Electric Vehicles Energy Management

Despite the unstoppable global drive towards electric mobility, the electrification of sub-Saharan Africa’s ubiquitous informal multi-passenger minibus taxis raises substantial concerns. This is due to a constrained electricity system, both in terms of generation capacity and distribution networks. Without careful planning and mitigation, the additional load of charging hundreds of thousands of electric minibus taxis during peak demand times could prove catastrophic. This paper assesses the impact of charging 202 of these taxis in Johannesburg, South Africa. The potential of using external stationary battery storage and solar PV generation is assessed to reduce both peak grid demand and total energy drawn from the grid. With the addition of stationary battery storage of an equivalent of 60 kWh/taxi and a solar plant of an equivalent of 9.45 kW${}_{\mathrm{pk}}$/taxi, the grid load impact is reduced by 66%, from 12 kW/taxi to 4 kW/taxi, and the daily grid energy by 58% from 87 kWh/taxi to 47 kWh/taxi. The country’s dependence on coal to generate electricity, including the solar PV supply, also reduces greenhouse gas emissions by 58%. Full article

The electrochemical impedance spectrum (EIS) is a non-destructive technique for the on-line evaluation and monitoring of the performance of lithium-ion batteries. However, the measured EIS can be unstable and inaccurate without the proper resting time. Therefore, we conducted comprehensive EIS tests during the charging process and at different state of charge (SOC) levels with various resting times. The test results revealed two findings: (1) EIS tests with a constant long resting time showed a clear pattern in the impedance spectral radius—a decrease followed by a slight increase. We analyzed the impedance data using an equivalent circuit model and explained the changes through circuit parameters. (2) We examined the effect of resting time on impedance at consistent SOC levels. While low SOC levels exhibited significant sensitivity to resting time, medium SOC levels showed less sensitivity, and high SOC levels had minimal impact on resting time. The equivalent circuit parameters matched the observed trends. Kramers–Kronig transformation was conducted to assess the reliability of the experiments. This study not only summarizes the relationship between the EIS and SOC but also highlights the importance of resting time in impedance analysis. Recognizing the role of the resting time could enhance impedance-based battery studies, contribute to refined battery status evaluation, and help researchers to design proper test protocols. Full article

Energy management plays a crucial role in achieving extended endurance for solar-powered Unmanned Aerial Vehicles (UAVs). Current studies in energy management primarily focus on natural energy harvesting and task-oriented path planning. This paper aims to optimize energy consumption during the climb and glide stages by exploring variable climb speeds and glide powers. To achieve this, fitness functions are established for both the climb and glide stages, taking into account the maximum climb speed and glide power limits of the aircraft. The particle swarm optimization (PSO) algorithm is employed to solve the problem, resulting in significant energy savings of over 68% in the climb stage and 4.8% in the glide stage. Based on an analysis of the optimization trends, this study proposes an energy-management strategy to fulfill the demand for long-endurance flights. The findings of this study can serve as a valuable reference for high-altitude missions that require extended flight times. Full article

The nascent electrification of transport has heralded a new chapter in the driving force of mobility. Developing regions such as sub-Saharan Africa already lag in this transformative transport transition. A potential transitional step towards full-scale electric mobility is the retrofitting of the existing fleet of internal combustion-based vehicles. This paper proposes a novel approach to the design of a retrofit electric drivetrain for an internal combustion engine vehicle. Specifically, a minibus taxi, which dominates the region’s informal paratransit industry, is electrified. This retrofit is the first formal research presented with a focus on sub-Saharan Africa and its unique challenges. A generic methodology is presented to systematically specify and select drivetrain components and assess the suitability and characteristics of those components. Unique about the presented methodology is the application of driving-cycle data of internal combustion engine vehicles, which provides quantitative insights into the performance and characteristics of the selected components for a retrofit. Finally, a real-world use case is presented to provide a tangible example and to validate the feasibility of the presented approach. Full article

