World Electr. Veh. J., Volume 14, Issue 2 (February 2023) – 30 articles

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

In this paper, we address the problem of trajectory tracking control of autonomous vehicles by considering the nonlinear characteristics of tires. By considering the influence of the tires’ dynamics on steering stability, the proposed predictive controller can track the desired trajectory and desired velocity in the presence of road curvature while minimizing the lateral tracking deviation. First of all, a hierarchical control structure is adopted, in which the upper-level controller is used to calculate the desired acceleration and the desired front-wheel angle to maintain the control target, and the lower-level controller realized the command through the corresponding component devices. Moreover, a force estimator is designed based on the radial basis function (RBF) neural network to estimate the lateral force of the tires, which is incorporated into the boundary conditions of the vehicle envelope constraint to improve the adaptability of the controller to the vehicle performance. Finally, the proposed controller is tested by co-simulation of CarSim (a simulation software specifically for vehicle dynamics)/Simulink (a modular diagram environment for multidomain simulation as well as model-based design) and hardware-in-loop simulation system. The co-simulation and experimental results demonstrate the controller safely driving at the vehicle’s handling limits and effectively reduce the slip phenomenon of the vehicle. Full article

A modular multilevel converter (MMC) can generate different common-mode voltage (CMV) values due to the high-frequency changing of the switching state under various modulation strategies. The high-frequency dv/dt will produce common-mode current in the equivalent common-mode loop to the ground, which will affect the insulation and shorten the life of the equipment. To eliminate the effect of common-mode voltage on MMC operation, a common-mode voltage elimination strategy (0CMV-SVPWM) is proposed for five-level MMC space vector pulse width modulation (SVPWM) by using the vector that does not generate common-mode voltage as the reference vector in this paper. The proposed strategy is studied and analyzed by the rapid prototype development experimental system based on RT-LAB to verify the feasibility and effectiveness of the proposed strategy. Full article

Driving at an unsignalized roundabout is a complex traffic scenario that requires both traffic safety and efficiency. At the unsignalized roundabout, the driving policy does not simply maintain a safe distance for all vehicles. Instead, it pays more attention to vehicles that potentially have conflicts with the ego-vehicle, while guessing the intentions of other obstacle vehicles. In this paper, a driving policy based on the Soft actor-critic (SAC) algorithm combined with interval prediction and self-attention mechanism is proposed to achieve safe driving of ego-vehicle at unsignalized roundabouts. The objective of this work is to simulate a roundabout scenario and train the proposed algorithm in a low-dimensional environment, and then test and validate the policy in the CARLA simulator to ensure safety while reducing costs. By using a self-attention network and interval prediction algorithms to enable ego-vehicle to focus on more temporal and spatial features, the risk of driving into and out of the roundabout is predicted, and safe and effective driving decisions are made. Simulation results show that our proposed driving policy can provide collision risk avoidance and improve vehicle driving safety, resulting in a 15% reduction in collisions. Finally, the trained model is transferred to the complete vehicle system of CARLA to validate the possibility of real-world deployment of the policy model. Full article

With the widespread use of electric vehicles (EVs), the charging behavior of these resources has brought a large amount of load growth to the grid, leading to a series of problems such as increased peak valley load difference and line flow violation. Non-intrusive load monitoring (NILM) is a key technology that can be employed to monitor the multi-source load data information in the power grid and support the high-proportion access of electric vehicles. However, traditional NILM approaches are designed to identify the operation of household appliances and cannot be applied at the substation level directly due to frequent and intricate switching events of electrical equipment at this stage. In this paper, a NILM algorithm that can be applied for the monitoring of the charging behavior of electric vehicles at the substation level is proposed to support the high-proportion injection of distributed energy resources. The proposed approach employs a deep learning framework and a multi-kernel convolutional neural network (multi-kernel CNN) framework is used. The performance of the proposed method is verified on the self-organized datasets based on Pecan Street data and results showed that the obtained f1 score is over 90% for both the training sets and testing sets. Full article

