-
Application of Real-Life On-Road Driving Data for Simulating the Electrification of Long-Haul Transport Trucks
-
Predicting the Torque Demand of a Battery Electric Vehicle for Real-World Driving Maneuvers Using the NARX Technique
-
Comparison of EV Fast Charging Protocols and Impact of Sinusoidal Half-Wave Fast Charging Methods on Lithium-Ion Cells
-
Simulation-Based Assessment of Energy Consumption of Alternative Powertrains in Agricultural Tractors
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
Global Patent Analysis of Battery Recycling Technologies: A Comparative Study of Korea, China, and the United States
World Electr. Veh. J. 2024, 15(6), 260; https://doi.org/10.3390/wevj15060260 - 14 Jun 2024
Abstract
This study provides a comprehensive analysis of global patent trends in battery recycling, focusing on secondary batteries and related technologies across Korea, China, and the United States. The methodology involved collecting data from various patent databases, followed by quantitative analysis to identify technology
[...] Read more.
This study provides a comprehensive analysis of global patent trends in battery recycling, focusing on secondary batteries and related technologies across Korea, China, and the United States. The methodology involved collecting data from various patent databases, followed by quantitative analysis to identify technology trends and guide future development. The research employed statistical tools to analyze patent activities, including the frequency and scope of patent filings, and comparative analysis to highlight differences between countries. This study reveals distinct emphases on technologies such as lithium-ion and waste battery recycling, highlighting notable differences in patent activities among key companies and countries. China’s large number of patents in battery manufacturing processes contrasts with the USA’s focus on electrochemical cell construction and storage systems, while Korea shows significant activity in waste battery technology. The novelty of this paper lies in its detailed comparative analysis of patent trends across these three major economies, providing insights into the technological focuses and priorities of each country. The study also identifies key challenges, such as the need for consistent innovation and broader geographic coverage in Korea, enhancing patent influence and international presence in China, and ensuring high patent quality and fostering innovation in lagging sectors in the United States. Addressing these challenges through enhanced collaboration, increased R&D investments, and supportive policies is crucial for strengthening the global position and driving further innovation in the battery recycling sector.
Full article
(This article belongs to the Special Issue EVS37—International Electric Vehicle Symposium and Exhibition (Seoul, Republic of Korea))
►
Show Figures
Open AccessArticle
An Automatic Emergency Braking Control Method for Improving Ride Comfort
by
Fei Lai, Junbo Liu and Yuanzhi Hu
World Electr. Veh. J. 2024, 15(6), 259; https://doi.org/10.3390/wevj15060259 - 14 Jun 2024
Abstract
►▼
Show Figures
The contribution of this paper is to present an automatic emergency braking (AEB) optimized algorithm based on time to collision (TTC) and a professional driver fitting (PDF) braking pattern. When the TTC value is less than the given threshold, the PDF control algorithm
[...] Read more.
The contribution of this paper is to present an automatic emergency braking (AEB) optimized algorithm based on time to collision (TTC) and a professional driver fitting (PDF) braking pattern. When the TTC value is less than the given threshold, the PDF control algorithm will be started, and vice versa. According to the standard test scenarios for passenger cars and commercial vehicles, the simulation analysis on the AEB systems using four different control algorithms, namely TTC, quadratic curve deceleration, PDF and proposed optimized control algorithm, is conducted, respectively. The results show that the proposed optimization algorithm can both meet the standard requirements and improve the ride comfort. While ensuring collision avoidance with the preceding vehicle, the control algorithm proposed in this study offers better braking comfort compared to the TTC algorithm and the quadratic curve deceleration algorithm. Additionally, it provides a more appropriate stopping distance compared to the PDF algorithm.
Full article
![](https://pub.mdpi-res.com/wevj/wevj-15-00259/article_deploy/html/images/wevj-15-00259-g001-550.jpg?1718337172)
Figure 1
Open AccessArticle
Exploring User Attitudes and Behavioral Intentions towards Augmented Reality Automotive Assistants: A Mixed-Methods Approach
by
Fucheng Wan, Jian Teng and Lisi Feng
World Electr. Veh. J. 2024, 15(6), 258; https://doi.org/10.3390/wevj15060258 - 12 Jun 2024
Abstract
As augmented reality (AR) technology is increasingly permeating the automotive industry, this study investigates users’ attitudes towards AR automotive assistants and their impact on usage behavior. Using the theory of reasoned action (TRA) and integrating insights from the Kano model, critical factors driving
[...] Read more.