Given the influence of the randomness of driving conditions on the energy management strategy of vehicles, deep reinforcement learning considering driving conditions prediction was proposed. A working condition prediction model based on the BP neural network was established, and the correction coefficient of vehicle demand torque was determined according to the working condition prediction results. An energy management strategy and deep reinforcement learning were integrated to build an energy management strategy with deep reinforcement learning based on driving condition prediction. Simulation experiments were conducted according to the actual collected working condition data. The experimental results show that the energy management strategy, i.e., deep reinforcement learning considering working condition prediction, has faster convergence speed and more vital self-learning ability, and the equivalent fuel consumption per 100 km under different driving conditions is 6.411 L/100 km, 6.327 L/100 km, and 6.388 L/100 km, respectively. Compared with the unimproved strategy, the fuel economy can be improved by 3.18%, 3.08%, and 2.83%. The research shows that the energy management strategy, the deep reinforcement learning based on driving condition prediction, is effective and adaptive. Full article

A balise is a specialized device utilized for train communication and control. However, existing balises are composed of separate components, resulting in high production costs, low integration, and significant room for improvement in stability. We propose an integrated solution, to enhance the integration of balises, reducing the number of discrete components. Additionally, we propose a feedback-enabled energy extraction circuit with a limiter-based startup circuit, to enhance the stability of the balise system. The prototype was fabricated using 180 nm high voltage(HV) complementary metal oxide semiconductor(CMOS) technology. Once implemented in the balise, the number of discrete components in the system is reduced by 25%, and the system can start up within 20 ms and operate stably. The mean data rate (MDR) deviation of the balise is only 62 ppm, which is 69% lower than that specified in the test specification for the Eurobalise form–fit–function interface specification (FFFIS), and the maximum time interval error (MTIE) is less than 250 ns. Full article

An efficient and stable braking feedback scheme is one of the key technologies to improve the endurance performance of pure electric vehicles. In this study, four constraint conditions for different braking feedback schemes were clearly defined, and tests and simulation analysis were carried out based on “the relationship between rear-drive feedback efficiency and vehicle configuration conditions” and “the relationship between front-drive feedback efficiency and braking efficiency”. The results show that for rear-driving, the RSF2 scheme with low dependence on the constraint conditions of tramping characteristics is the comprehensive optimal scheme under the condition of decoupling control constraints, and the mileage improvement rate reaches 29.2%. For front driving, the FSF1A scheme is the comprehensive optimal scheme considering both braking efficiency and feedback efficiency, and the mileage improvement rate reaches 35.8%. Finally, the feasibility of the proposed braking feedback scheme is proved using the drum test under cyclic conditions, and the research results provide a theoretical basis for the optimization of braking feedback energy efficiency of small pure electric vehicles. Full article

The method of multi-sensor integrated navigation improves navigation accuracy by fusing various sensor data. However, when a sensor is disturbed or malfunctions, incorrect measurement information will seriously affect the estimation of the trajectory, which will lead to a decrease in accuracy. Existing factor graph models based on weights can neither fully resist the influence of disturbances nor guarantee the local rationality of estimated trajectories. In this paper, a factor graph with local constraints model that fuses the magnetic field and pedestrian dead reckoning data is proposed to navigate complex curved trajectories. First, adding local constraints to the pedestrian dead reckoning measurement converts the navigation solution problem into a hard-constrained nonlinear least squares problem. Then, a mapping model is constructed to reconstruct the variable space and the Adam gradient algorithm is used to realize a fast calculation. The navigation accuracy of this algorithm is better than that of the state-of-the-art method in real-world experiments, with an average accuracy of 0.83 m. Full article

