Journal Description
World Electric Vehicle Journal
World Electric Vehicle Journal
is the first peer-reviewed, international, scientific journal that comprehensively covers all studies related to battery, hybrid, and fuel cell electric vehicles. The journal is owned by the World Electric Vehicle Association (WEVA) and its members, the European Association for e-Mobility (AVERE), Electric Drive Transportation Association (EDTA), and Electric Vehicle Association of Asia Pacific (EVAAP). It has been published monthly online by MDPI since Volume 9, Issue 1 (2018).
- Open Access— free for readers, with article processing charges (APC) paid by authors or their institutions.
- High Visibility: indexed within Scopus, ESCI (Web of Science), Ei Compendex, and other databases.
- Journal Rank: CiteScore - Q2 (Automotive Engineering)
- Rapid Publication: manuscripts are peer-reviewed and a first decision is provided to authors approximately 14.1 days after submission; acceptance to publication is undertaken in 3.7 days (median values for papers published in this journal in the second half of 2023).
- Recognition of Reviewers: reviewers who provide timely, thorough peer-review reports receive vouchers entitling them to a discount on the APC of their next publication in any MDPI journal, in appreciation of the work done.
Impact Factor:
2.3 (2022)
Latest Articles
Control Strategies, Economic Benefits, and Challenges of Vehicle-to-Grid Applications: Recent Trends Research
World Electr. Veh. J. 2024, 15(5), 190; https://doi.org/10.3390/wevj15050190 (registering DOI) - 28 Apr 2024
Abstract
With the rapid growth in the number of EVs, a huge number of EVs are connected to the power grid for charging, which places a great amount of pressure on the stable operation of the power grid. This paper focuses on the development
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With the rapid growth in the number of EVs, a huge number of EVs are connected to the power grid for charging, which places a great amount of pressure on the stable operation of the power grid. This paper focuses on the development of V2G applications, based on the current research status of V2G technology. Firstly, the standards on V2G applications and some pilot projects involving more representative V2G systems are introduced. Comparing V2G applications with ordered charging and unordered charging, the social and economic benefits of V2G applications are highlighted. Analysis of the social benefits of V2G applications concerns three points: the grid demand response, personalized charging, and the coordination of renewable energy sources. And analysis of the economic benefits of V2G applications is divided into three parties: the grid, the aggregator, and individuals. From the perspective of innovative EVs expanding the application scenarios through V2G technology, V2G applications for commercial EVs, emergency power applications, and vehicle-to-vehicle energy trading are introduced. The current challenges related to V2G applications are presented: users’ willingness to participate in V2G applications, battery loss, charging and discharging tariffs, privacy and security, and power loss. Finally, some research recommendations for the development of V2G applications are given and the current state of research in regard to those recommendations is presented.
Full article
(This article belongs to the Special Issue Electric Vehicles and Smart Grid Interaction)
Open AccessArticle
Short-Term Charging Load Prediction of Electric Vehicles with Dynamic Traffic Information Based on a Support Vector Machine
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Qipei Zhang, Jixiang Lu, Wenteng Kuang, Lin Wu and Zhaohui Wang
World Electr. Veh. J. 2024, 15(5), 189; https://doi.org/10.3390/wevj15050189 (registering DOI) - 28 Apr 2024
Abstract
This study proposes a charging demand forecasting model for electric vehicles (EVs) that takes into consideration the characteristics of EVs with transportation and mobile load. The model utilizes traffic information to evaluate the influence of traffic systems on driving and charging behavior, specifically
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This study proposes a charging demand forecasting model for electric vehicles (EVs) that takes into consideration the characteristics of EVs with transportation and mobile load. The model utilizes traffic information to evaluate the influence of traffic systems on driving and charging behavior, specifically focusing on the characteristics of EVs with transportation and mobile load. Additionally, it evaluates the effect of widespread charging on the distribution network. An urban traffic network model is constructed based on the multi-intersection features, and a traffic network–distribution network interaction model is determined according to the size of the urban road network. Type classification simplifies the charging and discharging characteristics of EVs, enabling efficient aggregation of EVs. The authors have built a singular EV transportation model and an EV charging queue model is established. The EV charging demand is forecasted and then used as an input in the support vector machine (SVM) model. The final projection value for EV charging load is determined by taking into account many influencing elements. Compared to the real load, the proposed method’s feasibility and effectiveness are confirmed.