In a proton exchange membrane fuel cell (PEMFC) system, the flow of air and hydrogen is the main factor affecting the output characteristics of the PEMFC, and there is a coordination problem in the flow control of both. To ensure real-time gas supply in the fuel cell and improve the output power and economic benefits of the system, a deep reinforcement learning controller with continuous state based on fusion optimization (FO-DDPG) and a control optimization strategy based on net power optimization are proposed in this paper, and the effects of whether the two gas controls are decoupled or not are compared. The experimental results show that the undecoupled FO-DDPG algorithm has a faster dynamic response and more stable static performance compared to the fuzzy PID, DQN, traditional DRL algorithm, and decoupled controllers, demonstrated by a dynamic response time of 0.15 s, an overshoot of less than 5%, and a steady-state error of 0.00003. Full article

Anti-slip control, as a fundamental technique of vehicle stability control, prevents loss of control of vehicles, especially under extreme driving conditions. However, current control methods fail to suppress vehicle slippage when steering. Therefore, a new anti-slip control approach for four-wheel independent-drive electric vehicles (EVs) based on the energy method is proposed. This approach makes full use of the distribution of motor energy between the body and the wheels during vehicle turning, being able to adjust the driving torque of each wheel. Simulation results validate that the proposed approach can prevent wheel slip when the vehicle steers on slippery roads. Furthermore, simulations also show that the proposed control strategy can maintain high control performance when the motor flux linkage varies. Full article

Automated vehicles, predicted to be fully electric in future, are expected to reduce road fatalities and road traffic emissions. The lane departure warning system, an important feature of automated vehicles, utilize lane detection and tracking algorithms. Researchers are constrained to test their lane detection algorithms because of the small publicly available datasets. Additionally, those datasets may not represent differences in road geometries, lane marking and other details unique to a particular geographic location. Existing methods to develop the ground truth datasets are time intensive. To address this gap, this study proposed a framework for an interpolation approach for quickly generating reliable ground truth data. The proposed method leverages the advantage of the existing manual and time-slice approaches. A detailed framework for the interpolation approach is presented and the performance of the approach is compared with the existing methods. Video datasets for performance evaluation were collected in Melbourne, Australia. The results show that the proposed approach outperformed four existing approaches with a reduction in time for generating ground truth data in the range from 4.8% to 87.4%. A reliable and quick method for generating ground truth data, as proposed in this study, will be valuable to researchers as they can use it to test and evaluate their lane detection and tracking algorithms. 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

Breaking the highly oil-dependent energy use structure in the transportation sector will be crucial for China to reduce its dependence on crude oil imports and ensure its energy security, and the development of new energy vehicles is helping to break this dilemma. A time series analysis summarizes the possible relationships between new energy vehicles and crude oil imports, i.e., new energy vehicles, as alternatives to fuel vehicles, will reduce the demand for oil in the transportation sector, which will in turn reduce crude oil imports, and crude oil prices and crude oil production will inhibit crude oil imports. In this empirical study, monthly data from 2015 to 2021 on crude oil imports, the market share of new energy vehicles, crude oil prices, and crude oil production are selected, time-series multiple regression modelling is adopted, and endogeneity is treated using a generalized method of moments (GMM). The regression results show that crude oil imports decrease by one unit for every 16.32% increase in crude oil prices, indicating that price factor is the most influential factor in China’s crude oil imports, while crude oil imports decrease by one unit for every 133.99% increase in crude oil production, indicating that an increase in crude oil production contributes less to the reduction of crude oil imports. One unit of crude oil imports is added for every 15.53% increase in the share of new energy vehicles, indicating that the effect of new energy vehicles on limiting crude oil imports has not yet emerged. Probably due to the fact that new energy vehicles have not yet had a significant impact on fuel vehicles, oil consumption will continue to increase in the short and medium term, with oil for the petrochemical industry becoming the primary driver of this increase. Finally, policy implications are provided from the perspective of crude oil demand, supply, and China’s oil price mechanism. Full article

