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: JCR - Q2 (Transportation Science and Technology) / CiteScore - Q2 (Automotive Engineering)
- Rapid Publication: manuscripts are peer-reviewed and a first decision is provided to authors approximately 15.7 days after submission; acceptance to publication is undertaken in 3.6 days (median values for papers published in this journal in the first half of 2024).
- 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:
2.6 (2023)
Latest Articles
Green Technology Innovation Premium: Evidence from New Energy Vehicle Industry in China
World Electr. Veh. J. 2024, 15(8), 336; https://doi.org/10.3390/wevj15080336 (registering DOI) - 26 Jul 2024
Abstract
Climate change and environmental issues have received increasing attention across the world. China’s governmental targets for carbon peak and carbon neutralization show the ambition and efforts necessary in challenging these problems. The transportation industry will be crucial in reducing carbon emissions. Based on
[...] Read more.
Climate change and environmental issues have received increasing attention across the world. China’s governmental targets for carbon peak and carbon neutralization show the ambition and efforts necessary in challenging these problems. The transportation industry will be crucial in reducing carbon emissions. Based on the green patent application data in China’s new energy vehicle (NEV) industry from 2006 to 2021, this article focuses on risk premium of green technology innovation. In particular, the premium effects of the green technology innovation and the cooperative network are empirically examined. Furthermore, two channels that play a role in generating the premium are investigated, i.e., attracting market attention and reducing financing constraints. The empirical results show that the stock returns are positively correlated to the green technology innovation and the company’s central position in the cooperative network, i.e., there exist the premium effects of green technology innovation in China’s NEV industry. The positional advantage in the cooperative innovation network can further increase analyst following and reduce financing constraints. The research can provide evidence and policy implications for the government, companies and investors.
Full article
(This article belongs to the Special Issue Electric Vehicle Technology Development, Energy and Environmental Implications, and Decarbonization)
Open AccessArticle
Vibration Performance Analysis of a Yokeless Stator Axial Flux PM Motor with Distributed Winding for Electric Vehicle Application
by
Xue Yu, Qin Wang, Yu Fu, Hao Chen, Jianfu Zhang and Weiwei Geng
World Electr. Veh. J. 2024, 15(8), 335; https://doi.org/10.3390/wevj15080335 - 26 Jul 2024
Abstract
This article presents a detailed analysis of the electromagnetic force and vibration behavior of a new axial flux permanent magnet (AFPM) machine with a yokeless stator and interior PM rotor. Firstly, the configuration of an AFPM machine with a dual rotor and a
[...] Read more.
This article presents a detailed analysis of the electromagnetic force and vibration behavior of a new axial flux permanent magnet (AFPM) machine with a yokeless stator and interior PM rotor. Firstly, the configuration of an AFPM machine with a dual rotor and a sandwiched stator is introduced, including the structural design, fixation of the yokeless stator and segmented skew rotor structure. Then, the influence of anisotropic material and a fixed structure on stator modes is analyzed, including elastic modulus, shear model, the skew angle of slot and the thickness of stator yoke. Furthermore, a new non-equally segmented skew rotor structure is proposed and calculated for the reduction in vibration based on the multiphysics model. Three different segmented skew rotor schemes are compared to illustrate the influence of reducing vibration and noise. The predicted results show that the effect of the non-equally segmented skew rotor on reducing vibration is better than the other two schemes. Finally, a 120 kW AFPM motor is experimented with and the result matches well with the predicted data. The vibration performance of the AFPM motor with a dual rotor and sandwiched yokeless stator is revealed comprehensively.
Full article
(This article belongs to the Special Issue Advanced Electrical Machine and Power Electronics for the Charging and Drive System of Electric Vehicles (EVs))
Open AccessArticle
Investment Decision-Making to Select Converted Electric Motorcycle Tests in Indonesia
by
Tasya Santi Rahmawati, Wahyudi Sutopo and Hendro Wicaksono
World Electr. Veh. J. 2024, 15(8), 334; https://doi.org/10.3390/wevj15080334 - 25 Jul 2024
Abstract
The issue of carbon emissions can be addressed through environmentally friendly technological innovations, which contribute to the journey towards achieving net-zero emissions (NZE). The electrification of transportation by converting internal combustion engine (ICE) motorcycles to converted electric motorcycles (CEM) directly reduces the number
[...] Read more.
The issue of carbon emissions can be addressed through environmentally friendly technological innovations, which contribute to the journey towards achieving net-zero emissions (NZE). The electrification of transportation by converting internal combustion engine (ICE) motorcycles to converted electric motorcycles (CEM) directly reduces the number of pollution sources from fossil-powered motors. In Indonesia, numerous government regulations support the commercialization of the CEM system, including the requirement for conversion workshops to be formal entities in the CEM process. Every CEM must pass a test to ensure its safety and suitability. Currently, the CEM testing process is conducted at only one location, making it inefficient and inaccessible. Therefore, most conversion workshops in Indonesia need to take investment steps in procuring CEM-type test tools. This research aims to determine the best alternative from several investment alternatives for CEM-type test tools. In selecting the investment, three criteria are considered: costs, operations, and specifications. By using the investment decision-making model, a hierarchical decision-making model is obtained, which is then processed using the analytical hierarchy process (AHP) and the technique for order of preference by similarity to the ideal solution (TOPSIS). Criteria are weighted to establish a priority order. The final step involves ranking the alternatives and selecting Investment 2 (INV2) as the best investment tool with a relative closeness value of 0.6279. Investment 2 has the shortest time process (40 min), the lowest electricity requirement, and the smallest dimensions. This research aims to provide recommendations for the best investment alternatives that can be purchased by the conversion workshops.