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(This article belongs to the Special Issue Fast-Charging Station for Electric Vehicles: Challenges and Issues)
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Open AccessArticle
Research on Power Optimization for Energy System of Hydrogen Fuel Cell Wheel-Driven Electric Tractor
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Jingyun Zhang, Buyuan Wang, Junjiang Zhang, Liyou Xu and Kai Zhang
World Electr. Veh. J. 2024, 15(5), 188; https://doi.org/10.3390/wevj15050188 (registering DOI) - 28 Apr 2024
Abstract
Hydrogen fuel cell tractors are emerging as a new power source for tractors. Currently, there is no mature energy management control method available. Existing methods mostly rely on engineers’ experience to determine the output power of the fuel cell and the power battery,
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Hydrogen fuel cell tractors are emerging as a new power source for tractors. Currently, there is no mature energy management control method available. Existing methods mostly rely on engineers’ experience to determine the output power of the fuel cell and the power battery, resulting in relatively low energy utilization efficiency of the energy system. To address the aforementioned problems, a power optimization method for the energy system of hydrogen fuel cell wheel-driven electric tractor was proposed. A dynamic model of tractor ploughing conditions was established based on the system dynamics theory. From this, based on the equivalent hydrogen consumption theory, the charging and discharging of the power battery were equivalent to the fuel consumption of the hydrogen fuel cell, forming an equivalent hydrogen consumption model for the tractor. Using the state of charge (SOC) of the power battery as a constraint, and with the minimum equivalent hydrogen consumption as the objective function, an instantaneously optimized power allocation method based on load demand in the energy system is proposed by using a traversal algorithm. The optimization method was simulated and tested based on the MATLAB simulation platform, and the results showed under ploughing conditions, compared with the rule-based control strategy, the proposed energy system power optimization method optimized the power output of hydrogen fuel cells and power batteries, allowing the energy system to work in a high-efficiency range, reducing the equivalent hydrogen consumption of the tractor by 7.79%, and solving the energy system power distribution problem.
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(This article belongs to the Special Issue New Energy Special Vehicle, Tractor and Agricultural Machinery)
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Open AccessArticle
Protection Coordination Strategy for the Distributed Electric Aircraft Propulsion Systems
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Anil Kumar Reddy Siddavatam, Kaushik Rajashekara, Hao Huang and Fred Wang
World Electr. Veh. J. 2024, 15(5), 187; https://doi.org/10.3390/wevj15050187 (registering DOI) - 28 Apr 2024
Abstract
The current trend in distributed electric aircraft propulsion systems is to utilize the DC bus system at higher voltage levels than conventional aircraft systems. With Boeing and Airbus utilizing the +/−270 V bipolar DC bus system, the research on high-voltage systems is increasing
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The current trend in distributed electric aircraft propulsion systems is to utilize the DC bus system at higher voltage levels than conventional aircraft systems. With Boeing and Airbus utilizing the +/−270 V bipolar DC bus system, the research on high-voltage systems is increasing gradually, with voltage levels ranging from 1 to 10 kV systems or +/−0.5 to +/−5 kV DC bus systems. These voltage levels present considerable challenges to the distributed electric aircraft propulsion systems. In addition to partial discharge effects, there are other challenges, particularly the challenge associated with effectively limiting short-circuit fault currents due to the low cable impedance of the distribution system. The cable impedance is a significant factor that determines the fault current during fault conditions. Due to the low impedance, there is a sharp increase in fault current, necessitating an enhanced protection strategy, which ensures that the system is adequately protected. This paper introduces a coordinated protection strategy specifically designed for distributed electric aircraft propulsion systems to mitigate or prevent short-circuit faults. The proposed algorithm utilizes an I2t-based strategy and the current-limiting-based strategy to protect the system from short-circuit faults and overload conditions. Redundant backup protection is also included in the algorithm in case the circuit breaker fails to operate.
Full article
(This article belongs to the Special Issue Electric and Hybrid Electric Aircraft Propulsion Systems)
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Open AccessArticle
Autonomous Vehicles Perception, Acceptance, and Future Prospects in the GCC: An Analysis Using the UTAUT-Based Model
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Farrukh Hafeez, Abdullahi Abubakar Mas’ud, Saud Al-Shammari, Usman Ullah Sheikh, Mubarak A. Alanazi, Muhammad Hamid and Ameer Azhar
World Electr. Veh. J. 2024, 15(5), 186; https://doi.org/10.3390/wevj15050186 (registering DOI) - 28 Apr 2024
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The emergence of Autonomous Vehicles (AVs) marks a significant advancement in the automotive industry, transitioning from driver-assistance technologies to fully autonomous systems. This change is particularly impactful in the Gulf Cooperation Council (GCC) region, which is a significant automotive market and technological hub.