Ever since the discovery of oil in 1938, the State of Kuwait has increasingly sought out international brands in the car market due to the high purchasing power of Kuwaiti nationals. However, the makers of electric vehicles (EVs) have not been able to penetrate this market, with the exception of innovators and early adopters. The phenomenon in disruptive innovation theory—called “Crossing the Chasm”—regarding a mass market appeal has not yet occurred in Kuwait. Through deep interviews with 12 Kuwaiti owners of EVs and automotive dealers who sold either EVs or Hybrid Electric Vehicles (HEVs), 10 key reasons for this phenomenon have been previously revealed, which were used to develop an extensive questionnaire. A total of 472 car drivers aged from 18 to 30, identified as the “early majority”, completed the questionnaire to achieve the objective of identifying the factors required to create a mass market for EVs in Kuwait. The results demonstrated that potential customers highly preferred three different types of attributes of EVs: environmental, financial, and technological. There were significant differences in the identified attributes preferred by Kuwaiti individuals for EVs in terms of the number of cars owned and the sector of employment. Moreover, the results of our study indicate that potential customers are very willing to buy EVs in the future, considering both their financial and infrastructure attributes. There were further significant differences in the identified necessary conditions to buy EVs in terms of educational level and monthly income. This study discusses a variety of valuable promotional tactics, which may be implemented in conjunction with public incentives and policy changes in the State of Kuwait. This information is considered useful for marketers and designers who wish to tap into this lucrative market, which is significantly different from that in the global North. Full article

Model predictive control (MPC) technology for multi-phase electric drives has received increasing attention in modern industries, especially in electric vehicles, marine electrical propulsion, and wind power generation. However, MPC has several challenges in controlling multi-phase electric drives, including the design of weighting factors, high computational complexity, large harmonic currents, heavy reliance on the system model, fault-tolerant control operation, common-mode voltage, and zero-sequence current hazards. Therefore, this paper gives a comprehensive review of the latest and most effective solutions to the existing major technical challenges and prospects for the future trends of MPC for multi-phase electric drives. Full article

This paper reports the discovery of a series of computational social media accounts (Fanbots) on Twitter that may have played a critical role in sustaining the entrepreneurial narratives of Tesla, the electric-vehicle maker. From 2010 to 2020—a period of trial, error, and eventual success for Tesla—these computational agents generated pro-firm tweets (Corporate Computational Propaganda, CCP), accounting for more than 10% of the total Twitter activity that included the cashtag, $TSLA, and 23% of activity that included the hashtag, #TSLA. Though similar to programmed social media content in the political sphere, the activities of these accounts predate the existence of political computational propaganda associated with foreign support for, for instance, Brexit in the United Kingdom (2016) and Donald Trump in the United States (2016). The paper (a) characterizes the extent of Fanbot content in two large Tesla tweet corpora, (b) identifies possible motivations for the creation of these accounts in relation to the firm’s entrepreneurial narratives, and (c) explores possible mechanisms by which the Fanbots might have acted. Although we are unable to directly observe the source or stated purpose of these accounts, based upon the timing of Fanbot creation and other indirect indicators, we infer that these accounts and the social media activity they generated were intended to influence social perception of Tesla. The conclusion assesses the generalizability of a Fanbot-based strategy, highlighting contextual limitations, while also pointing to ways that firms may already be using CCP to manage social approval in emerging-industry contexts. Full article

In this paper, an economy-oriented car-following control (EOCFC) strategy is proposed for electric vehicles in car-following scenarios. Specifically, a controller based on model predictive control (MPC) is developed to optimize the host vehicle’s speed for better energy economy while ensuring good car-following performance and ride comfort. The vehicle’s energy consumption is accurately quantified in the form of demand power, which is incorporated in the cost function for energy optimization. The proposed EOCFC strategy is evaluated using three standard test cycles, i.e., New European Driving Cycle (NEDC), Urban Dynamometer Driving Schedule (UDDS) and Worldwide Harmonized Light Vehicles Test Cycle (WLTC), in comparison with a typical multi-objective adaptive cruise control strategy. The evaluation results demonstrate that the proposed EOCFC improves the energy economy of the host vehicle by 0.53%, 3.33% and 1.51%, under the NEDC, UDDS and WLTC test cycles respectively. Full article