As augmented reality (AR) technology is increasingly permeating the automotive industry, this study investigates users’ attitudes towards AR automotive assistants and their impact on usage behavior. Using the theory of reasoned action (TRA) and integrating insights from the Kano model, critical factors driving user acceptance and engagement were identified. The research reveals that trust in AR technology, perceived utility, and ease of interaction are prioritized by users. Clustering analysis identified three distinct user groups: a ‘Safety-Conscious Group’, a ‘Technology Enthusiast Group’, and an ‘Experience-Seeking Group’, each displaying unique preferences towards AR features. Additionally, a support vector machine (SVM) model effectively predicted user behavior with a training set accuracy of 89.96%. These findings provide valuable insights for the design and marketing of AR automotive assistants, acknowledging both essential features and delighters identified through the Kano model. By understanding user preferences and expectations, tailored AR solutions can be developed to enhance user satisfaction and adoption rates in the automotive sector. Moreover, this research contributes to the sustainable development goals related to the automotive industry by fostering innovation in vehicle technology, promoting eco-friendly driving practices, and enhancing overall mobility efficiency.
Full article
Open AccessArticle
Improved Model Predictive Control Path Tracking Approach Based on Online Updated Algorithm with Fuzzy Control and Variable Prediction Time Domain for Autonomous Vehicles
by
Binshan Liu, Zhaoqiang Wang, Hui Guo and Guoxiang Zhang
World Electr. Veh. J. 2024, 15(6), 257; https://doi.org/10.3390/wevj15060257 - 12 Jun 2024
Abstract
The design of trajectory tracking controllers for smart driving cars still faces problems, such as uncertain parameters and it being time-consuming. To improve the tracking performance of the trajectory tracking controller and reduce the computation of the controller, this paper proposes an improved
[...] Read more.
The design of trajectory tracking controllers for smart driving cars still faces problems, such as uncertain parameters and it being time-consuming. To improve the tracking performance of the trajectory tracking controller and reduce the computation of the controller, this paper proposes an improved model predictive control (MPC) method based on fuzzy control and an online update algorithm. First, a vehicle dynamics model is constructed and a feedforward MPC controller is designed; second, a real-time updating method of the time domain parameters is proposed to replace the previous method of empirically selecting the time domain parameters; lastly, a fuzzy controller is proposed for the real-time adjustment of the weight coefficient matrix of the model predictive controller according to the lateral and heading errors of the vehicle, and a state matrix-based cosine similarity updating mechanism is developed for determining the updating nodes of the state matrix to reduce the controller computation caused by the continuous updating of the state matrix when the longitudinal vehicle speed changes. Finally, the controller is compared with the traditional model prediction controller through the co-simulation of CARSIM and MATLAB/Simulink, and the results show that the controller has great improvement in terms of tracking accuracy and controller computational load.
Full article
(This article belongs to the Special Issue Dynamics, Control and Simulation of Electrified Vehicles)
Open AccessArticle
Design and Construction of a Multipole Electric Motor Using an Axial Flux Configuration
by
Adrián González-Parada, Francisco Moreno Del Valle and Ricard Bosch-Tous
World Electr. Veh. J. 2024, 15(6), 256; https://doi.org/10.3390/wevj15060256 - 12 Jun 2024
Abstract
In the transportation industry, the use of renewable energies has been implemented in conjunction with the development of higher-power electric motors and, consequently, the development of intelligent control systems for torque and speed control. Currently, the coupling between both systems is being developed
[...] Read more.
In the transportation industry, the use of renewable energies has been implemented in conjunction with the development of higher-power electric motors and, consequently, the development of intelligent control systems for torque and speed control. Currently, the coupling between both systems is being developed through mechanical systems, affecting the efficient transmission of energy and the useful life of the components. On the other hand, new configurations of electric motors are being developed, such as axial flux motors (AFM), because these can be coupled directly without a mechanical coupling, given their characteristics of high torque at low speeds. In the present work, an innovative design of a multipole axial flux motor (MAFM) is introduced. General criteria for the design and construction are presented considering the geometry in axial flux and permanent magnets. The performance of the system is evaluated through finite element magnetic simulations (FEMM) and compared with experimental measurements of the developed prototype; confirming the effectiveness of the design, obtaining torques of up to 1.784 Nm without extra mechanical couplings and maximum speed regulation errors of 8.43%. The motor was controlled by a digital pole switching system whit six control mode, applied to a permanent magnet MFA for speed and torque control at constant speed. This control can be extended to conventional radial flux electric motor configurations and intelligent traction applications, based on torque demand.