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The emergence of Autonomous Vehicles (AVs) marks a significant advancement in the automotive industry, transitioning from driver-assistance technologies to fully autonomous systems. This change is particularly impactful in the Gulf Cooperation Council (GCC) region, which is a significant automotive market and technological hub. However, the adoption of AVs in the GCC faces unique challenges due to the influence of cultural norms and geographical characteristics. Our research utilizes a customized framework of the Unified Theory of Acceptance and Use of Technology (UTAUT), which is adapted to include cultural and geographical factors. This approach fills a gap in the existing literature by identifying and analyzing the key factors affecting the adoption of AVs in the GCC. Our findings indicate a difference in the receptiveness towards AVs among different demographics. Younger participants displayed a more favorable attitude towards AVs compared to older individuals. Additionally, gender and educational attainment play significant roles in the acceptance of AVs. Specifically, our results suggest that there are variations in acceptance rates among genders and individuals with varying levels of education. The United Arab Emirates (UAE) has a relatively high acceptance rate of AVs due to its advanced infrastructure and openness to technological innovations. Our study identifies facilitating conditions and performance expectancy as crucial determinants of intention to use AVs in the GCC. It emphasizes the importance of infrastructure readiness and the perceived advantages of AVs in promoting their adoption.
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Open AccessArticle
Research on Optimization of Intelligent Driving Vehicle Path Tracking Control Strategy Based on Backpropagation Neural Network
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Qingling Cai, Xudong Qu, Yun Wang, Dapai Shi, Fulin Chu and Jiaheng Wang
World Electr. Veh. J. 2024, 15(5), 185; https://doi.org/10.3390/wevj15050185 (registering DOI) - 27 Apr 2024
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To enhance path tracking precision in intelligent vehicles, this study proposes a lateral–longitudinal control strategy optimized with a Backpropagation (BP) neural network. The strategy employs the BP neural network to dynamically adjust prediction and control time-domain parameters within an established Model Predictive Control
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To enhance path tracking precision in intelligent vehicles, this study proposes a lateral–longitudinal control strategy optimized with a Backpropagation (BP) neural network. The strategy employs the BP neural network to dynamically adjust prediction and control time-domain parameters within an established Model Predictive Control (MPC) framework, effectively computing real-time front-wheel steering angles for lateral control. Simultaneously, it integrates an incremental Proportional–Integral–Derivative (PID) approach with a meticulously designed acceleration–deceleration strategy for accurate and stable longitudinal speed tracking. The strategy’s efficiency and superior performance are validated through a comprehensive CarSim(2020)/Simulink(2020b) simulation, demonstrating that the proposed controller adeptly modulates control parameters to adapt to various road adhesion coefficients and vehicle speeds. This adaptability significantly improves tracking and driving dynamics, thereby enhancing accuracy, safety, stability, and real-time responsiveness in the intelligent vehicle tracking control system.
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Open AccessArticle
Fractional-Order PIλDµ Control to Enhance the Driving Smoothness of Active Vehicle Suspension in Electric Vehicles
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Zongjun Yin, Ru Wang, Xuegang Ma and Rong Su
World Electr. Veh. J. 2024, 15(5), 184; https://doi.org/10.3390/wevj15050184 - 26 Apr 2024
Abstract
The suspension system is a crucial part of an electric vehicle, which directly affects its handling performance, driving comfort, and driving safety. The dynamics of the 8-DoF full-vehicle suspension with seat active control are established based on rigid-body dynamics, and the time-domain stochastic
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The suspension system is a crucial part of an electric vehicle, which directly affects its handling performance, driving comfort, and driving safety. The dynamics of the 8-DoF full-vehicle suspension with seat active control are established based on rigid-body dynamics, and the time-domain stochastic excitation model of four tires is constructed by the filtered white noise method. The suspension dynamics model and road surface model are constructed on the Matlab/Simulink simulation software platform, and the simulation study of the dynamic characteristics of active suspension based on the fractional-order PIλDµ control strategy is carried out. The three performance indicators of acceleration, suspension dynamic deflection, and tire dynamic displacement are selected to construct the fitness function of the genetic algorithm, and the structural parameters of the fractional-order PIlDm controller are optimized using the genetic algorithm. The control effect of the optimized fractional-order PIlDm controller based on the genetic algorithm is analyzed by comparing the integer-order PID control suspension and passive suspension. The simulation results show that for optimized fractional-order PID control suspension, compared with passive suspension, the average optimization of the root mean square (RMS) of acceleration under random road conditions reaches over 25%, the average optimization of suspension dynamic deflection exceeds 30%, and the average optimization of tire dynamic displacement is 5%. However, compared to the integer-order PID control suspension, the average optimization of the root mean square (RMS) of acceleration under random road conditions decreased by 5%, the average optimization of suspension dynamic deflection increased by 3%, and the average optimization of tire dynamic displacement increased by 2%.