Full article
(This article belongs to the Special Issue Electric Vehicle Technology Development, Energy and Environmental Implications, and Decarbonization)
►▼
Show Figures
![](https://pub.mdpi-res.com/wevj/wevj-15-00334/article_deploy/html/images/wevj-15-00334-g001-550.jpg?1722042533)
Figure 1
Open AccessArticle
Research on the Driving Behavior and Decision-Making of Autonomous Vehicles (AVs) in Mixed Traffic Flow by Integrating Bilayer-GRU-Att and GWO-XGBoost Models
by
Lei Wang, Zhiwei Guan, Jian Liu and Jianyou Zhao
World Electr. Veh. J. 2024, 15(8), 333; https://doi.org/10.3390/wevj15080333 - 25 Jul 2024
Abstract
►▼
Show Figures
The continuous increase in the penetration rate of autonomous vehicles in highway traffic flow has become an irreversible development trend; in this paper, a novel hybrid prediction model of deep sequence learning and an integrated decision tree is proposed for human–machine mixed driving
[...] Read more.
The continuous increase in the penetration rate of autonomous vehicles in highway traffic flow has become an irreversible development trend; in this paper, a novel hybrid prediction model of deep sequence learning and an integrated decision tree is proposed for human–machine mixed driving heterogeneous traffic flow scenarios, so as to realize the accurate prediction of the driving intention of the target vehicle in the traffic environment by autonomous vehicles (AVs). Firstly, the hybrid model uses the attention mechanism-based double-layer gated network model (Bilayer-GRU-Att) to effectively capture the time sequence dependence of the target vehicle’s driving state, and then accurately calculate its trajectory data in different prediction time-domains (tpred). Furthermore, the hybrid model introduces the eXtreme Gradient Boosting decision tree optimized by the Grey Wolf Optimization model (GWO-XGBoost) to identify the lane-changing intention of the target vehicle, because the prediction information of the future trajectory data of the target vehicle by the aforementioned Bilayer-GRU-Att model is properly integrated. The GWO-XGBoost model can accurately predict the lane-changing intention of the target vehicle in different prediction time-domains. Finally, the efficacy of this hybrid model was tested using the HighD dataset for training, validation, and testing purposes. The results of a benchmark analysis indicate that the hybrid model proposed in this paper has the best error evaluation index and balanced prediction time consuming index under the six prediction time-domains. Meanwhile, the hybrid model demonstrates the best classifying performance in predicting the lane-changing intentions of “turning left”, “going straight”, and “turning right” driving behaviors.
Full article
![](https://pub.mdpi-res.com/wevj/wevj-15-00333/article_deploy/html/images/wevj-15-00333-g001-550.jpg?1721975649)
Figure 1
Open AccessReview
A Comprehensive Analysis of Supercapacitors and Their Equivalent Circuits—A Review
by
Pranathi Mehra, Sahaj Saxena and Suman Bhullar
World Electr. Veh. J. 2024, 15(8), 332; https://doi.org/10.3390/wevj15080332 - 25 Jul 2024
Abstract
Supercapacitors (SCs) are an emerging energy storage technology with the ability to deliver sudden bursts of energy, leading to their growing adoption in various fields. This paper conducts a comprehensive review of SCs, focusing on their classification, energy storage mechanism, and distinctions from
[...] Read more.
Supercapacitors (SCs) are an emerging energy storage technology with the ability to deliver sudden bursts of energy, leading to their growing adoption in various fields. This paper conducts a comprehensive review of SCs, focusing on their classification, energy storage mechanism, and distinctions from traditional capacitors to assess their suitability for different applications. To investigate the voltage response of SCs, the existing electrical equivalent circuits are further studied. The analysis is carried forward with the parameter of impedance, which has not so far been addressed. Impedance analysis is essential for a better understanding of SCs as capacitors work on alternating source of supply. The paper also highlights the applications of SCs in electric automobiles and charging stations, showcasing their advantages such as fast charging and higher power density compared to traditional capacitors. Additionally, other applications in areas like the military, medicine, and industry are discussed, demonstrating the versatility of SC technology.
Full article
(This article belongs to the Special Issue Power and Energy Systems for E-mobility)
►▼
Show Figures
![](https://pub.mdpi-res.com/wevj/wevj-15-00332/article_deploy/html/images/wevj-15-00332-g001-550.jpg?1721886379)
Figure 1
Open AccessArticle
The Influence of Psychological Factors on Consumer Purchase Intention for Electric Vehicles: Case Study from China: Integrating the Necessary Condition Analysis Methodology from the Perspective of Self-Determination Theory
by
Haipeng Zhao, Fumitaka Furuoka and Rajah Rasiah
World Electr. Veh. J. 2024, 15(8), 331; https://doi.org/10.3390/wevj15080331 - 24 Jul 2024
Abstract
This paper examines the impact of psychological factors on consumer purchase intention for electric vehicles (EVs) through the lens of Self-Determination Theory (SDT). By integrating the three dimensions of autonomy, relatedness, and competence, this study addresses a research gap in consumer innovative consumption,
[...] Read more.