Full article
(This article belongs to the Topic Advanced Electrical Machine Design and Optimization Ⅱ)
►▼
Show Figures
![](https://pub.mdpi-res.com/wevj/wevj-15-00256/article_deploy/html/images/wevj-15-00256-g001-550.jpg?1718178639)
Figure 1
Open AccessArticle
Novel Deep Learning Domain Adaptation Approach for Object Detection Using Semi-Self Building Dataset and Modified YOLOv4
by
Ahmed Gomaa and Ahmad Abdalrazik
World Electr. Veh. J. 2024, 15(6), 255; https://doi.org/10.3390/wevj15060255 - 12 Jun 2024
Abstract
Moving object detection is a vital research area that plays an essential role in intelligent transportation systems (ITSs) and various applications in computer vision. Recently, researchers have utilized convolutional neural networks (CNNs) to develop new techniques in object detection and recognition. However, with
[...] Read more.
Moving object detection is a vital research area that plays an essential role in intelligent transportation systems (ITSs) and various applications in computer vision. Recently, researchers have utilized convolutional neural networks (CNNs) to develop new techniques in object detection and recognition. However, with the increasing number of machine learning strategies used for object detection, there has been a growing need for large datasets with accurate ground truth used for the training, usually demanding their manual labeling. Moreover, most of these deep strategies are supervised and only applicable for specific scenes with large computational resources needed. Alternatively, other object detection techniques such as classical background subtraction need low computational resources and can be used with general scenes. In this paper, we propose a new a reliable semi-automatic method that combines a modified version of the detection-based CNN You Only Look Once V4 (YOLOv4) technique and background subtraction technique to perform an unsupervised object detection for surveillance videos. In this proposed strategy, background subtraction-based low-rank decomposition is applied firstly to extract the moving objects. Then, a clustering method is adopted to refine the background subtraction (BS) result. Finally, the refined results are used to fine-tune the modified YOLO v4 before using it in the detection and classification of objects. The main contribution of this work is a new detection framework that overcomes manual labeling and creates an automatic labeler that can replace manual labeling using motion information to supply labeled training data (background and foreground) directly from the detection video. Extensive experiments using real-world object monitoring benchmarks indicate that the suggested framework obtains a considerable increase in mAP compared to state-of-the-art results on both the CDnet 2014 and UA-DETRAC datasets.
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-15-00255/article_deploy/html/images/wevj-15-00255-g001-550.jpg?1718183436)
Figure 1
Open AccessArticle
Research on Filtering Algorithm of Vehicle Dynamic Weighing Signal
by
Lingcong Xiong, Tieyi Zhang, Anlu Yuan and Zhipeng Zhang
World Electr. Veh. J. 2024, 15(6), 254; https://doi.org/10.3390/wevj15060254 - 12 Jun 2024
Abstract
This study analyzes the advantages and disadvantages of filtering algorithms for dynamic weighing signals. Highway road surface has road surface unevenness and other influencing factors. The body vibration of the vehicle driving process produces a certain amount of interference signals collected by the
[...] Read more.
This study analyzes the advantages and disadvantages of filtering algorithms for dynamic weighing signals. Highway road surface has road surface unevenness and other influencing factors. The body vibration of the vehicle driving process produces a certain amount of interference signals collected by the load cell to form noise signals. In addition, piezoelectric sensors and amplification circuits introduce a large amount of electrical noise. These noise signals are non-smooth, nonlinear, and have other characteristics. We study the filtering effects of moving average (MA), wavelet transform (WT), and variational mode decomposition (VMD) filtering algorithms on axle weight signals and evaluate the performance of the filtering algorithms through the Root Mean Square Error (RMSE), signal-to-noise ratio (SNR), and Normalized Correlation Coefficient (NCC). The comprehensive analysis shows that the variational modal decomposition filtering algorithm is more advantageous for axial weight signal processing. The design of the axle weight signal noise filtering algorithm is of great significance for improving the accuracy of the overall dynamic weighing system of the vehicle.
Full article
(This article belongs to the Special Issue Cooperative Perception, Communication and Computing for Autonomous Vehicles)
►▼
Show Figures
![](https://pub.mdpi-res.com/wevj/wevj-15-00254/article_deploy/html/images/wevj-15-00254-g001-550.jpg?1718183329)
Figure 1
Open AccessArticle
Multi-Cell Cooperative Resource Allocation and Performance Evaluation for Roadside-Assisted Automated Driving
by
Shu Yang, Xuanhan Zhu, Yang Li, Quan Yuan and Lili Li
World Electr. Veh. J. 2024, 15(6), 253; https://doi.org/10.3390/wevj15060253 - 11 Jun 2024
Abstract
The proliferation of wireless technologies, particularly the advent of 5G networks, has ushered in transformative possibilities for enhancing vehicular communication systems, particularly in the context of autonomous driving. Leveraging sensory data and mapping information downloaded from base stations using I2V links, autonomous vehicles
[...] Read more.