Full article
(This article belongs to the Special Issue Design, Modelling and Control Strategies for Hybrid and Electric Vehicles)
Open AccessArticle
Life Cycle Cost Assessment of Electric, Hybrid, and Conventional Vehicles in Bangladesh: A Comparative Analysis
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Md. Sarowar Khaled, Abdalla M. Abdalla, Pg Emeroylariffion Abas, Juntakan Taweekun, Md. Sumon Reza and Abul K. Azad
World Electr. Veh. J. 2024, 15(5), 183; https://doi.org/10.3390/wevj15050183 - 26 Apr 2024
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The automobile industry is shifting from internal combustion engine vehicles (ICEVs) to hybrid electric vehicles (HEVs) or electric vehicles (EVs) extremely fast. Our calculation regarding the most popular private car brand in Bangladesh, Toyota, shows that the life cycle cost (LCC) of a
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The automobile industry is shifting from internal combustion engine vehicles (ICEVs) to hybrid electric vehicles (HEVs) or electric vehicles (EVs) extremely fast. Our calculation regarding the most popular private car brand in Bangladesh, Toyota, shows that the life cycle cost (LCC) of a Toyota BZ3 (EV), USD 43,409, is more expensive than a Toyota Aqua (HEV) and Toyota Prius (HEV), but cheaper than a Toyota Axio (ICEV) and Toyota Allion (ICEV). It has been found that about a 25% reduction in the acquisition cost of a Toyota BZ3 would lower its LCC to below others. EVs can be a good choice for those who travel a lot. Changes in electricity prices have little effect upon the LCC of EVs. With the expected decline in the annual price for batteries, which is between 6 and 9%, and the improvement of their capacities, EVs will be more competitive with other vehicles by 2030 or even earlier. EVs will dominate the market since demand for alternative fuel-powered vehicles is growing due to their environmental and economic advantages.
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Open AccessArticle
Efficiency Analysis of Electric Vehicles with AMT and Dual-Motor Systems
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Zhenghong Wang, Xudong Qu, Qingling Cai, Fulin Chu, Jiaheng Wang and Dapai Shi
World Electr. Veh. J. 2024, 15(5), 182; https://doi.org/10.3390/wevj15050182 - 24 Apr 2024
Abstract
With the rapid development of automobiles, energy shortages and environmental pollution have become a growing concern. In order to decrease the energy consumption of electric vehicles (EVs), this study aims to improve EV efficiency with AMT and dual-motor systems. Firstly, the paper establishes
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With the rapid development of automobiles, energy shortages and environmental pollution have become a growing concern. In order to decrease the energy consumption of electric vehicles (EVs), this study aims to improve EV efficiency with AMT and dual-motor systems. Firstly, the paper establishes an Automated Manual Transmission (AMT) model for EVs, which is then simulated using MATLAB R2022a software. In order to eliminate the impact of gear ratio selection, the genetic algorithm is used to optimize the AMT gear ratios. Meanwhile, a dual-motor EV model is constructed, and three different torque distribution schemes are simulated and analyzed. The results indicate that due to the elongation of the energy transmission chain in AMT-equipped EVs, energy losses increase, leading to some improvement in optimized power consumption. However, these EVs remain inferior to those with only a single-stage main reducer. The study also found that the torque distribution based on optimal efficiency further improves results.
Full article
Open AccessArticle
Charging Station Site Selection Optimization for Electric Logistics Vehicles, Taking into Account Time-Window and Load Constraints
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Li Cai, Junting Li, Haitao Zhu, Chenxi Yang, Juan Yan, Qingshan Xu and Xiaojiang Zou
World Electr. Veh. J. 2024, 15(5), 181; https://doi.org/10.3390/wevj15050181 - 24 Apr 2024
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In order to improve the efficiency of the “last-mile” distribution in urban logistics and solve the problem of the difficult charging of electric logistics vehicles (ELVs), this paper proposes a charging station location optimization scheme for ELVs that takes into account time-window and
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In order to improve the efficiency of the “last-mile” distribution in urban logistics and solve the problem of the difficult charging of electric logistics vehicles (ELVs), this paper proposes a charging station location optimization scheme for ELVs that takes into account time-window and load constraints (TW-LCs). Taking the optimal transportation path as the objective function and considering the time-window and vehicle load constraints, a charging station siting model was established. For the TW-LC problem, an improved genetic algorithm combining the farthest-insertion heuristic idea and local search operation was designed. Three different types of standardized arithmetic examples, C type, R type, and RC type, were used to test the proposed algorithm and compare it with the traditional genetic algorithm. The results indicate that, under the same conditions, compared to the traditional genetic algorithm, the improved genetic algorithm reduced the optimal path length by an average of 11.12%. It also decreased the number of charging stations selected, the number of vehicles in use, and the algorithm complexity by 22.97%, 13.71%, and 46.81%. Building on this, a case study was conducted on the TW-LC problem in a specific area of Chongqing, China. It resulted in a 50% reduction in the number of charging stations and a 25% reduction in the number of vehicles selected. In terms of economic indicators, the proposed algorithm improves unit electricity sales by 73.88% and reduces the total annualized cost of the logistics company by 18.81%, providing a theoretical basis for the subsequent promotion of ELVs.