As autonomous vehicle technology advances, the development of energy-efficient control methodologies emerges as a critical area in the literature. This includes the behavior control of vehicles near signalized intersections, which still needs comprehensive exploration. Through connectivity, the adoption of promising eco-driving approaches can manage a vehicle’s speed profile to improve energy consumption. This study focuses on controlling the speed of an autonomous electric vehicle (AEV) both up and downstream of a signalized intersection in the presence of preceding vehicles. In order to achieve this, a dynamic pro-active predictive cruise control eco-driving (eco-PPCC) framework is developed that, instead of merely reacting to the preceding vehicle’s speed changes, uses the preceding vehicle’s upcoming data to actively adjust and optimize the speed profile of the AEV. The proposed algorithm is compared to the conventional Gipps and eco-PCC models for benchmarking and performance analysis through numerous scenarios. Additionally, real-world measurements are performed and taken to consider practical use cases. The results demonstrate that when compared to the two baseline methods, the proposed framework can add significant value to reducing energy consumption, preventing unnecessary stops at intersections, and improving travel time. Full article

Compared with batteries, ultracapacitors have higher specific power and longer cycle life. They can act as power buffers to absorb peak power during charging and discharging, playing a role in peak shaving and valley filling, thereby extending the cycle life of the battery. In this article, a replaceable battery electric coupe SUV equipped with a lithium iron phosphate (LiFePO₄) power battery is taken as the research object, and a vehicle dynamics simulation model is established on the MATLAB/Simulink platform. Parameter matching and control optimization for a hybrid energy storage system (HESS) are conducted. Through a proven semiempirical cycle model of the LiFePO₄ power battery, the operating cycle life model is derived and used to estimate the battery cycle life. World Light Vehicle Test Cycle (WLTC) simulation results show that the HESS with 308 ultracapacitors can extend the cycle life of the LiFePO₄ power battery by 34.24%, thus significantly reducing the operation cost of the battery replacement station. Full article

Planning for the eventual electrification of transport in sub-Saharan Africa requires accurate simulation of its unique transport systems. The few studies that attempt to model electric minibus taxis—vehicles extensively used in sub-Saharan Africa’s public transport systems—vary greatly in their results. This paper analyses, compares and corrects the only two existing studies that project energy consumption of electric minibus taxis in the region. One of the studies projected an energy consumption of 0.39 kWh/km, while the other projected 0.93 kWh/km. This paper carefully analyses the simulation tools and models and cumulatively applies corrections from the literature and scientific analyses. As a result, the discrepancy between the two simulation tools is eliminated for a given data input and a final energy consumption is estimated in the range of 0.49–0.52 kWh/km, depending on the input data. Full article

Electric vehicles (EVs) are one of the most practical solutions to the energy issue and environmental pollution. In recent years, EVs have developed rapidly, but are still limited by charging problems. The emergence of photovoltaic charging stations can solve the environmental pollution and charging problems. The location of charging stations is critical in the life cycle of electric vehicles. In this paper, a multiple-criteria decision-making (MCDM) method based on Geographic Information Technology (GIS) for optimal site selection is proposed. First, based on literature reading and expert interviews, a site selection index system was identified, including four aspects with a total of ten sub-criteria. Secondly, a spatial database of relevant evaluation criteria was established using GIS, and preliminary analysis was conducted. Then, the fuzzy DEMATEL (Decision-Making Trial and Evaluation Laboratory method) is applied for assigning the criteria weights. Then, potential sites are ranked using the fuzzy MULTIMOORA (Multi-Objective Optimization on the basis of Ratio Analysis) method. Then, the model was validated by siting the electric vehicle PV charging stations in Qingdao, and eight stations were identified in the preliminary selection stage, and the most suitable locations were finally selected through the MCDM stage. Finally, the reliability and validity of the model were further verified by comparative analysis and dual sensitivity analysis. Full article

