carried out to study the effects of the transmitter-compensated inductance mistuning on charging power, transfer efficiency, and the phase angle between the input voltage and input current in a wireless power transfer (WPT) system using inductor/capacitor/capacitor-series (LCC-S) topology. To cancel out the effects of the mistuning, an integrated transmitting coil design scheme is proposed, in which the transmitting coil is unipolar while the compensation coils are bipolar. Theoretical calculations and simulations are performed to show that the proposed compensation inductor guarantees the stability of the compensation inductance when the permeability of the magnetic sheet changes. Furthermore, it is verified that by using the integrated structure the effect of the horizontal misalignment can be ignored. Finally, an experimental platform is built to validate the above results of theoretical analysis and simulation. This proves that the theoretical analysis and simulation results are consistent with each other, which confirmed the stability and feasibility of the integrated compensation inductor.
]]>World Electric Vehicle Journal doi: 10.3390/wevj15020044
Authors: Jianguo Xi Haozhe Si Jianping Gao
In order to improve the shifting quality of pure electric commercial vehicles, a torque control strategy based on the driving intention during the shifting process is presented in this paper. Firstly, dynamic analysis is conducted on the lifting and twisting stage in the two-speed Automated Mechanical Transmission (AMT) shift process without a synchronizer. Secondly, fuzzy identification is performed on the driver’s expected acceleration, incorporating the driver’s acceleration intention into the lifting and twisting process, and, further, the output time correction factor k is deblurred. Finally, the control time of the lifting and reducing torque is corrected to achieve dynamic adjustment of the control parameters during the shift process. The actual vehicle test results indicate that the proposed control strategy can enhance the shifting quality and adapt the performance of a vehicle to the driver’s expectations and requirements.
]]>World Electric Vehicle Journal doi: 10.3390/wevj15020043
Authors: Yinquan Hu Heping Liu Hu Huang
Accurate and real-time estimation of pack system-level chips is essential for the performance and reliability of future electric vehicles. Firstly, this study constructed a model of a nickel manganese cobalt cell on the ground of the electrochemical process of the packs. Then, it used methods on the grounds of the unscented Kalman filter and unscented Kalman particle filter for system-level chip estimation and algorithm construction. Both algorithms are on the ground of Kalman filters and can handle nonlinear and uncertain system states. In comparative testing, it can be seen that the unscented Kalman filter algorithm can accurately evaluate the system-level chip of the nickel manganese cobalt cell under intermittent discharge conditions. The system-level chip was 0.53 at 1000 s and was reduced to 0.45 at 1500 s. These results demonstrate that the evaluation of the ternary lithium battery pack’s performance is time-dependent and indicate the accuracy of the algorithm used during this time period. These data should be considered in the broader context of the study for a comprehensive understanding of their meaning. In the later stage, the estimation error of the recursive least-squares unscented Kalman particle filter method for system-level chips began to significantly increase, gradually exceeding 1%, with a corresponding root-mean-square error of 0.002171. This indicates that the recursive least-squares optimization algorithm, the unscented Kalman particle filter algorithm, diminished its root mean square error by 27.59%. The unscented Kalman filter and unscented Kalman particle filter are effective in estimating the system-level chip of nickel manganese cobalt cells. However, UPF performs more robustly in handling complex situations, such as pack aging and temperature changes. This study provides a new perspective and method that has a high reference value for pack management systems. This helps to achieve more effective energy management and improve pack life, thereby enhancing the reliability and practicality of electric vehicles.
]]>World Electric Vehicle Journal doi: 10.3390/wevj15020042
Authors: Sarmad Ali Muhammad Mahabat Khan Muhammad Irfan
The rapid increase in emissions and the depletion of fossil fuels have led to a rapid rise in the electric vehicle (EV) industry. Electric vehicles predominantly rely on lithium-ion batteries (LIBs) to power their electric motors. However, the charging and discharging processes of LIB packs generate heat, resulting in a significant decline in the battery performance of EVs. Consequently, there is a pressing need for effective battery thermal management systems (BTMSs) for lithium-ion batteries in EVs. In the current study, a novel experimental BTMS was developed for the thermal performance enhancement of an LIB pack comprising 2 × 2 cells. Three distinct fin configurations (circular, rectangular, and tapered) were integrated for the outer wall of the lithium-ion cells. Additionally, the cells were fully submerged in phase change material (PCM). The study considered 1C, 2C, and 3C cell discharge rates, affiliated with their corresponding volumetric heat generation rates. The combination of rectangular fins and PCM manifested superior performance, reducing the mean cell temperature by 29.71% and 28.36% compared to unfinned lithium-ion cells under ambient conditions at the 1C and 2C discharge rates. Furthermore, at the 3C discharge rate, lithium-ion cells equipped with rectangular fins demonstrated a delay of 40 min in reaching the maximum surface temperature of 40 °C compared to the unfinned ambient case. After 60 min of battery discharge at the 3C rate, the cell surface temperature of the rectangular fin case only reached 42.7 °C. Furthermore, numerical simulations showed that the Nusselt numbers for lithium-ion cells with rectangular fins improved by 9.72% compared to unfinned configurations at the 3C discharge rate.
]]>World Electric Vehicle Journal doi: 10.3390/wevj15020041
Authors: Joeri Van Mierlo
The World Electric Vehicle Journal was started in 2007 with the aim of providing a platform for scientific communications within the field of electric vehicles [...]
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