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Open AccessArticle
A Collision Avoidance Strategy Based on Entropy-Increasing Risk Perception in a Vehicle–Pedestrian-Integrated Reaction Space
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Yongming Ding, Weiwei Zhang, Xuncheng Wu, Jiejie Xu and Jun Gong
World Electr. Veh. J. 2024, 15(5), 180; https://doi.org/10.3390/wevj15050180 - 24 Apr 2024
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Ensuring pedestrian safety is one of the most significant challenges for autonomous driving systems in urban scenarios due to the non-cooperative and unpredictable nature of pedestrian movements. To tackle this problem, firstly, we propose a collision avoidance strategy based on entropy-increasing risk perception
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Ensuring pedestrian safety is one of the most significant challenges for autonomous driving systems in urban scenarios due to the non-cooperative and unpredictable nature of pedestrian movements. To tackle this problem, firstly, we propose a collision avoidance strategy based on entropy-increasing risk perception in a vehicle–pedestrian reaction space. Our approach combines a limited range of reaction space regions with entropy to quantify the risk of pedestrian–vehicle collision. Then, multi-vehicle candidate trajectories are generated using the path and speed sequence method, and the uncertain states of pedestrians are predicted based on the social force model and Markov model accordingly. Finally, to determine the optimal collision avoidance trajectory, we use quantitative reaction-space entropy as a new “cost function” to measure potential risk and perform multi-objective trajectory optimization based on the elitist non-dominated-sorting genetic algorithm region-focused (NSGA-RF) approach. Simulation results show that our proposed strategy can enhance the safety of the planned trajectory interaction between vehicles and pedestrians for autonomous driving under normal and emergency conditions.
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Open AccessArticle
Study of Resistance Extraction Methods for Proton Exchange Membrane Fuel Cells Based on Static Resistance Correction
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Yuzheng Mao, Yongping Hou, Rongxin Gu, Dong Hao and Qirui Yang
World Electr. Veh. J. 2024, 15(5), 179; https://doi.org/10.3390/wevj15050179 - 24 Apr 2024
Abstract
Accurate extraction of polarization resistance is crucial in the application of proton exchange membrane fuel cells. It is generally assumed that the steady-state resistance obtained from the polarization curve model is equivalent to the AC impedance obtained from the electrochemical impedance spectroscopy (EIS)
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Accurate extraction of polarization resistance is crucial in the application of proton exchange membrane fuel cells. It is generally assumed that the steady-state resistance obtained from the polarization curve model is equivalent to the AC impedance obtained from the electrochemical impedance spectroscopy (EIS) when the frequency approaches zero. However, due to the low-frequency stability and nonlinearity issues of the EIS method, this dynamic process leads to an additional rise in polarization resistance compared to the steady-state method. In this paper, a semi-empirical model and equivalent circuit models are developed to extract the steady-state and dynamic polarization resistances, respectively, while a static internal resistance correction method is proposed to represent the systematic error between the two. With the correction, the root mean square error of the steady-state resistance relative to the dynamic polarization resistance decreases from 26.12% to 7.42%, indicating that the weighted sum of the static internal resistance and the steady-state resistance can better correspond to the dynamic polarization resistance. The correction method can also simplify the EIS procedure by directly generating an estimate of the dynamic polarization resistance in the full current interval.
Full article
(This article belongs to the Special Issue Revolutionizing the Automotive Landscape: Fuel Cell Applications Powering the Future)
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Open AccessArticle
Research on Yaw Stability Control of Front-Wheel Dual-Motor-Driven Driverless Formula Racing Car
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Boju Liu, Gang Li, Hongfei Bai, Shuang Wang and Xing Zhang
World Electr. Veh. J. 2024, 15(5), 178; https://doi.org/10.3390/wevj15050178 - 24 Apr 2024
Abstract
In order to improve the yaw stability of a front-wheel dual-motor-driven driverless vehicle, a yaw stability control strategy is proposed for a front-wheel dual-motor-driven formula student driverless racing car. A hierarchical control structure is adopted to design the upper torque distributor based on
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In order to improve the yaw stability of a front-wheel dual-motor-driven driverless vehicle, a yaw stability control strategy is proposed for a front-wheel dual-motor-driven formula student driverless racing car. A hierarchical control structure is adopted to design the upper torque distributor based on the integral sliding mode theory, which establishes a linear two-degree-of-freedom model of the racing car to calculate the expected yaw angular velocity and the expected side slip angle and calculates the additional yaw moments of the two front wheels. The lower layer is the torque distributor, which optimally distributes the additional moments to the motors of the two front wheels based on torque optimization objectives and torque distribution rules. Two typical test conditions were selected to carry out simulation experiments. The results show that the driverless formula racing car can track the expected yaw angular velocity and the expected side slip angle better after adding the yaw stability controller designed in this paper, effectively improving driving stability.