The article concerns the issue of retrofitting (i.e., the conversion of worn-out diesel buses into electric buses). As this solution is often cheaper than purchasing new electric buses, it can be attractive for low-population areas with a weaker economic infrastructure. The article aims to present an original method for rapidly selecting components for the electric traction system, such as the electric motor, inverter, and transmission systems, combined with a battery installed in a drawer. The battery swapping solution is dedicated to regions with underdeveloped power infrastructure that does not allow for fast charging of bus batteries using pantographs. A mathematical model in the form of a polynomial was developed to estimate the energy losses for a given route. This model consists of a bus physics model, an energy loss model in the propulsion system, and a battery model. The weight coefficients of the polynomials were determined based on an analytical analysis of the model dependencies. The obtained models were reduced using the Lasso regularization method in linear regression. The input data for the model includes route characteristics (or driving cycle) and technical characteristics of the traction system components. The model output provides a detailed profile of electric energy consumption and peak values of the drive system characteristics (e.g., maximum torque of the motor) which must not be exceeded. Implemented as computer software, the model—combined with a database of motors, inverters, drive transmission systems, and batteries—allows for a quick calculation of the possibilities of applying a selected configuration to cover a given route. The approach proposed in the article enables the rapid composition of electric traction devices based on required driving conditions during the initial vehicle prototyping stage. At the same time, it allows the state of the bus battery to be monitored and estimates the remaining range during the operation of upgraded buses. Full article

To exhaust the potential of energy efficiency and dynamic performance of the dual-motor four-drive powertrain, this study developed a multi-objective real-time optimal energy management strategy considering energy efficiency and flexible torque response. First, a theoretical analysis of energy loss and operating characteristics was performed to elucidate the energy-saving advantages and control challenges of the dual-motor four-drive powertrain. Second, an economic strategy based on the adaptive nonlinear particle swarm optimization (ANLPSO) and optimization freezing tolerance mechanism was devised to realize real-time optimal power distribution. Then, the pre-shifting recognition schedule and gradient torque recovery strategy were developed to achieve flexible torque response during gear shifting. Finally, smooth switching logic was created to assure a seamless transition between the two strategies. Numerous simulation results indicate that compared with the single-motor drive strategy, the proposed strategy can increase energy efficiency by 8.1%, 4.02%, and 9.49% under NEDC, WLTC, and CLTC, respectively. During shifting, the longitudinal acceleration and jerk of the proposed strategy are significantly superior to those of the original strategy, thereby enhancing the vehicle’s dynamic performance and ride comfort. The results of the drum experiment validate the efficacy of the proposed method for energy consumption optimization and torque coordination control in the actual vehicle environment. Full article

Electric vehicles are able to provide immediate power through the vehicle-to-grid function, and they can adjust their charging power level when in the grid-to-vehicle mode. This allows them to provide ancillary services such as frequency control. Their batteries differ from conventional energy storage systems in that the owner’s energy requirement constraint must be met when the vehicles participate in a frequency control system. An optimization problem was defined by considering both the owner satisfaction and frequency control performance. The main contribution of the proposed paper, compared to the literature, are (1) to keep the total available energy stored in the batteries connected to a charging station in an optimal region that favors the frequency regulation capability of the station and the proposed QoS and (2) to consider the optimal region bounded by the efficiency thresholds of the charger to allow for maximum regulation power. The problem is expressed as a multi-criteria optimization problem with time-dependent references. The paper presents an energy management strategy for frequency control, describes a concept of an optimal time-dependent state of charge for electric vehicle charging demands, and considers the power dependence of the electric vehicle charger efficiency. Finally, the simulation results are presented via Matlab/Simulink to prove the effectiveness of the proposed algorithm. Full article