The proliferation of wireless technologies, particularly the advent of 5G networks, has ushered in transformative possibilities for enhancing vehicular communication systems, particularly in the context of autonomous driving. Leveraging sensory data and mapping information downloaded from base stations using I2V links, autonomous vehicles in these networks present the promise of enabling distant perceptual abilities essential to completing various tasks in a dynamic environment. However, the efficient down-link transmission of vehicular network data via base stations, often relying on spectrum sharing, presents a multifaceted challenge. This paper addresses the intricacies of spectrum allocation in vehicular networks, aiming to resolve the thorny issues of cross-station interference and coupling while adapting to the dynamic and evolving characteristics of the vehicular environment. A novel approach is suggested involving the utilization of a multi-agent option-critic reinforcement learning algorithm. This algorithm serves a dual purpose: firstly, it learns the most efficient way to allocate spectrum resources optimally. Secondly, it adapts to the ever-changing dynamics of the environment by learning various policy options tailored to different situations. Moreover, it identifies the conditions under which a switch between these policy options is warranted as the situation evolves. The proposed algorithm is structured in two layers, with the upper layer consisting of policy options that are shared across all agents, and the lower layer comprising intra-option policies executed in a distributed manner. Through experimentation, we showcase the superior spectrum efficiency and communication quality achieved by our approach. Specifically, our approach outperforms the baseline methods in terms of training average reward convergence stability and the transmission success rate. Control-variable experiments also reflect the better adaptability of the proposed method as the environmental conditions change, underscoring the significant potential of the proposed method in aiding successful down-link transmissions in vehicular networks.
Full article
(This article belongs to the Special Issue Cooperative Perception, Communication and Computing for Autonomous Vehicles)
►▼
Show Figures
![](https://pub.mdpi-res.com/wevj/wevj-15-00253/article_deploy/html/images/wevj-15-00253-g001-550.jpg?1718182166)
Figure 1
Open AccessArticle
Intelligent Vehicle Formation System Based on Information Interaction
by
Peng Wang, Tao Ouyang, Shixin Zhao, Xuelin Wang, Zhewen Ni and Yuezhen Fan
World Electr. Veh. J. 2024, 15(6), 252; https://doi.org/10.3390/wevj15060252 - 11 Jun 2024
Abstract
►▼
Show Figures
Urban traffic congestion has become an increasingly serious problem, and the transportation industry is gradually becoming a high-energy-consuming industry. Intelligent Transportation System (ITSs) that integrate technologies such as electronic sensing, data transmission, and intelligent control have emerged as a new approach to fundamentally
[...] Read more.
Urban traffic congestion has become an increasingly serious problem, and the transportation industry is gradually becoming a high-energy-consuming industry. Intelligent Transportation System (ITSs) that integrate technologies such as electronic sensing, data transmission, and intelligent control have emerged as a new approach to fundamentally solving transportation problems. As one of the cores of intelligent transportation systems, multi-vehicle formation technology has the advantage of promoting vehicle information interaction, improving vehicle mobility, and enhancing traffic conditions. Due to the high cost and risk of conducting multi-vehicle formation experiments using real vehicles, experimenting with intelligent vehicles has become a viable option. Based on the leader–follower formation strategy, this study designed an intelligent vehicle formation system using the Arduino platform. It utilizes infrared sensors, ultrasonic sensors, and photoelectric encoders to perceive information about the vehicle fleet and the road. Information is aggregated to the master vehicle through ZigBee communication modules. The controller of the master vehicle applies a PID algorithm, combined with a differential steering model, to solve the speed instructions for each vehicle in the fleet. Motion control instructions are then transmitted to each slave vehicle through ZigBee communication modules, enabling the automatic adjustment of the fleet’s traveling speed and spacing. Additionally, a Bluetooth app has been designed for users to monitor and control the movement status of the fleet dynamically in real time. Experimental verification has shown that this research effectively improves intelligent fleets’ capabilities in environmental perception, intelligent decision-making, collaborative control, and motion execution. It also enhances road traffic efficiency and safety, providing new ideas and methods for the development of autonomous driving technology.