This paper examines the impact of psychological factors on consumer purchase intention for electric vehicles (EVs) through the lens of Self-Determination Theory (SDT). By integrating the three dimensions of autonomy, relatedness, and competence, this study addresses a research gap in consumer innovative consumption, offering a deeper understanding of green transportation. The research reveals that psychological factors significantly influence innovative consumption and the purchase intention of EVs, aligning with the existing literature. In sustainable transportation, psychological factors such as motivation, attitude, and inner activities increasingly drive purchase decisions. This study examines the direct and indirect effects of psychological factors on purchase intention by employing Partial Least Squares Structural Equation Modeling (PLS-SEM) and Necessary Condition Analysis (NCA). It also considers the moderating role of driving experience in the relationship between psychological factors and innovative consumption. This combined data analysis approach provides a comprehensive understanding of the mechanisms influencing purchase intention, highlighting the intricate interplay between psychological determinants and consumer behavior in the adoption of electric vehicles.
Full article
(This article belongs to the Topic Advanced Electric Vehicle Technology, 2nd Volume)
►▼
Show Figures
![](https://pub.mdpi-res.com/wevj/wevj-15-00331/article_deploy/html/images/wevj-15-00331-g001-550.jpg?1721830136)
Figure 1
Open AccessArticle
Optimal Fast-Charging Strategy for Cylindrical Li-Ion Cells at Different Temperatures
by
Joris Jaguemont, Ali Darwiche and Fanny Bardé
World Electr. Veh. J. 2024, 15(8), 330; https://doi.org/10.3390/wevj15080330 - 24 Jul 2024
Abstract
Ensuring efficiency and safety is critical when developing charging strategies for lithium-ion batteries. This paper introduces a novel method to optimize fast charging for cylindrical Li-ion NMC 3Ah cells, enhancing both their charging efficiency and thermal safety. Using Model Predictive Control (MPC), this
[...] Read more.
Ensuring efficiency and safety is critical when developing charging strategies for lithium-ion batteries. This paper introduces a novel method to optimize fast charging for cylindrical Li-ion NMC 3Ah cells, enhancing both their charging efficiency and thermal safety. Using Model Predictive Control (MPC), this study presents a cost function that estimates the thermal safety boundary of Li-ion batteries, emphasizing the relationship between the temperature gradient and the state of charge (SoC) at different temperatures. The charging control framework combines an equivalent circuit model (ECM) with minimal electro-thermal equations to estimate battery state and temperature. Optimization results indicate that at ambient temperatures, the optimal charging allows the cell’s temperature to self-regulate within a safe operating range, requiring only one additional minute to reach 80% SoC compared to a typical fast-charging protocol (high current profile). Validation through numerical simulations and real experimental data from an NMC 3Ah cylindrical cell demonstrates that the simple approach adheres to the battery’s electrical and thermal limitations during the charging process.
Full article
(This article belongs to the Special Issue EVS37—International Electric Vehicle Symposium and Exhibition (Seoul, Republic of Korea))
►▼
Show Figures
![](https://pub.mdpi-res.com/wevj/wevj-15-00330/article_deploy/html/images/wevj-15-00330-ag-550.jpg?1721822577)
Graphical abstract
Open AccessArticle
The Technology Innovation of Hybrid Electric Vehicles: A Patent-Based Study
by
Yan Zhu, Jie Wu and Oleg Gaidai
World Electr. Veh. J. 2024, 15(8), 329; https://doi.org/10.3390/wevj15080329 - 24 Jul 2024
Abstract
A hybrid electric vehicle (HEV) is a relatively practical technology that has emerged as electric vehicle technology has gradually matured. The analysis of the HEV patent lifecycle is crucial for understanding its impact on the development of this technology. This lifecycle tracks the
[...] Read more.
A hybrid electric vehicle (HEV) is a relatively practical technology that has emerged as electric vehicle technology has gradually matured. The analysis of the HEV patent lifecycle is crucial for understanding its impact on the development of this technology. This lifecycle tracks the progress of HEV technologies from their inception and patenting, through their market adoption, and to the expiration of their patent protection. In this study, we aimed to evaluate the technology lifecycle of the HEV industry using the growth S-curve method. The purpose of this study is to describe the technological lifecycle trajectory and current stage of the HEV industry, as well as the technical stages of each sub-technology, to facilitate better decision making. As part of this study, we used patent family data collected from the Derwent Innovation Index database from 1975 to 2022 and established an S-curve model for HEVs and their sub-technologies using logistic regression. In 2022, the technological maturity of HEVs reached 44%. The sub-technologies with the most substantial diffusion capabilities are energy management, propulsion systems, and cooling circuits. According to predictions, the saturation period for the patent family quantity related to HEVs is estimated to be around 53 years.
Full article
(This article belongs to the Special Issue Electric Vehicle Technology Development, Energy and Environmental Implications, and Decarbonization)
►▼
Show Figures
![](https://pub.mdpi-res.com/wevj/wevj-15-00329/article_deploy/html/images/wevj-15-00329-g001-550.jpg?1721814394)
Figure 1
Open AccessArticle
Anti-Rollover Trajectory Planning Method for Heavy Vehicles in Human–Machine Cooperative Driving
by
Haixiao Wu, Zhongming Wu, Junfeng Lu and Li Sun
World Electr. Veh. J. 2024, 15(8), 328; https://doi.org/10.3390/wevj15080328 - 24 Jul 2024
Abstract
The existing trajectory planning research mainly considers the safety of the obstacle avoidance process rather than the anti-rollover requirements of heavy vehicles. When there are driving risks such as rollover and collision, how to coordinate the game relationship between the two is the
[...] Read more.