An intelligent, accurate, and powerful object detection system is required for automated driving systems to keep these vehicles aware of their surrounding objects. Thus, vehicles adapt their speed and operations to avoid crashing with the existing objects and follow the driving rules around the existence of emergency vehicles and installed traffic signs. The objects considered in this work are summarized by regular vehicles, big trucks, emergency vehicles, pedestrians, bicycles, traffic lights, and traffic signs on the roadside. Autonomous vehicles are equipped with high-quality sensors and cameras, LiDAR, radars, and GPS tracking systems that help to detect existing objects, identify them, and determine their exact locations. However, these tools are costly and require regular maintenance. This work aims to develop an intelligent object classification mechanism for autonomous vehicles. The proposed mechanism uses machine learning technology to predict the existence of investigated objects over the road network early. We use different datasets to evaluate the performance of the proposed mechanism. Accuracy, Precision, F1-Score, G-Mean, and Recall are the measures considered in the experiments. Moreover, the proposed object classification mechanism is compared to other selected previous techniques in this field. The results show that grouping the dataset based on their mobility nature before applying the classification task improved the results for most of the algorithms, especially for vehicle detection. Full article

Consumption habits are changing due to the development of new technologies around renewable energy, environmental awareness, and new incentive policies. Smart grids are seen as an effective way to accommodate more renewable energy, achieve better control of demand, and improve the operating conditions of the electrical system. However, electric vehicles, which are an environmentally friendly alternative, have very high market penetration and require efficient electrical management at charging stations. Among the factors that have a significant impact on electrical energy consumption are traffic conditions, which can seriously impact the efficiency of electric vehicles. Therefore, the focus is on developing charging infrastructure and reducing vehicle waiting time by optimally allocating electric vehicles to charging stations. To this end, an optimization approach is presented, based on the traffic conditions collected by the SUMO simulator. This approach enables each vehicle to be assigned to the appropriate station while maintaining its battery state of charge at a higher level. Full article

Environmental perception forms the basis of intelligent driving systems and is a prerequisite for path planning and vehicle control. Among them, dynamic multi-obstacle tracking is the key to environmental perception. In order to solve the problem of a large amount of correlation calculations and false correlations in the process of dynamic multi-obstacle tracking, and to obtain more accurate surrounding environment information, this paper first designs an obstacle data correlation algorithm based on improving the joint probabilistic data-association algorithm. Then, in order to solve the problem of obstacle movement mobility and the poor filtering effect of a single model, the interacting multiple model is designed to complete the filtering of multiple behavior patterns of obstacles. An obstacle state estimation algorithm based on the unscented Kalman filter is designed to solve the nonlinear problem of obstacle motion. Finally, an experimental prototype is built and tested. The results show that the data association algorithm designed in this paper can complete the data association of obstacles at different times, and there is no problem of obstacle loss and association error. The average running time of each frame is 51.63 ms. The result comparison between the proposed method and the traditional method shows that the proposed method is more effective. Full article

With the development of electric vehicles, research on the cooperation of transportation networks (TNs) and power distribution networks (PDNs) has become important. Because of practicability, most cooperation research focuses on user equilibrium assignment based on the Wardrop I principle. There is less research focusing on network cooperation involving the system optimal assignment based on Wardrop II. This research paper constructs a cooperation between dynamic system optimal (DSO) and dynamic optimal power flow (DOPF) assignments with multi-objective optimization. Based on Wardrop II, this DSO model realizes multiple origin–destination pairs, multiple tasks, and multiple vehicle types. Electric vehicle and fast charging station models are designed as the connection between both networks. The optimal result gives three scenarios: TN prior, PDN prior, and a compromise of both. DSO minimized the total travel cost and DOPF minimized the total cost of power generation. Several path choices resulted from the scenarios. Whichever scenario is chosen, an electric vehicle is assigned dispersedly for a certain time period to reduce power loss. The optimal solution is also affected by the charging power in fast charging stations. This research can be applied to logistics transportation under traffic restrictions. It offers a dynamic optimization model for transportation and power operators. Full article