Full article
![](https://pub.mdpi-res.com/wevj/wevj-15-00252/article_deploy/html/images/wevj-15-00252-g001-550.jpg?1718111932)
Figure 1
Open AccessArticle
State-Feedback and Nonsmooth Controller Design for Truck Platoon Subject to Uncertainties and Disturbances
by
Jianbo Feng, Zepeng Gao and Bingying Guo
World Electr. Veh. J. 2024, 15(6), 251; https://doi.org/10.3390/wevj15060251 - 11 Jun 2024
Abstract
►▼
Show Figures
Intelligent truck platoons can benefit road transportation due to the short gap and better fuel economy, but they are also subject to dynamic uncertainties and external disturbances. Therefore, this paper develops a novel robust control algorithm for connected truck platoons. By introducing a
[...] Read more.
Intelligent truck platoons can benefit road transportation due to the short gap and better fuel economy, but they are also subject to dynamic uncertainties and external disturbances. Therefore, this paper develops a novel robust control algorithm for connected truck platoons. By introducing a linearized expression method of platoon error dynamics based on state measurement, the state feedback mechanism combined with a nonsmooth controller for a truck platoon is proposed in the development of the distributed control method. The state-feedback controller can drive the nominal platoon system to the state of second-order consensus, and the nonsmooth controller counterparts the uncertainties and disturbances. The convergence and string stability of the proposed control algorithm are demonstrated both theoretically and experimentally, and the effectiveness and robustness are also verified by simulation tests.
Full article
![](https://pub.mdpi-res.com/wevj/wevj-15-00251/article_deploy/html/images/wevj-15-00251-g001-550.jpg?1718354604)
Figure 1
Open AccessArticle
Simulation and Testing of Self-Reconfigurable Battery Advanced Functions for Automotive Application
by
Rémy Thomas, Nicolas Léto, Jérome Lachaize, Sylvain Bacquet, Yan Lopez and Leandro Cassarino
World Electr. Veh. J. 2024, 15(6), 250; https://doi.org/10.3390/wevj15060250 - 8 Jun 2024
Abstract
This article presents the design and production work carried out jointly by Vitesco Technologies and the CEA in order to build a Self-Reconfigurable Battery (SRB) demonstrator representative of an electric vehicle traction battery pack. The literature demonstrates that the use of an SRB
[...] Read more.
This article presents the design and production work carried out jointly by Vitesco Technologies and the CEA in order to build a Self-Reconfigurable Battery (SRB) demonstrator representative of an electric vehicle traction battery pack. The literature demonstrates that the use of an SRB allows for individual bypassing or serialization of each cell in a battery pack, enabling control of the voltage output and dynamic balancing of the battery pack during all phases of vehicle use. The simulations and tests presented in this article confirm that the use of an SRB results in a 6% reduction in energy consumption compared to a Conventional Battery Pack (CBP) on a driving profile based on WLTP cycles. Additionally, an SRB enhances fast charging performance, with a charging time that is 22% faster than a CBP. Furthermore, it is shown that an SRB without a voltage inversion capability can still be connected directly to the AC grid for charging without the need for a dedicated converter, using only a single diode bridge rectifier for the whole system.
Full article
(This article belongs to the Special Issue EVS36—International Electric Vehicle Symposium and Exhibition (California, USA))
►▼
Show Figures
![](https://pub.mdpi-res.com/wevj/wevj-15-00250/article_deploy/html/images/wevj-15-00250-g001-550.jpg?1718170228)
Figure 1
Open AccessArticle
Joint Estimation of Driving State and Road Surface Adhesion Coefficient of a Four-Wheel Independent and Steering-Drive Electric Vehicle
by
Zhixin Chen, Gang Li, Zhihua Zhang and Ruolan Fan
World Electr. Veh. J. 2024, 15(6), 249; https://doi.org/10.3390/wevj15060249 - 7 Jun 2024
Abstract
Vehicle running state parameters and road surface state are crucial to the stability of four-wheel independent drive and steering electric vehicle control. Therefore, this study explores the estimation of vehicle driving state parameters and road surface adhesion coefficients using a combination of federal
[...] Read more.