The existing trajectory planning research mainly considers the safety of the obstacle avoidance process rather than the anti-rollover requirements of heavy vehicles. When there are driving risks such as rollover and collision, how to coordinate the game relationship between the two is the key technical problem to realizing the anti-rollover trajectory planning under the condition of driving risk triggering. Given the above problems, this paper studies the non-cooperative game model construction method of the obstacle avoidance process that integrates the vehicle driving risk in a complex traffic environment. Then it obtains the obstacle avoidance area that satisfies both the collision and rollover profit requirements based on the Nash equilibrium. A Kmeans-SMOTE risk clustering fusion is proposed in this paper, in which more sampling points are supplemented by the SMOTE oversampling method, and then the ideal obstacle avoidance area is obtained through clustering algorithm fusion to determine the optimal feasible area for obstacle avoidance trajectory planning. On this basis, to solve the convergence problems of the existing multi-objective particle swarm optimization algorithm and analyze the influence of weight parameters and the diversity of the optimization process, this paper proposes an anti-rollover trajectory planning method based on the improved cosine variable weight factor MOPSO algorithm. The simulation results show that the trajectory obtained based on the method proposed in this paper can effectively improve the anti-rollover performance of the controlled vehicle while avoiding obstacles.
Full article
(This article belongs to the Special Issue Dynamics, Control and Simulation of Electrified Vehicles)
►▼
Show Figures
![](https://pub.mdpi-res.com/wevj/wevj-15-00328/article_deploy/html/images/wevj-15-00328-g001-550.jpg?1721809913)
Figure 1
Open AccessArticle
Optimization of Charging Station Capacity Based on Energy Storage Scheduling and Bi-Level Planning Model
by
Wenwen Wang, Yan Liu, Xinglong Fan and Zhengmei Zhang
World Electr. Veh. J. 2024, 15(8), 327; https://doi.org/10.3390/wevj15080327 - 23 Jul 2024
Abstract
With the government’s strong promotion of the transformation of new and old driving forces, the electrification of buses has developed rapidly. In order to improve resource utilization, many cities have decided to open bus charging stations (CSs) to private vehicles, thus leading to
[...] Read more.
With the government’s strong promotion of the transformation of new and old driving forces, the electrification of buses has developed rapidly. In order to improve resource utilization, many cities have decided to open bus charging stations (CSs) to private vehicles, thus leading to the problems of high electricity costs, long waiting times, and increased grid load during peak hours. To address these issues, a dual-layer optimization model was constructed and solved using the Golden Sine Algorithm, balancing the construction cost of CSs and user costs. In addition, the problem was alleviated by combining energy storage scheduling and the M/M/c queue model to reduce grid pressure and shorten waiting times. The study shows that energy storage scheduling effectively reduces grid load, and the electricity cost is reduced by 6.0007%. The average waiting time is reduced to 2.1 min through the queue model, reducing the electric vehicles user’s time cost. The bi-level programming model and energy storage scheduling strategy have positive implications for the operation and development of bus CSs.
Full article
(This article belongs to the Special Issue The Energy Efficiency of Electric Vehicle Charging Stations with Minimal Grid Impact)
►▼
Show Figures
![](https://pub.mdpi-res.com/wevj/wevj-15-00327/article_deploy/html/images/wevj-15-00327-g001-550.jpg?1721740590)
Figure 1
Open AccessArticle
A Study on an Energy-Regenerative Braking Model Using Supercapacitors and DC Motors
by
Alistair Teasdale, Lucky Ishaku, Chiemela Victor Amaechi, Ibitoye Adelusi and Abdelrahman Abdelazim
World Electr. Veh. J. 2024, 15(7), 326; https://doi.org/10.3390/wevj15070326 - 22 Jul 2024
Abstract
This study presents an energy regeneration model and some theory required to construct a regeneration braking system. Due to the effects of carbon dioxide (CO2) emissions, there is increasing interest in the use of electric vehicles (EVs), electric bikes, electric bicycles,
[...] Read more.
This study presents an energy regeneration model and some theory required to construct a regeneration braking system. Due to the effects of carbon dioxide (CO2) emissions, there is increasing interest in the use of electric vehicles (EVs), electric bikes, electric bicycles, electric buses and electric aircraft globally. In order to promote the use of electric transportation systems, there is a need to underscore the impact of net zero emissions. The development of EVs requires regenerating braking system. This study presents the advantages of regenerative braking. This system is globally seen in applications such as electric cars, trams, and trains. In this study, the design specification, design methodology, testing configurations, Simulink model, and recommendations will be outlined. A unique element of this work is the practical experiment that was carried out using 1.5 Amps with no load and 2.15 Amps with a load. The discharge voltage was purely from the 22 W bulb load connected to the capacitor bank as we limited this study to the use of 1.5 Amps and it took 15 min for a full discharge cycle, after which no charge was left in the capacitor bank. The results showed that the discharge rate and charging rate for the regenerative braking system were effective but could be improved. The objective of this paper is to investigate how a supercapacitor works alongside a battery in regenerative braking applications. This study demonstrates that the superconductor used can deliver maximum power when required. Also, it can also withstand elevated peaks in charging or discharging current via the supercapacitor. Combining a battery with a supercapacitor reduces the abrupt load on the battery by shifting it to the capacitor. When these two combinations are used in tandem, the battery pack’s endurance and lifespan are both boosted.