Full article
(This article belongs to the Special Issue Design, Modelling and Control Strategies for Hybrid and Electric Vehicles)
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Open AccessArticle
A Lithium-Ion Battery Remaining Useful Life Prediction Model Based on CEEMDAN Data Preprocessing and HSSA-LSTM-TCN
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Shaoming Qiu, Bo Zhang, Yana Lv, Jie Zhang and Chao Zhang
World Electr. Veh. J. 2024, 15(5), 177; https://doi.org/10.3390/wevj15050177 - 24 Apr 2024
Abstract
Accurate prediction of the Remaining Useful Life (RUL) of lithium-ion batteries is crucial for reducing battery usage risks and ensuring the safe operation of systems. Addressing the impact of noise and capacity regeneration-induced nonlinear features on RUL prediction accuracy, this paper proposes a
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Accurate prediction of the Remaining Useful Life (RUL) of lithium-ion batteries is crucial for reducing battery usage risks and ensuring the safe operation of systems. Addressing the impact of noise and capacity regeneration-induced nonlinear features on RUL prediction accuracy, this paper proposes a predictive model based on Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) data preprocessing and IHSSA-LSTM-TCN. Firstly, CEEMDAN is used to decompose lithium-ion battery capacity data into high-frequency and low-frequency components. Subsequently, for the high-frequency component, a Temporal Convolutional Network (TCN) prediction model is employed. For the low-frequency component, an Improved Sparrow Search Algorithm (IHSSA) is utilized, which incorporates iterative chaotic mapping and a variable spiral coefficient to optimize the hyperparameters of Long Short-Term Memory (LSTM). The IHSSA-LSTM prediction model is obtained and used for prediction. Finally, the predicted values of the sub-models are combined to obtain the final RUL result. The proposed model is validated using the publicly available NASA dataset and CALCE dataset. The results demonstrate that this model outperforms other models, indicating good predictive performance and robustness.
Full article
(This article belongs to the Special Issue Lithium-Ion Batteries for Electric Vehicle)
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Open AccessArticle
PortLaneNet: A Scene-Aware Model for Robust Lane Detection in Container Terminal Environments
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Haixiong Ye, Zhichao Kang, Yue Zhou, Chenhe Zhang, Wei Wang and Xiliang Zhang
World Electr. Veh. J. 2024, 15(5), 176; https://doi.org/10.3390/wevj15050176 - 23 Apr 2024
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In this paper, we introduce PortLaneNet, an optimized lane detection model specifically designed for the unique challenges of enclosed container terminal environments. Unlike conventional lane detection scenarios, this model addresses complexities such as intricate ground markings, tire crane lane lines, and various types
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In this paper, we introduce PortLaneNet, an optimized lane detection model specifically designed for the unique challenges of enclosed container terminal environments. Unlike conventional lane detection scenarios, this model addresses complexities such as intricate ground markings, tire crane lane lines, and various types of regional lines that significantly complicate detection tasks. Our approach includes the novel Scene Prior Perception Module, which leverages pre-training to provide essential prior information for more accurate lane detection. This module capitalizes on the enclosed nature of container terminals, where images from similar area scenes offer effective prior knowledge to enhance detection accuracy. Additionally, our model significantly improves understanding by integrating both high- and low-level image features through attention mechanisms, focusing on the critical components of lane detection. Through rigorous experimentation, PortLaneNet has demonstrated superior performance in port environments, outperforming traditional lane detection methods. The results confirm the effectiveness and superiority of our model in addressing the complex challenges of lane detection in such specific settings. Our work provides a valuable reference for solving lane detection issues in specialized environments and proposes new ideas and directions for future research.