Vehicle running state parameters and road surface state are crucial to the stability of four-wheel independent drive and steering electric vehicle control. Therefore, this study explores the estimation of vehicle driving state parameters and road surface adhesion coefficients using a combination of federal Kalman filtering and an intelligent bionic antlion optimization algorithm. Firstly, according to the research purpose of the paper and the focus on the accuracy of the establishment of the three degrees of freedom dynamics model, fully considering the road conditions, the paper adopts the Dugoff tire model and finally completes the establishment of the vehicle state estimation model. Secondly, the drive state estimation algorithm is developed utilizing the principles of federal Kalman filtering and volume Kalman filtering. At the same time, robust estimation theory is introduced into the sub-filter, and the antlion optimization module is designed at the lower layer of the main filter to enhance the accuracy of estimates. It is easy to see that the design of the Antlion federal Kalman travel state estimation algorithm has noticeably enhanced accuracy and traceability, according to the result. Thirdly, a joint estimation algorithm of state estimation and road surface adhesion coefficient has been devised to enhance the stability and precision of the estimation process. Finally, the results showed that the joint estimation algorithm has high accuracy in estimating vehicle driving state parameters such as the center of mass lateral deflection angle and road surface adhesion coefficient by simulation.
Full article
(This article belongs to the Special Issue Advanced Vehicle Dynamics Identification, Control and Observer Methods for Autonomous, Electrified Vehicles)
Open AccessArticle
State of Health Prediction of Lithium-Ion Batteries Based on Multi-Kernel Relevance Vector Machine and Error Compensation
by
Li Zhang, Chao Sun and Shilin Liu
World Electr. Veh. J. 2024, 15(6), 248; https://doi.org/10.3390/wevj15060248 - 6 Jun 2024
Abstract
►▼
Show Figures
Though lithium-ion batteries are extensively applied in electric vehicles as a power source due to their excellent advantages in recent years, the security risk has inarguably always existed. The state of health (SOH) of lithium-ion batteries is one of the most important indicators
[...] Read more.
Though lithium-ion batteries are extensively applied in electric vehicles as a power source due to their excellent advantages in recent years, the security risk has inarguably always existed. The state of health (SOH) of lithium-ion batteries is one of the most important indicators related to security, the prediction of SOH is paid close attention spontaneously. To improve the prediction accuracy of SOH, this paper constructs an SOH prediction model based on a multi-kernel relevance vector machine and error compensation (EC-MKRVM). The provided model comprises a pre-estimation model and an error compensation model, both of which use the multi-kernel relevance vector machine (MKRVM) algorithm. The pre-estimation model takes the feature factors extracted in the charging segment as the input variable and the SOH pre-estimation value as the output. The error compensation model takes the pre-estimation error sequence as the input variable and the SOH prediction error as the output. Finally, the SOH prediction error is used to compensate for the SOH pre-estimation value of the pre-estimation model, and the final SOH prediction value is obtained. To verify the effectiveness and advancement of the model, the CACLE dataset is used for comparative experimental analysis. The results show that the proposed prediction model in this paper has higher prediction accuracy.
Full article
![](https://pub.mdpi-res.com/wevj/wevj-15-00248/article_deploy/html/images/wevj-15-00248-g001-550.jpg?1717666198)
Figure 1
Open AccessArticle
All-Wheel Steering Tracking Control Method for Virtual Rail Trains with Only Interoceptive Sensors
by
Zhenpo Wang, Yi Zhang and Zhifu Wang
World Electr. Veh. J. 2024, 15(6), 247; https://doi.org/10.3390/wevj15060247 - 4 Jun 2024
Abstract
►▼
Show Figures
A virtual rail train (VRT) is a multi-articulated vehicle as well as a novel public transportation system due to its low economic cost, environmental friendliness and high transit capacity. Equipped with all-wheel steering (AWS) and a tracking control method, the super long VRT
[...] Read more.
A virtual rail train (VRT) is a multi-articulated vehicle as well as a novel public transportation system due to its low economic cost, environmental friendliness and high transit capacity. Equipped with all-wheel steering (AWS) and a tracking control method, the super long VRT can travel on urban roads easily. This paper proposed a tracking control approach using only interoceptive sensors with high scene adaptivity. The kinematic model was established first under reasonable assumptions when the sensor configuration was completed simultaneously. A hierarchical controller consists of a front axle controller and a rear axle controller. The former applies virtual axles theory to avoid motion interference. The latter generates a first-axle reference path with path segmentation and a data updating method to improve storage and computational efficiency. Then, a fast curvature matching rear axles control method is developed with an actuator time delay considered. Finally, the proposed approach is verified in a hardware in loop (HIL) simulation under various situations with predefined evaluation standards, which shows better tracking performance and applicability.