Full article
(This article belongs to the Special Issue Power and Energy Systems for E-mobility)
►▼
Show Figures
![](https://pub.mdpi-res.com/wevj/wevj-15-00326/article_deploy/html/images/wevj-15-00326-g001-550.jpg?1721650211)
Figure 1
Open AccessArticle
Shifting towards Electric Vehicles: A Case Study of Mercedes-Benz from the Perspective of Cross-Functional Teams and Workforce Transformation
by
Charisios Achillas and Parthena Iosifidou
World Electr. Veh. J. 2024, 15(7), 325; https://doi.org/10.3390/wevj15070325 - 22 Jul 2024
Abstract
►▼
Show Figures
The automotive industry’s shift towards electric vehicles (EVs) is driven by technological advancements and environmental concerns. This paper examines Mercedes-Benz’s strategy in this transition, highlighting the challenges and opportunities involved. Using thematic analysis of semi-structured interviews with key professionals at Mercedes-Benz, the study
[...] Read more.
The automotive industry’s shift towards electric vehicles (EVs) is driven by technological advancements and environmental concerns. This paper examines Mercedes-Benz’s strategy in this transition, highlighting the challenges and opportunities involved. Using thematic analysis of semi-structured interviews with key professionals at Mercedes-Benz, the study reveals a dual strategy: integrating new talents with specific EV competencies and upskilling the existing workforce. This approach reflects the company’s recognition of evolving vehicle development requirements and commitment to maintaining a skilled workforce. Emphasis on data-driven functions highlights the industry’s shift towards technological advancements. The transition significantly impacts workforce roles, necessitating role reassignment and collaborative planning, indicating a culture of inclusivity and proactive change management. Challenges include the importance of mindset change and adaptability among employees, as well as managing overlapping traditional and EV projects, leading to increased workloads and compressed timelines. Tailored training and development strategies are essential for a comprehensive transition. Mercedes-Benz’s commitment to an electric-only strategy signals a clear future direction. However, this raises questions about workforce preparedness and ongoing skill development. The study offers insights into managing workforce transformation in the EV transition, contributing to academic discussions and providing practical guidance for industry professionals.
Full article
![](https://pub.mdpi-res.com/wevj/wevj-15-00325/article_deploy/html/images/wevj-15-00325-g001-550.jpg?1721714584)
Figure 1
Open AccessArticle
Dynamic Charging Optimization Algorithm for Electric Vehicles to Mitigate Grid Power Peaks
by
Alain Aoun, Mehdi Adda, Adrian Ilinca, Mazen Ghandour and Hussein Ibrahim
World Electr. Veh. J. 2024, 15(7), 324; https://doi.org/10.3390/wevj15070324 - 21 Jul 2024
Abstract
The rapid proliferation of electric vehicles (EVs) presents both opportunities and challenges for the electrical grid. While EVs offer a promising avenue for reducing greenhouse gas emissions and dependence on fossil fuels, their uncoordinated charging behavior can strain grid infrastructure, thus creating new
[...] Read more.
The rapid proliferation of electric vehicles (EVs) presents both opportunities and challenges for the electrical grid. While EVs offer a promising avenue for reducing greenhouse gas emissions and dependence on fossil fuels, their uncoordinated charging behavior can strain grid infrastructure, thus creating new challenges for grid operators and EV owners equally. The uncoordinated nature of electric vehicle charging may lead to the emergence of new peak loads. Grid operators typically plan for peak demand periods and deploy resources accordingly to ensure grid stability. Uncoordinated EV charging can introduce unpredictability and variability into peak load patterns, making it more challenging for operators to manage peak loads effectively. This paper examines the implications of uncoordinated EV charging on the electric grid to address this challenge and proposes a novel dynamic optimization algorithm tailored to manage EV charging schedules efficiently, mitigating grid power peaks while ensuring user satisfaction and vehicle charging requirements. The proposed “Proof of Need” (PoN) charging algorithm aims to schedule the charging of EVs based on collected data such as the state of charge (SoC) of the EV’s battery, the charger power, the number of connected vehicles per household, the end-user’s preferences, and the local distribution substation’s capacity. The PoN algorithm calculates a priority index for each EV and coordinates the charging of all connected EVs at all times in a way that does not exceed the maximum allocated power capacity. The algorithm was tested under different scenarios, and the results offer a comparison of the charging power demand between an uncoordinated EV charging baseline scenario and the proposed coordinated charging model, proving the efficiency of our proposed algorithm, thus reducing the charging demand by 40.8% with no impact on the overall total charging time.
Full article
(This article belongs to the Topic Electric Vehicles Energy Management, 2nd Volume)
►▼
Show Figures
![](https://pub.mdpi-res.com/wevj/wevj-15-00324/article_deploy/html/images/wevj-15-00324-g001-550.jpg?1721718378)
Figure 1
Open AccessArticle
YOLO-ADual: A Lightweight Traffic Sign Detection Model for a Mobile Driving System
by
Simin Fang, Chengming Chen, Zhijian Li, Meng Zhou and Renjie Wei
World Electr. Veh. J. 2024, 15(7), 323; https://doi.org/10.3390/wevj15070323 - 21 Jul 2024
Abstract
Traffic sign detection plays a pivotal role in autonomous driving systems. The intricacy of the detection model necessitates high-performance hardware. Real-world traffic environments exhibit considerable variability and diversity, posing challenges for effective feature extraction by the model. Therefore, it is imperative to develop
[...] Read more.