Increasing penetration of electric vehicles brings a set of challenges for the electricity system related to its energy, power and balance adequacy. Research related to this topic often requires estimates of charging demand in various forms to feed various models and simulations. This paper proposes a methodology to simulate charging demand for different driver types in a local energy system in the form of time series of charging sessions. The driver types are extracted from historical charging session data via data mining techniques and then characterized using a kernel density estimation process. The results show that the methodology is able to capture the stochastic nature of the drivers’ charging behavior in time, frequency and energy demand for different types of drivers, while respecting aggregated charging demand. This is essential when studying the energy balance of a local energy system and allows for calculating future demand scenarios by compiling driver population based on number of drivers per driver type. The methodology is then tested on a simulator to assess the benefits of smart charging. Full article

In the last decade, a number of research works in electrified vehicles have been devoted to the analysis of the electric consumption of battery electric vehicles and the evaluation of the main influencing factors. The literature analysis reveals that the electric motor size, efficiency, and driving condition substantially affect the electric energy stored in the vehicle battery. This paper studies the degree of sensitivity of energy consumption to electric motor size and to its efficiency map characteristics. In order to accomplish this task, three electric motors whose parameters are re-scaled to fit the maximum power torque and speed with different efficiency maps are simulated by installing them on two commercially available battery electric vehicles. This allows for isolating the influence of the efficiency map on electricity consumption. The original characteristics of the motors are then used to evaluate the influence on the electricity consumption of both the size and the efficiency characteristics. The results of the simulation revealed that the influences of the efficiency map and the electric motor size can be around 8–10% and 2–11%, respectively. When both factors are taken into account, the overall difference in electricity consumption can be around 10–21%. Full article

The race for road electrification has started, and convincing drivers to switch from fuel-powered vehicles to electric vehicles requires robust Electric Vehicle (EV) charging infrastructure. This article proposes an innovative EV charging demand estimation and segmentation method. First, we estimate the charging demand at a neighborhood granularity using aggregated cellular signaling data. Second, we propose a segmentation model to partition the total charging needs among different charging technology: normal, semi-rapid, and fast charging. The segmentation model, an approach based on the city’s points of interest, is a state-of-the-art method that derives useful trends applicable to city planning. A case study for the city of Brussels is proposed. Our demand estimation results heavily correlate with the government’s predictions under similar assumptions. The segmentation reveals clear city patterns, such as transportation hubs, commercial and industrial zones or residential districts, and stresses the importance of a deployment plan involving all available charging technologies. Full article

As position sensorless control technology can avoid many disadvantages caused by mechanical position sensors, improve the reliability of the motor, reduce costs and other advantages, a large number of researchers have conducted research on compound control technology in order to achieve position sensorless control technology in a wide speed range. In this article, the position sensorless compound control technology of a permanent magnet synchronous motor is reviewed, and the compound control technology of a permanent magnet synchronous motor without a position sensor is elaborated. Finally, the existing problems and development trend of sensorless compound control technology are summarized and prospected. Full article