Full article
![](https://pub.mdpi-res.com/wevj/wevj-15-00247/article_deploy/html/images/wevj-15-00247-g001-550.jpg?1718180533)
Figure 1
Open AccessArticle
A Comparative Study of Traffic Signal Control Based on Reinforcement Learning Algorithms
by
Chen Ouyang, Zhenfei Zhan and Fengyao Lv
World Electr. Veh. J. 2024, 15(6), 246; https://doi.org/10.3390/wevj15060246 - 4 Jun 2024
Abstract
In recent years, the increasing production and sales of automobiles have led to a notable rise in congestion on urban road traffic systems, particularly at ramps and intersections with traffic signals. Intelligent traffic signal control represents an effective means of addressing traffic congestion.
[...] Read more.
In recent years, the increasing production and sales of automobiles have led to a notable rise in congestion on urban road traffic systems, particularly at ramps and intersections with traffic signals. Intelligent traffic signal control represents an effective means of addressing traffic congestion. Reinforcement learning methods have demonstrated considerable potential for addressing complex traffic signal control problems with multidimensional states and actions. In this research, the team propose Q-learning and Deep Q-Network (DQN) based signal control frameworks that use variable phase sequences and cycle times to adjust the order and the duration of signal phases to obtain a stable traffic signal control strategy. Experiments are simulated using the traffic simulator Simulation of Urban Mobility (SUMO) to test the average speed and the lane occupancy rate of vehicles entering the ramp to evaluate its safety performance and test the vehicle’s traveling time to assess its stability. The simulation results show that both reinforcement learning algorithms are able to control cars in dynamic traffic environments with higher average speed and lower lane occupancy rate than the no-control method and that the DQN control model improves the average speed by about 10% and reduces the lane occupancy rate by about 30% compared to the Q-learning control model, providing a higher safety performance.
Full article
(This article belongs to the Special Issue Development towards Vehicle Safety in Future Smart Traffic Systems)
►▼
Show Figures
![](https://pub.mdpi-res.com/wevj/wevj-15-00246/article_deploy/html/images/wevj-15-00246-g001-550.jpg?1718156759)
Figure 1
Open AccessReview
Beyond Tailpipe Emissions: Life Cycle Assessment Unravels Battery’s Carbon Footprint in Electric Vehicles
by
Sharath K. Ankathi, Jessey Bouchard and Xin He
World Electr. Veh. J. 2024, 15(6), 245; https://doi.org/10.3390/wevj15060245 - 2 Jun 2024
Abstract
While electric vehicles (EVs) offer lower life cycle greenhouse gas emissions in some regions, the concern over the greenhouse gas emissions generated during battery production is often debated. This literature review examines the true environmental trade-offs between conventional lithium-ion batteries (LIBs) and emerging
[...] Read more.
While electric vehicles (EVs) offer lower life cycle greenhouse gas emissions in some regions, the concern over the greenhouse gas emissions generated during battery production is often debated. This literature review examines the true environmental trade-offs between conventional lithium-ion batteries (LIBs) and emerging technologies such as solid-state batteries (SSBs) and sodium-ion batteries (SIBs). It emphasizes the carbon-intensive nature of LIB manufacturing and explores how alternative technologies can enhance efficiency while reducing the carbon footprint. We have used a keyword search technique to review articles related to batteries and their environmental performances. The study results reveal that the greenhouse gas (GHG) emissions of battery production alone range from 10 to 394 kgCO2 eq./kWh. We identified that lithium manganese cobalt oxide and lithium nickel cobalt aluminum oxide batteries, despite their high energy density, exhibit higher GHGs (20–394 kgCO2 eq./kWh) because of the cobalt and nickel production. Lithium iron phosphate (34–246 kgCO2 eq./kWh) and sodium-ion (40–70 kgCO2 eq./kWh) batteries showed lower environmental impacts because of the abundant feedstock, emerging as a sustainable choice, especially when high energy density is not essential. This review also concludes that the GHGs of battery production are highly dependent on the regional grid carbon intensity. Batteries produced in China, for example, have higher GHGs than those produced in the United States (US) and European Union (EU). Understanding the GHGs of battery production is critical to fairly evaluating the environmental impact of battery electric vehicles.
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-15-00245/article_deploy/html/images/wevj-15-00245-g001-550.jpg?1717317796)
Figure 1
Open AccessArticle
Research on a Multi-Strategy Improved Sand Cat Swarm Optimization Algorithm for Three-Dimensional UAV Trajectory Path Planning
by
Lili Liu, Yixin Lu, Bufan Yang, Longyue Yang, Jianyong Zhao, Yue Chen and Longhai Li
World Electr. Veh. J. 2024, 15(6), 244; https://doi.org/10.3390/wevj15060244 - 31 May 2024
Abstract
In response to the issues of premature convergence, lack of population diversity, and poor convergence accuracy in the traditional Sand Cat Swarm Optimization (SCSO) algorithm, a Multi-Strategy Improved SCSO (MISCSO) algorithm is proposed. Firstly, multiple population strategies are used to avoid premature convergence
[...] Read more.