Traffic sign detection plays a pivotal role in autonomous driving systems. The intricacy of the detection model necessitates high-performance hardware. Real-world traffic environments exhibit considerable variability and diversity, posing challenges for effective feature extraction by the model. Therefore, it is imperative to develop a detection model that is not only highly accurate but also lightweight. In this paper, we proposed YOLO-ADual, a novel lightweight model. Our method leverages the C3Dual and Adown lightweight modules as replacements for CPS and CBL modules in YOLOv5. The Adown module effectively mitigates feature loss during downsampling while reducing computational costs. Meanwhile, C3Dual optimizes the processing power for kernel feature extraction, enhancing computation efficiency while preserving network depth and feature extraction capability. Furthermore, the inclusion of the CBAM module enables the network to focus on salient information within the image, thus augmenting its feature representation capability. Our proposed algorithm achieves a [email protected] of 70.1% while significantly reducing the number of parameters and computational requirements to 51.83% and 64.73% of the original model, respectively. Compared to various lightweight models, our approach demonstrates competitive performance in terms of both computational efficiency and accuracy.
Full article
(This article belongs to the Special Issue Electric Vehicle Autonomous Driving Based on Image Recognition)
►▼
Show Figures
![](https://pub.mdpi-res.com/wevj/wevj-15-00323/article_deploy/html/images/wevj-15-00323-g001-550.jpg?1721633463)
Figure 1
Open AccessReview
Path Planning Algorithms for Smart Parking: Review and Prospects
by
Zhonghai Han, Haotian Sun, Junfu Huang, Jiejie Xu, Yu Tang and Xintian Liu
World Electr. Veh. J. 2024, 15(7), 322; https://doi.org/10.3390/wevj15070322 - 20 Jul 2024
Abstract
Path planning algorithms are crucial components in the process of smart parking. At present, there are many path planning algorithms designed for smart parking. A well-designed path planning algorithm has a significant impact on the efficiency of smart parking. Firstly, this paper comprehensively
[...] Read more.
Path planning algorithms are crucial components in the process of smart parking. At present, there are many path planning algorithms designed for smart parking. A well-designed path planning algorithm has a significant impact on the efficiency of smart parking. Firstly, this paper comprehensively describes the principles and steps of four types of path planning algorithms: the Dijkstra algorithm (including its optimized derivatives), the A* algorithm (including its optimized derivatives), the RRT (Rapidly exploring Random Trees) algorithm (including its optimized derivatives), and the BFS (Breadth First Search) algorithm. Secondly, the Dijkstra algorithm, the A* algorithm, the BFS algorithm, and the Dynamic Weighted A* algorithm were utilized to plan the paths required for the process of smart parking. During the analysis, it was found that the Dijkstra algorithm had the drawbacks of planning circuitous paths and taking too much time in the path planning for smart parking. Although the traditional A* algorithm based on the Dijkstra algorithm had greatly reduced the planning time, the effect of path planning was still unsatisfactory. The BFS (Breadth First Search) algorithm had the shortest planning time among the four algorithms, but the paths it plans were unstable and not optimal. The Dynamic Weighted A* algorithm could achieve better path planning results, and with adjustments to the weight values, this algorithm had excellent adaptability. This review provides a reference for further research on path planning algorithms in the process of smart parking.
Full article
(This article belongs to the Special Issue Research on Intelligent Vehicle Path Planning Algorithm)
►▼
Show Figures
![](https://pub.mdpi-res.com/wevj/wevj-15-00322/article_deploy/html/images/wevj-15-00322-g001-550.jpg?1721637246)
Figure 1
Open AccessArticle
Single-Snapshot Direction of Arrival Estimation for Vehicle-Mounted Millimeter-Wave Radar via Fast Deterministic Maximum Likelihood Algorithm
by
Hong Liu, Han Xie, Zhen Wang, Xianling Wang and Donghang Chai
World Electr. Veh. J. 2024, 15(7), 321; https://doi.org/10.3390/wevj15070321 - 20 Jul 2024
Abstract
As one of the fundamental vehicular perception technologies, millimeter-wave radar’s accuracy in angle measurement affects the decision-making and control of vehicles. In order to enhance the accuracy and efficiency of the Direction of Arrival (DoA) estimation of radar systems, a super-resolution angle measurement
[...] Read more.
As one of the fundamental vehicular perception technologies, millimeter-wave radar’s accuracy in angle measurement affects the decision-making and control of vehicles. In order to enhance the accuracy and efficiency of the Direction of Arrival (DoA) estimation of radar systems, a super-resolution angle measurement strategy based on the Fast Deterministic Maximum Likelihood (FDML) algorithm is proposed in this paper. This strategy sequentially uses Digital Beamforming (DBF) and Deterministic Maximum Likelihood (DML) in the Field of View (FoV) to perform a rough search and precise search, respectively. In a simulation with a signal-to-noise ratio of 20 dB, FDML can accurately determine the target angle in just 16.8 ms, with a positioning error of less than 0.7010. DBF, the Iterative Adaptive Approach (IAA), DML, Fast Iterative Adaptive Approach (FIAA), and FDML are subjected to simulation with two targets, and their performance is compared in this paper. The results demonstrate that under the same angular resolution, FDML reduces computation time by and angle measurement error by compared with the angular measurement results of two targets. The FDML algorithm significantly improves computational efficiency while ensuring measurement performance. It provides more reliable technical support for autonomous vehicles and lays a solid foundation for the advancement of autonomous driving technology.