The driving range of electric vehicles (EVs) is still an important factor restricting their development. Although the rising battery energy density has reached a bottleneck, which is a key constraint, the drive energy management strategy also has a significant effect and can improve the driving range of EVs, since wheel traction torque control can directly optimize the driving energy consumption of EVs. In order to comprehensively analyze the current research status of driving energy management and clarify its development direction, this review focuses on the driving energy management strategy of EVs and systematically summarizes the configurations and power distribution strategies of the dual-motor coupling drive system (DCDS), as well as torque vectoring control strategies of the decentralized drive system. Firstly, driving energy losses are analyzed in detail, which mainly include electric loss, tire slip energy dissipation, and the power of cornering resistance. Secondly, typical configurations of the DCDS are introduced, and the power distribution strategies of the DCDS are comprehensively reviewed. Finally, as an interesting energy-saving technology, energy-saving torque vectoring, generally applied to decentralized drive systems, is reviewed in detail in terms of its energy-saving pathways and control strategies, which are classified as front-and-rear torque vectoring and left-and-right torque vectoring. Research findings indicate that the driving range of EVs can be effectively increased by applying a driving energy management strategy based on several novel multi-power source drive systems. The development of a driving energy management strategy and the required novel drive systems will be a valuable and crucial direction for further energy conservation in EVs. Full article

Auxiliary diesel heaters are commonly used in all types of vehicles in cold climates and conditions around the world. Electric buses used in public transport utilise diesel-burning auxiliary heaters to provide thermal comfort for passengers under cold weather conditions while maintaining the operational range otherwise reduced by electric heating. However, the downside of utilising diesel burners is that they cause similar exhaust pollutants to conventional diesel vehicles. Because the emission control for auxiliary heaters is lax, the diesel burners typically lack any exhaust aftertreatment (EAT), resulting in potentially high local emissions. As the public transport sectors around the world seem to transit from traditional internal combustion engine-vehicles to battery electric applications, the significance of the emissions caused by diesel auxiliary heaters is continuously increasing. EVs are generally considered zero-emission vehicles but the implementation of diesel burners is evidently conflicting with this concept. Nevertheless, publicly available experimental results from studies around this topic are surprisingly limited. The data of the few available publications are not directly comparable because there is no direct procedure or protocol for determining the exhaust pollutants from auxiliary heaters in real-world conditions at present. As a result, assessing the direct effect of the pollutants caused by electric vehicles utilising auxiliary heaters in the public transport is challenging. This study addresses this problem by introducing two methods for measuring auxiliary heater emissions; first, a field-test method that is applicable for a quick screening of the emissions of multiple heater units; secondly, a laboratory test method for a more detailed emission characterisation in a simulated real-world operation environment. In these experiments, the primarily objective was to study the emissions of the auxiliary heaters, including CO₂, CO, NO_x and soot. The heater operation was found to be cyclic with numerous start-ups during its typical operation. The cyclic operation resulted in concurrent emission peaks in CO and soot. Measurements of actual operation showed auxiliary heater utilisation rates similar to the controlled measurements, although the whole temperature range of the controlled measurements was not reached in real-world conditions. The measurements conducted during the field screening revealed high variations between emissions of individual units. A further screening of auxiliary heaters would provide a better outlook for the mitigation of their emissions. Full article

This paper proposes the electric vehicle (EV)-station-grid coordination optimization strategy considering user preferences, which regulates the charging behaviors of EV users from the user side to ensure the stable and safe operation of the power grid. Firstly, the spatio-temporal prediction model of charging load based on speed-temperature is developed. The model of EV power consumption per unit mileage affected by temperature and EV speed is constructed, and the shortest path algorithm is applied to determine the driving paths of EVs so as to judge the charging demand in combination with the state of charge (SOC) of the battery and to determine the charging periods and locations of the EVs, thus obtaining the spatio-temporal information of the charging load. Secondly, a multi-attribute charging decision model considering user preferences is constructed. Fuzzy clustering and rough set theory are applied to mine user behavior preferences, combined with behavioral economics to describe users’ irrational charging decision-making psychology. Lastly, a real-time charging price model considering voltage fluctuation index and user charging cost is constructed to analyze the impact of price on guiding charging behaviors. The simulation results verify the effectiveness and performance of the collaborative optimization strategy. Full article