In order to solve the problem where the traditional electric power steering system (EPS) provides too much assist torque under low load and low adhesion coefficient, which damages the driver’s road feeling and affects driving safety, this paper designs a multi-map EPS control strategy. First, based on the change of steering resistance torque under different front axle loads and adhesion coefficients, EPS power characteristics considering the front axle load and adhesion coefficient were designed. In addition, the BP (Back Propagation, BP) neural network is used to determine steering resistance torque under different front axle loads and adhesion coefficients. Furthermore, the EPS control strategy based on multi-map is proposed. The proposed control strategy is evaluated through the co-simulation of Trucksim and Simulink. Simulation results show that the proposed EPS control strategy gives the vehicle good steering portability, with the handling torque meeting the ideal handling torque for a commercial vehicle. Under light load and low adhesion coefficient conditions, the lateral acceleration and yaw rate with traditional EPS are 0.1674 g and 5.641 deg/s, and with multi-map EPS are 0.1399 g and 4.715 deg/s. Therefore, the vehicle’s handliitung stability is improved. The steering wheel torque gradient is also increased, and the driver’s road feeling is improved. Full article

The vehicle longitudinal control algorithm is the core function of the adaptive cruise control system, whose main task is to convert vehicle acceleration and deceleration requirements into vehicle driving and braking commands so that the vehicle can quickly and accurately track the desired acceleration. Traditional longitudinal control algorithms rely on accurate vehicle dynamic modeling or complex controller parameter calibrations. To overcome those difficulties, a longitudinal control algorithm based on RBF-PID is proposed in this paper. The algorithm uses the RBFNN (radial basis function neural network), which can simply and quickly approximate any complex nonlinear system, to identify the Jacobian information of the vehicle and perform parameter tuning for PID control and achieve vehicle longitudinal control with self-tuning capability. Finally, the algorithm of this paper is verified by the joint simulation of Matlab/Simulink and Carsim. The results show that this algorithm has a better response rate and anti-jamming capability than the traditional PID control and can achieve accurate and rapid tracking of the desired acceleration. Full article

Aiming at the problem of body instability caused by actuator failure in a distributed electric vehicle drive system, a fault-tolerant control strategy of longitudinal and lateral force cooperative reconstruction with active steering control was proposed, and a layered control structure was adopted based on the vehicle model. In the upper controller, the resultant force and torque are calculated according to the vehicle parameter state and MPC algorithm; the lower controller is the cooperative reconfiguration allocation layer, and the minimum tire load rate, longitudinal and lateral force constraints and front wheel angle control are considered. Finally, offline simulation experiments and hardware-in-the-loop experiments are completed to verify the effectiveness and real-time performance of the designed strategy. The results show that the designed strategy can significantly improve the driving stability and safety of the vehicle when the actuator fails. Full article

This research work deals with the problem of controlling a salient-pole permanent-magnet synchronous motor (SP-PMSM) used in hybrid electric vehicles. An adaptive nonlinear controller based on the backstepping technique is developed to meet the following requirements: control of the reference vehicle speed in the presence of load variation and changes in the internal motor parameters while keeping the reliability and stability of the vehicle. The complexity of the control problem lies on the system nonlinearity, instability and the problem of inaccessibility to measure all the internal parameters, such as inertia, friction and load variation. For this issue, an adaptive backstepping regulator is developed to estimate these parameters. On the basis of formal analysis and simulation, as well as test results, it is clearly shown that the designed controller achieves all the goals, namely robustness and reliability of the controller, stability of the system and speed control, considering the uncertainty parameters’ measurements. Full article

A mechatronic inerter can simulate the equivalent mechanical network through the external electrical network and can be used in a wide range of mechanical device design applications. In this paper, we study the use of a mechatronic inerter to enhance vibration isolation in vehicle seat suspensions. Firstly, the vertical and pitch movements of the vehicle’s sprung mass and the vertical vibration of the seat are considered in a half vehicle model. Then, the mechatronic inerter is introduced and the external electrical network is presented. The particle swarm optimization algorithm was used to optimize the seat suspension layout parameters with different transfer function-orders. Numerical simulations under different speeds were performed, and the results show that the application of the used mechatronic inerter’s seat suspension vibration isolation performance outperforms passive suspension. In addition, with an increase in the external electrical network transfer function-order, the seat acceleration and pitch acceleration RMS values will be further reduced. The results of the study will contribute to a new approach to vehicle seat suspension design. Full article