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Open AccessArticle
Bill It Right: Evaluating Public Charging Station Usage Behavior under the Presence of Different Pricing Policies
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Markus Fischer, Wibke Michalk, Cornelius Hardt and Klaus Bogenberger
World Electr. Veh. J. 2024, 15(4), 175; https://doi.org/10.3390/wevj15040175 - 22 Apr 2024
Abstract
This study investigates for the first time how public charging infrastructure usage differs under the presence of diverse pricing models. About 3 million charging events from different European countries were classified according to five different pricing models (cost-free, flat-rate, time-based, energy-based, and mixed)
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This study investigates for the first time how public charging infrastructure usage differs under the presence of diverse pricing models. About 3 million charging events from different European countries were classified according to five different pricing models (cost-free, flat-rate, time-based, energy-based, and mixed) and evaluated using various performance indicators such as connection duration; transferred energy volumes; average power; achievable revenue; and the share of charging and idle time for AC, DC, and HPC charging infrastructure. The study results show that the performance indicators differed for the classified pricing models. In addition to the quantitative comparison of the performance indicators, a Kruskal–Wallis one-way analysis of variance and a pairwise comparison using the Mann–Whitney-U test were used to show that the data distributions of the defined pricing models were statistically significantly different. The results are discussed from various perspectives on the efficient design of public charging infrastructure. The results show that time-based pricing models can improve the availability of public charging infrastructure, as the connection duration per charging event can be roughly halved compared to other pricing models. Flat-rate pricing models and AC charging infrastructure can support the temporal shift of charging events, such as shifting demand peaks, as charging events usually have several hours of idle time per charging process. By quantifying various performance indicators for different charging technologies and pricing models, the study is relevant for stakeholders involved in the development and operation of public charging infrastructure.
Full article
(This article belongs to the Special Issue Smart Charging Strategies for Plug-In Electric Vehicles)
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Open AccessArticle
End-to-End Differentiable Physics Temperature Estimation for Permanent Magnet Synchronous Motor
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Pengyuan Wang, Xinjian Wang and Yunpeng Wang
World Electr. Veh. J. 2024, 15(4), 174; https://doi.org/10.3390/wevj15040174 - 21 Apr 2024
Abstract
Differentiable physics is an approach that effectively combines physical models with deep learning, providing valuable information about physical systems during the training process of neural networks. This integration enhances the generalization ability and ensures better consistency with physical principles. In this work, we
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Differentiable physics is an approach that effectively combines physical models with deep learning, providing valuable information about physical systems during the training process of neural networks. This integration enhances the generalization ability and ensures better consistency with physical principles. In this work, we propose a framework for estimating the temperature of a permanent magnet synchronous motor by combining neural networks with the differentiable physical thermal model, as well as utilizing the simulation results. In detail, we first implement a differentiable thermal model based on a lumped parameter thermal network within an automatic differentiation framework. Subsequently, we add a neural network to predict thermal resistances, capacitances, and losses in real time and utilize the thermal parameters’ optimized empirical values as the initial output values of the network to improve the accuracy and robustness of the final temperature estimation. We validate the conceivable advantages of the proposed method through extensive experiments based on both synthetic data and real-world data and then provide some further potential applications.
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(This article belongs to the Special Issue Deep Learning Applications for Electric Vehicles)
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Open AccessArticle
Deep Reinforcement Learning Lane-Changing Decision Algorithm for Intelligent Vehicles Combining LSTM Trajectory Prediction
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Zhengcai Yang, Zhengjun Wu, Yilin Wang and Haoran Wu
World Electr. Veh. J. 2024, 15(4), 173; https://doi.org/10.3390/wevj15040173 - 21 Apr 2024
Abstract
Intelligent decisions for autonomous lane-changing in vehicles have consistently been a focal point of research in the industry. Traditional lane-changing algorithms, which rely on predefined rules, are ill-suited for the complexities and variabilities of real-world road conditions. In this study, we propose an
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Intelligent decisions for autonomous lane-changing in vehicles have consistently been a focal point of research in the industry. Traditional lane-changing algorithms, which rely on predefined rules, are ill-suited for the complexities and variabilities of real-world road conditions. In this study, we propose an algorithm that leverages the deep deterministic policy gradient (DDPG) reinforcement learning, integrated with a long short-term memory (LSTM) trajectory prediction model, termed as LSTM-DDPG. In the proposed LSTM-DDPG model, the LSTM state module transforms the observed values from the observation module into a state representation, which then serves as a direct input to the DDPG actor network. Meanwhile, the LSTM prediction module translates the historical trajectory coordinates of nearby vehicles into a word-embedding vector via a fully connected layer, thus providing predicted trajectory information for surrounding vehicles. This integrated LSTM approach considers the potential influence of nearby vehicles on the lane-changing decisions of the subject vehicle. Furthermore, our study emphasizes the safety, efficiency, and comfort of the lane-changing process. Accordingly, we designed a reward and penalty function for the LSTM-DDPG algorithm and determined the optimal network structure parameters. The algorithm was then tested on a simulation platform built with MATLAB/Simulink. Our findings indicate that the LSTM-DDPG model offers a more realistic representation of traffic scenarios involving vehicle interactions. When compared to the traditional DDPG algorithm, the LSTM-DDPG achieved a 7.4% increase in average single-step rewards after normalization, underscoring its superior performance in enhancing lane-changing safety and efficiency. This research provides new ideas for advanced lane-changing decisions in autonomous vehicles.