This paper presents the status regarding electric vehicles (EVs) and public charging stations managed by Cluj-Napoca City Hall. The types of charging stations and the quantity of the used energy were stated. The behavior of EV owners in charging their vehicles was also studied. The presented work proposes an extension of the EV charging infrastructure to support current and future EV owners. Simulations were carried out in Simulink to create an overview of the existing infrastructure. The simulation results showed the amount of greenhouse gas (CO₂ emissions) reduced by using EVs. An estimated number of kilometers traveled with EVs was calculated by tracking the loads in all public charging stations. Full article

Electric vehicles (EVs) are becoming increasingly popular due to their low emissions, energy efficiency, and reduced reliance on fossil fuels. One of the most critical components in an EV is the energy storage and management system, which requires compactness, lightweight, high efficiency, and superior build quality. Active cell equalization circuits such as those used in battery management systems (BMS) have been developed to balance the voltage and state of charge (SoC) of individual cells, ensuring the safety and reliability of the energy storage system. The use of these types of equalization circuits offers several benefits including improved battery performance, extended battery life, and enhanced safety, which are essential for the successful adoption of EVs. This paper provides a comprehensive overview of the research works related to active cell equalization circuits. This review highlights the important aspects, advantages and disadvantages, and specifications. Full article

The sustainability of a clean energy transition for electric vehicle transportation is clearly affected by increased energy consumption cost, which is associated with large-scale electric vehicles (EVs) charging on a fossil-fuel dependent electricity grid. This places a potential threat on the safe operations and stability of the grid and increases the emissions of greenhouse gases (GHGs) from the power stations that generate the electricity. Furthermore, the uncontrolled large-scale integration of EVs charging on the grid will increase exponentially in the coming years. Because of this, new peaks on the grid will be generated due to the EV charging load variance, and a significant impact on the transformer limit and substation capacity violation will occur. To mitigate the significant impact of the high cost of energy consumption by large-scale EVs charging on the grid, and to reduce the emissions of GHGs, there is a need to provide a multi-level optimization approach that is robust and dynamic to solve the uncontrolled charging problem of large-scale integration of EVs to the grid. This paper investigates the grid energy consumption by EVs and reviews recent applications of EV charging controls and optimization approaches used for the energy management of large-scale EVs charging on the grid. Energy management in this context is not trivial. It implies that the objectives such as load shifting, peak shaving, and minimizing the high cost of electricity consumption with a stable grid operation can be achieved. In the context of this study, EVs charging on the grid includes both battery electric vehicles (BEVs), which have larger battery banks with a longer charging duration and higher energy consumption capacity, and plug-in hybrid electric vehicles (PHEVs) which have smaller battery capacities. Full article

The hybrid electric dump truck is equipped with multiple power sources, and each powertrain component is controlled by an energy management strategy (EMS) to split the demanded power. This study proposes an EMS based on deep reinforcement learning (DRL) algorithm to extend the battery life and reduced total usage cost for the vehicle, namely the twin delayed deep deterministic policy gradient (TD3) based EMS. Firstly, the vehicle model is constructed and the optimization objective function, including battery aging cost and fuel consumption cost, is designed. Secondly, the TD3-based EMS is used for continuous action control of ICE power based on vehicle state, and the action mask is applied to filter out invalid actions. Thirdly, the simulations of the EMSs are trained under the CHTC-D driving cycle and C-WTVC driving cycle. The results show that the action mask improves the convergence efficiency of the strategies, and the proposed TD3-based EMS outperforms the deep deterministic policy gradient (DDPG) based EMS. Meanwhile, the battery life is extended by 36.17% under CHTC-D and 35.49% under C-WTVC, and the total usage cost is reduced by 4.30% and 2.49% when the EMS considers battery aging. In summary, the proposed TD3-based EMS can extend the battery life and reduce usage cost, and provides a method to solve the optimization problem for the EMS of hybrid power systems. Full article