Full article
(This article belongs to the Special Issue Advanced Vehicle Dynamics Identification, Control and Observer Methods for Autonomous, Electrified Vehicles)
►▼
Show Figures
![](https://pub.mdpi-res.com/wevj/wevj-15-00321/article_deploy/html/images/wevj-15-00321-g001-550.jpg?1721880945)
Figure 1
Open AccessArticle
Dynamic Obstacle Avoidance for Mobile Robots Based on 2D Differential Euclidean Signed Distance Field Maps in Park Environment
by
Jingze Zhong, Mengjie Zhang, Zonghai Chen and Jikai Wang
World Electr. Veh. J. 2024, 15(7), 320; https://doi.org/10.3390/wevj15070320 - 20 Jul 2024
Abstract
►▼
Show Figures
In this paper, a novel and complete navigation system is proposed for mobile robots in a park environment, which can achieve safe and stable navigation as well as robust dynamic obstacle avoidance. The navigation system includes a global planning layer and a local
[...] Read more.
In this paper, a novel and complete navigation system is proposed for mobile robots in a park environment, which can achieve safe and stable navigation as well as robust dynamic obstacle avoidance. The navigation system includes a global planning layer and a local planning layer. The global planner plans a series of way-points using the A* algorithm based on an offline stored occupancy grid map and sends them to the local planner. The local planner incorporates a dynamic obstacle avoidance mechanism. In contrast to existing dynamic obstacle avoidance algorithms based on trajectory tracking, we innovatively construct a two-dimensional Difference ESDF (Euclidean Signed Distance Field) map to represent obstacle motion information. The local planner outputs control actions by scoring candidate paths. A series of simulation experiments and real-world tests are conducted to verify that the navigation system can safely and robustly accomplish navigation tasks. The safety distance of the simulation experiment group with the dynamic obstacle avoidance scoring item added increased by 1.223 compared to the group without the dynamic obstacle avoidance scoring item.
Full article
![](https://pub.mdpi-res.com/wevj/wevj-15-00320/article_deploy/html/images/wevj-15-00320-g001-550.jpg?1721638321)
Figure 1
Open AccessArticle
Development of a Low-Expansion and Low-Shrinkage Thermoset Injection Moulding Compound Tailored to Laminated Electrical Sheets
by
Florian Braunbeck, Florian Schönl, Timo Preußler, Hans-Christian Reuss, Martin Demleitner, Holger Ruckdäschel and Philipp Berendes
World Electr. Veh. J. 2024, 15(7), 319; https://doi.org/10.3390/wevj15070319 - 18 Jul 2024
Abstract
This study presents a thermoset moulding compound designed for electrical machines with high power densities. The compound reduces residual stresses induced by the difference in thermal expansion during use and by shrinkage in the compound during the manufacturing process. To reduce the internal
[...] Read more.
This study presents a thermoset moulding compound designed for electrical machines with high power densities. The compound reduces residual stresses induced by the difference in thermal expansion during use and by shrinkage in the compound during the manufacturing process. To reduce the internal stresses in the compound, in the electrical sheet lamination and at their interface, first the moulding’s coefficient of thermal expansion (CTE) must match that of the lamination because the CTE of the electrical sheets cannot be altered. Second, the shrinkage of the compound needs to be minimized because the moulding compound is injected around a prefabricated electrical sheet lamination. This provides greater freedom in the design of an electric motor or generator, especially if the thermoset needs to be directly bonded to the electrical sheet. The basic suitability of the material for the injection moulding process was iteratively optimised and confirmed by spiral flow tests. Due to the reduction of the residual stresses, the compound enables efficient cooling solutions for electrical machines with high power densities. This innovative compound can have a significant impact on electric propulsion systems across industries that use laminated electrical sheets.
Full article
(This article belongs to the Special Issue Advances in Electrification and Thermal Management of Propulsion Systems)
►▼
Show Figures
![](https://pub.mdpi-res.com/wevj/wevj-15-00319/article_deploy/html/images/wevj-15-00319-g001-550.jpg?1721802700)
Figure 1
Open AccessArticle
The Impact of Consumer Sentiment on Sales of New Energy Vehicles: Evidence from Textual Analysis
by
Yaqin Liu, Mengya Zhang, Xi Chen, Ke Li and Liwei Tang
World Electr. Veh. J. 2024, 15(7), 318; https://doi.org/10.3390/wevj15070318 - 18 Jul 2024
Abstract
►▼
Show Figures
The advancement of new energy vehicles (NEVs) represents a strategic initiative to combatting climate change, mitigating the energy crisis, and fostering green growth. Using provincial panel data from China between 2017 and 2022, in this study, we applied machine learning techniques for sentiment
[...] Read more.