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(This article belongs to the Special Issue Motion Planning and Control of Autonomous Vehicles)
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Open AccessArticle
Optimal Allocation of Fast Charging Stations on Real Power Transmission Network with Penetration of Renewable Energy Plant
by
Sami M. Alshareef and Ahmed Fathy
World Electr. Veh. J. 2024, 15(4), 172; https://doi.org/10.3390/wevj15040172 - 20 Apr 2024
Abstract
Because of their stochastic nature, the high penetration of electric vehicles (EVs) places demands on the power system that may strain network reliability. Along with increasing network voltage deviations, this can also lower the quality of the power provided. By placing EV fast
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Because of their stochastic nature, the high penetration of electric vehicles (EVs) places demands on the power system that may strain network reliability. Along with increasing network voltage deviations, this can also lower the quality of the power provided. By placing EV fast charging stations (FCSs) in strategic grid locations, this issue can be resolved. Thus, this work suggests a new methodology incorporating an effective and straightforward Red-Tailed Hawk Algorithm (RTH) to identify the optimal locations and capacities for FCSs in a real Aljouf Transmission Network located in northern Saudi Arabia. Using a fitness function, this work’s objective is to minimize voltage violations over a 24 h period. The merits of the suggested RTH are its high convergence rate and ability to eschew local solutions. The results obtained via the suggested RTH are contrasted with those of other approaches such as the use of a Kepler optimization algorithm (KOA), gold rush optimizer (GRO), grey wolf optimizer (GWO), and spider wasp optimizer (SWO). Annual substation demand, solar irradiance, and photovoltaic (PV) temperature datasets are utilized in this study to describe the demand as well as the generation profiles in the proposed real network. A principal component analysis (PCA) is employed to reduce the complexity of each dataset and to prepare them for the k-means algorithm. Then, k-means clustering is used to partition each dataset into k distinct clusters evaluated using internal and external validity indices. The values of these indices are weighted to select the best number of clusters. Moreover, a Monte Carlo simulation (MCS) is applied to probabilistically determine the daily profile of each data set. According to the obtained results, the proposed RTH outperformed the others, achieving the lowest fitness value of 0.134346 pu, while the GRO came in second place with a voltage deviation of 0.135646 pu. Conversely, the KOA was the worst method, achieving a fitness value of 0.148358 pu. The outcomes attained validate the suggested approach’s competency in integrating FCSs into a real transmission grid by selecting their best locations and sizes.
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(This article belongs to the Special Issue Sustainable EV Rapid Charging, Challenges, and Development)
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Leveraging 5G Technology to Investigate Energy Consumption and CPU Load at the Edge in Vehicular Networks
by
Salah Eddine Merzougui, Xhulio Limani, Andreas Gavrielides, Claudio Enrico Palazzi and Johann Marquez-Barja
World Electr. Veh. J. 2024, 15(4), 171; https://doi.org/10.3390/wevj15040171 - 19 Apr 2024
Abstract
The convergence of vehicular communications, 5th generation mobile network (5G) technology, and edge computing marks a paradigm shift in intelligent transportation. Vehicular communication systems, including Vehicle-to-Vehicle and Vehicle-to-Infrastructure, are integral to Intelligent Transportation Systems. The advent of 5G enhances connectivity, while edge computing
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The convergence of vehicular communications, 5th generation mobile network (5G) technology, and edge computing marks a paradigm shift in intelligent transportation. Vehicular communication systems, including Vehicle-to-Vehicle and Vehicle-to-Infrastructure, are integral to Intelligent Transportation Systems. The advent of 5G enhances connectivity, while edge computing brings computational processes closer to data sources. This synergy holds the potential to revolutionize transportation efficiency and safety. This research investigates vehicular communication and edge computing dynamics within a 5G network, considering varying distances between On Board Units and Roadside Units. Energy consumption patterns and CPU load at the RSU are analyzed through meticulous real-world experiments and simulations. Our results show stable energy consumption at shorter distances, with fluctuations increasing at greater ranges. CPU load correlates with communication distance, highlighting the need for adaptive algorithms. While experiments exhibit higher variability, our simulations validate these findings, emphasizing the importance of considering transmission range in vehicular communication network design.
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(This article belongs to the Special Issue Autonomous Electric Vehicles Combined with Non-connected Vehicles in Smart Cities)
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