This paper proposes an energy management strategy for a series hybrid electric vehicle based on driving pattern recognition, driving condition prediction, and model predictive control to improve the fuel consumption while maintain the state of charge of the battery. To further improve the computational efficiency, the discretization and linearization of the model is conducted, and the MPC problem is transferred into a quadratic programming problem, which can be solved by the interior point method effectively. The simulation is carried out by using Matlab/Simulink platform, and the simulation results verify the feasibility of the condition prediction method and the performance of the proposed method. In addition, the predictive control strategy successfully improves the fuel economy of the hybrid vehicle compared with the rule-based method. Full article

The urgent need to reduce greenhouse gas emissions to achieve a decarbonized society has led to the active introduction of electric vehicles worldwide. Renewable energy sources that do not emit greenhouse gases during charging must also be used. However, the uncertainty in the supply of renewable energy is an issue that needs to be considered in practical applications. Therefore, in this study, we predicted the amount of electricity generated by renewable energy using model predictive control, and we considered the operation of a complete island-operated park and ride EV parking station that does not depend on commercial electricity. To perform appropriate model predictive control, we performed comparative simulations for several different forecast interval cases. Based on the obtained results, we determined the forecast horizon and we simulated the economic impact of implementing EV demand response on the electricity demand side. We found that without demand response, large amounts of electricity are recharged and a very high return on investment can be achieved, but with demand response, the return on investment is faster. The results provide a rationale for encouraging infrastructure development in areas that have not yet adopted electric vehicles. Full article

Signalized intersections can increase the vehicle stops and consequently increase the energy consumption by forcing stop-and-go dynamics on vehicles. Eco-driving with the help of connectivity is a solution that could avoid multiple stops and improve energy efficiency. In this paper, an eco-driving framework is developed, which finds the energy-efficient speed profile both up- and downstream of a signalized intersection in free-flow situations (eco-FF). The proposed framework utilizes the signal phasing and timing (SPaT) data that are communicated to the vehicle. The energy consumption model used in this framework is a combination of vehicle dynamics and time-dependent auxiliary consumption, which implicitly incorporates the travel time into the function and is validated with real-world test data. It is shown that, by using the proposed eco-FF framework, the vehicle’s energy consumption is notably reduced. Full article

As the performances of energy management strategy (EMS) are essential for a plug-in hybrid electric bus (PHEB) to operate in an efficient way. The proximal policy optimization (PPO) based multi-objective EMS considering the battery thermal characteristic is proposed for PHEB, aiming to improve vehicle energy saving performance while ensuring the battery State of Charge (SOC) and temperature within a rational range. Since these three objectives are contradictory to each other, the optimal tradeoff between multiple objectives is realized by intelligently adjusting the weights in the training process. Compared with original PPO-based EMSs without considering battery thermal dynamics, simulation results demonstrate the effectiveness of the proposed strategies in battery thermal management. Results indicate that the proposed strategies can obtain the minimum energy consumption, fastest computing speed, and lowest battery temperature in comparison with other RL-based EMSs. Regarding dynamic programming (DP) as the benchmark, the PPO-based EMSs can achieve similar fuel economy and outstanding computation efficiency. Furthermore, the adaptability and robustness of the proposed methods are confirmed in UDDS, WVUSUB and real driving cycle. Full article