Aiming at the output torque error of a steering gear electric load simulator caused by excess torque and backlash interference, an electric load simulator based on double-motor loading is designed. The double-motor loading mode is adopted in the structure, the mathematical model is established, and the sources of excess torque and backlash interference are analyzed. In the control strategy, firstly, a torque controller is designed as a feedback controller based on the improved error symbol robust integral control method, and then a backlash interference compensator is designed as a feedforward controller based on the drive redundancy strategy. Finally, a dual motor speed synchronization controller is designed based on the improved cross coupling control method to ensure the stable operation of the torque controller and backlash compensator in the dual-motor system. The simulation results show that the compound control method can reduce the tracking error to 1.13%, 4.44% less than the PID control method. The tracking error is only 1.54% in the case of redundant torque, backlash, and different parameters of dual motors. The method proposed in this paper can still output loading torque with high accuracy. Full article

In this study, an energy-saving control strategy based on instantaneous optimization is proposed to improve the energy efficiency of hybrid tractors. Using a parallel diesel–electric hybrid tractor as the research object, the topological and working characteristics were analyzed, and a coupled dynamic model of rotary tillage and tractor plow was constructed. Aiming to minimize the equivalent fuel consumption of the entire machine, the motor and diesel engine torques were taken as the control variables, and the state of charge of the power battery was taken as the state variable. Subsequently, an energy-saving control strategy based on instantaneous optimization is proposed. Finally, a simulation experiment was carried out using MATLAB to verify the effectiveness of the energy-saving control strategy based on instantaneous optimization. Compared with the energy-saving control strategy based on power-following, the results show that energy-saving control strategy based on instantaneous optimization can reasonably control the operating state of the diesel engine and motor. Therefore, the diesel engine and motor work in the high-efficiency area, and effectively reduce the equivalent fuel consumption of the tractor during field operation. Under rotary tillage and plowing conditions, equivalent fuel consumption is reduced by 4.70% and 6.31%, respectively. Full article

To enable the deployment of battery electric vehicles (BEVs) as passenger cars in the private transport sector, suitable charging infrastructure is crucial. In this paper, a methodology for the efficient spatial distribution of charging infrastructure is evaluated by investigating a scenario with a 100% market penetration of BEVs of (around 1.3 million vehicles) in Berlin, Germany. The goal of the evaluated methodology is the development of various charging infrastructure scenarios—including public and private charging—which are suitable to cover the entire charging demand. Therefore, these scenarios are investigated in detail with a focus on the number of public charging points, their spatial distributions, the available charging power, and the necessary capital costs. For the creation of these charging infrastructure scenarios, a placement model is developed. As input, it uses the data of a multi-agent transport simulation (MATSim) scenario of the metropolitan area of Berlin to evaluate and optimize different distributions of charging infrastructure. The model uses a genetic algorithm and the principle of multi-objective optimization. The capital costs of the charging points and the mean detour car drivers must undertake are used as the optimization criteria. Using these criteria, we expect to generate cost-efficient infrastructure solutions that provide high usability at the same time. The main advantage of the method selected is that multiple optimal solutions with different characteristics can be found, and suitable solutions can be selected by subsequently using other criteria. Besides the generated charging scenarios for Berlin, the main goal of this paper is to provide a valid methodology, which is able to use the output data of an agent-based, microscopic transport simulation of an arbitrary city or area (or even real driving data) and calculate different suitable charging infrastructure scenarios regarding the different optimization criteria. This paper shows a possible application of this method and provides suggestions to improve the significance of the results in future works. The optimized charging infrastructure solutions for the Berlin scenario show capital costs of between EUR 624 and 2950 million. Users must cover an additional mean detour of 254 m to 590 m per charging process to reach an available charging point. According to the results, a suitable ratio between the charging points and vehicles is between 11:1 and 5:1. A share of fast charging infrastructure (>50 kW) of less than ten percent seems to be sufficient if it is situated at the main traffic routes and highly frequented places. Full article