The advancement of new energy vehicles (NEVs) represents a strategic initiative to combatting climate change, mitigating the energy crisis, and fostering green growth. Using provincial panel data from China between 2017 and 2022, in this study, we applied machine learning techniques for sentiment analysis of textual reviews, used word frequency statistics to explore consumers’ views on the attributes of new energy vehicles, and constructed a consumer sentiment index to study the impact of consumer sentiment on NEV sales. Considering the dependence of NEVs on a charging station, this paper explores the nonlinear impact of the popularity of charging stations on the relationship between consumer sentiment and sales of new energy vehicles. The findings indicate the potential for enhancement in the areas of space, interior design, and comfort of NEVs. Additionally, consumer sentiment was found to facilitate the diffusion of NEVs, with this effect being heterogeneous across different educational backgrounds, income levels, and ages. Furthermore, the availability of per capita public charging stations was shown to significantly reduce range anxiety and encourage consumer purchasing behavior.
Full article
![](https://pub.mdpi-res.com/wevj/wevj-15-00318/article_deploy/html/images/wevj-15-00318-g001-550.jpg?1721374809)
Figure 1
Open AccessArticle
Optimizing Electric Racing Car Performance through Telemetry-Integrated Battery Charging: A Response Surface Analysis Approach
by
A. F. Villa-Salazar, I. N. Gomez-Miranda, A. F. Romero-Maya, J. D. Velásquez-Gómez and K. Lemmel-Vélez
World Electr. Veh. J. 2024, 15(7), 317; https://doi.org/10.3390/wevj15070317 - 18 Jul 2024
Abstract
The link between the world of communications and the world of racing is provided by the telemetry systems in electric racing cars. These systems send real-time data about the vehicle’s behavior and systems to enable informed decisions during the race. The objective of
[...] Read more.
The link between the world of communications and the world of racing is provided by the telemetry systems in electric racing cars. These systems send real-time data about the vehicle’s behavior and systems to enable informed decisions during the race. The objective of this research was to integrate telemetry into the battery bank of an electric racing car in order to find the optimal values of current and voltage that optimize the charging process and thus improve the performance of the vehicle in competition using Response Surface Analysis. Specifically, the telemetry system consisted of an Arduino Mega, a digital wattmeter, and temperature sensors, all installed in the vehicle. Once the telemetry data were obtained, a response surface design was fitted with current, voltage, and temperature as factors varying from low to high values, with the objective function being to minimize the battery charging time. Using the response surface methodology and the steepest descent algorithm, it was found that all factors significantly affect the charging time, with the minimum charging time being 6961 s, obtained with a current of 2.4 amps and voltages of 50.5 volts and 43.6 volts.
Full article
(This article belongs to the Special Issue Lithium-Ion Batteries for Electric Vehicle)
►▼
Show Figures
![](https://pub.mdpi-res.com/wevj/wevj-15-00317/article_deploy/html/images/wevj-15-00317-g001-550.jpg?1721348914)
Figure 1
![World Electric Vehicle Journal wevj-logo](https://pub.mdpi-res.com/img/journals/wevj-logo.png?f309f59ad8705353)
Journal Menu
► ▼ Journal Menu-
- WEVJ Home
- Aims & Scope
- Editorial Board
- Topical Advisory Panel
- Instructions for Authors
- Special Issues
- Topics
- Article Processing Charge
- Indexing & Archiving
- Editor’s Choice Articles
- Most Cited & Viewed
- Journal Statistics
- Journal History
- Journal Awards
- Society Collaborations
- Conferences
- Editorial Office
Journal Browser
► ▼ Journal BrowserHighly Accessed Articles
Latest Books
E-Mail Alert
News
Topics
Topic in
Energies, Processes, Electronics, Applied Sciences, WEVJ
Energy Management and Efficiency in Electric Motors, Drives, Power Converters and Related Systems
Topic Editors: Mario Marchesoni, Alfonso DamianoDeadline: 15 October 2024
Topic in
Applied Sciences, Batteries, Electricity, Electronics, Sensors, WEVJ, Technologies, Chips
Advanced Wireless Charging Technology
Topic Editors: Chong Zhu, Kailong LiuDeadline: 31 October 2024
Topic in
Computation, Electronics, Energies, Sensors, Sustainability, WEVJ
Modern Power Systems and Units
Topic Editors: Jan Taler, Ali Cemal Benim, Sławomir Grądziel, Marek Majdak, Moghtada Mobedi, Tomasz Sobota, Dawid Taler, Bohdan WęglowskiDeadline: 30 November 2024
Topic in
Energies, Materials, Sensors, Sustainability, Vehicles, WEVJ
Advanced Engines Technologies
Topic Editors: Davide Di Battista, Fabio Fatigati, Marco Di BartolomeoDeadline: 31 December 2024
![loading...](https://pub.mdpi-res.com/img/loading_circle.gif?9a82694213036313?1721979229)
Conferences
Special Issues
Special Issue in
WEVJ
Dynamics Modelling and Control of Electrified Chassis for Intelligent Vehicles
Guest Editors: Junnian Wang, Hongqing ChuDeadline: 31 July 2024
Special Issue in
WEVJ
Temperature Field, Electromagnetic Field, and Operation Control of Permanent Magnet Motor for Electric Vehicles
Guest Editor: Zhongxian ChenDeadline: 31 August 2024
Special Issue in
WEVJ
The Energy Efficiency of Electric Vehicle Charging Stations with Minimal Grid Impact
Guest Editor: Javier Martínez-GómezDeadline: 20 September 2024
Special Issue in
WEVJ
The Contribution of Electric Vehicles to Realization of Dual Carbon Goal
Guest Editors: Yongxing Wang, Chaoru Lu, Dongfan XieDeadline: 1 October 2024