The most employed technique to mimic the behavior of lithium-ion cells to monitor and control them is the equivalent circuit model (ECM). This modeling tool should be precise enough to ensure the system’s reliability. Two significant parameters that affect the accuracy of the ECM are the applied current rate and operating temperature. Without a thorough understating of the influence of these parameters on the ECM, parameter estimation should be carried out manually within the calibration, which is not favorable. In this work, an enhanced ECM was developed for high-power lithium-ion capacitors (LiC) for a wide temperature range from the freezing temperature of −30 °C to the hot temperature of +60 °C with the applied rates from 10 A to 500 A. In this context, experimental tests were carried out to mimic the behavior of the LiC by modeling an ECM with two RC branches. In these branches, two resistance and capacitance (RC) are required to maintain the precision of the model. The validation results proved that the semi-empirical second-order ECM can estimate the electrical and thermal parameters of the LiC with high accuracy. In this context, when the current rate was less than 150 A, the error of the developed ECM was lower than 3%. Additionally, when the demanded power was high, in current rates above 150 A, the simulation error was lower than 5%. Full article

China has developed a preliminary policy system for the development of new energy vehicles regarding the law, electricity price, grid-connected standards, project management, and financial support, however, defects remain in the policy and market environment, market mechanism, control technology, infrastructure, etc. We analyze new energy vehicles based on the analysis of basic data such as the number of electric vehicles and charging facilities, focusing on industrial development strategies, related subsidies, and tax policies. First, this paper summarizes the development status of China’s new energy vehicles in different scenarios. In 2021, China’s new energy vehicle production was 3545 thousand, and sales amounted to 3521 thousand. According to preliminary estimates, the number of new energy vehicles will exceed 15 million in 2030. The research route for the development of new energy vehicle bottlenecks is proposed. Secondly, we analyze foreign and Chinese policies on different stages and construct the implementation path for the healthy and stable development of China’s new energy vehicles. By comparing the basic indicators, related policies, and related innovation activities of new energy vehicles in China, we conclude that the development of the national electric vehicle industry needs to be increased in terms of government policies, business model innovation, and public awareness. Full article

A battery-swapping station (BSS) can serve as a flexible source in distribution systems, since electric vehicle (EV) batteries can be charged at different time periods prior to their swapping at a BSS. This paper presents an EV battery service transformation from charging to swapping batteries for EVs for the capacity enhancement of a distribution system. A novel mathematical model is proposed to optimally quantify and maximize the flexibility of BSS loads in providing demand response for the utility operator while considering technical operations in the distribution grid. Case studies and numerical findings that consider data from the National Household Travel Survey and a 32-bus distribution system are reported and discussed to demonstrate the effectiveness of the proposed model. Offering battery-swapping services helps reduce not only the peak load, but also the station operation cost. Full article

Nowadays, due to the increasing use of electric vehicles, manufacturers are making more and more innovations in the batteries used in electromobility, in order to make these vehicles more efficient and provide them with greater autonomy. This has led to the need to evaluate and compare the efficiency of different batteries used in electric vehicles to determine which one is the best to be implemented. This paper characterises, models and compares three batteries used in electromobility: lithium-ion, lead-acid, and nickel metal hydride, and determines which of these three is the most efficient based on their state of charge. The main drawback to determine the state of charge is that there are a great variety of methods and models used for this purpose; in this article, the Thévenin model and the Coulomb Count method are used to determine the state of charge of the battery. When obtaining the electrical parameters, the simulation of the same is carried out, which indicates that the most efficient battery is the Lithium-ion battery presenting the best performance of state of charge, reaching 99.05% in the charging scenario, while, in the discharge scenario, it reaches a minimum value of 40.68%; in contrast, the least efficient battery is the lead acid battery, presenting in the charging scenario a maximum value of 98.42%, and in the discharge scenario a minimum value of 10.35%, presenting a deep discharge. This indicates that the lithium-ion battery is the most efficient in both the charge and discharge scenarios, and is the best option for use in electric vehicles. In this paper, it was decided to use the Coulomb ampere counting method together with the Thévenin equivalent circuit model because it was determined that the combination of these two methods to estimate the SOC can be applied to any battery, not only applicable to electric vehicle batteries, but to battery banks, BESS systems, or any system or equipment that has batteries for its operation, while the models based on Kalman, or models based on fuzzy mathematics and neural networks, as they are often used and are applicable only to a specific battery system. Full article