In response to the issues of premature convergence, lack of population diversity, and poor convergence accuracy in the traditional Sand Cat Swarm Optimization (SCSO) algorithm, a Multi-Strategy Improved SCSO (MISCSO) algorithm is proposed. Firstly, multiple population strategies are used to avoid premature convergence and falling into local optima traps. Secondly, a distribution estimation learning strategy is introduced to represent the relationships between individuals, using probability models to improve algorithm performance. Next, the diversity of candidate solutions in the elite pool is utilized to expand the search space and enhance the algorithm’s ability to avoid local solutions. Lastly, a Cauchy disturbance strategy is adopted to accelerate the convergence speed of the algorithm, thereby improving the search efficiency and convergence accuracy. The experimental results of CEC2017 tests show that the improved algorithm balances convergence speed and global search capabilities effectively. Finally, the algorithm is applied to actual drone path planning and compared with six other intelligent algorithms, demonstrating the practicality and effectiveness of the improved algorithm.
Full article
Open AccessArticle
Adaptive Fuzzy Control of an Electronic Differential Based on the Stability Criterion of the Phase Plane Method
by
Shaopeng Zhu, Yekai Xu, Linlin Li, Yong Ren, Chenyang Kuang, Huipeng Chen and Jian Gao
World Electr. Veh. J. 2024, 15(6), 243; https://doi.org/10.3390/wevj15060243 - 31 May 2024
Abstract
To improve the handling stability of distributed drive electric vehicles, this paper introduces an electronic differential control strategy based on the stability criterion of the phase plane method. The strategy first plots the distributed electric vehicle’s center of mass side angle and center
[...] Read more.
To improve the handling stability of distributed drive electric vehicles, this paper introduces an electronic differential control strategy based on the stability criterion of the phase plane method. The strategy first plots the distributed electric vehicle’s center of mass side angle and center of mass angular speed on the phase plane, and then it analyzes the vehicle’s stability under various working conditions to determine the parameters that ensure the stability performance. Subsequently, an adaptive fuzzy control strategy is employed to achieve fast and accurate distribution of the torque to each wheel, thereby enhancing the vehicle’s stability. A joint simulation platform is constructed using MATLAB/Simulink and CarSim. A comparison with the traditional electronic differential strategy demonstrates that the proposed distribution strategy based on phase plane stability exhibited excellent stability.
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-15-00243/article_deploy/html/images/wevj-15-00243-g001-550.jpg?1717550673)
Figure 1
Open AccessArticle
Research on Vertical Cooperation and Pricing Strategy of Electric Vehicle Supply Chain
by
Dou-Dou Wu
World Electr. Veh. J. 2024, 15(6), 242; https://doi.org/10.3390/wevj15060242 - 30 May 2024
Abstract
To determine a vertical cooperation strategy and address the optimal pricing problem of the electric vehicle (EV) supply chain, a supply chain system consisting of two competing EV manufacturers (M1 and M2) and a battery supplier is studied. Firstly, three
[...] Read more.
To determine a vertical cooperation strategy and address the optimal pricing problem of the electric vehicle (EV) supply chain, a supply chain system consisting of two competing EV manufacturers (M1 and M2) and a battery supplier is studied. Firstly, three cooperation strategy models were constructed for the battery supplier and the EV manufacturers, namely: Strategy N (neither the battery supplier nor the two manufacturers cooperate with each other); Strategy I (M1 cooperates with the battery supplier); and Strategy II (M2 cooperates with the battery supplier). Then, the Stackelberg solution method was used to obtain the optimal equilibrium decisions under the three strategic models. Finally, the effect of the preference coefficient of consumers for leasing EVs per unit on the optimal equilibrium decision was analyzed. We found that: (1) The wholesale price of batteries provided by the battery supplier to M1 is always greater than to M2. (2) Strategies I and II prompt M1 and M2 to reduce the unit and fixed rental prices of EVs to some extent, while intensifying the competition between the two manufacturers in terms of EV lease prices. (3) When the consumer preference coefficient (θ) for leasing EVs per unit provided by manufacturer M1 is relatively small, the cooperation alliance S2 and the supply chain achieve the maximum profit under Strategy II; however, while θ is large, M1, cooperative alliance S1, and the entire supply chain could benefit the most under Strategy I.
Full article
Open AccessArticle
Study on Obstacle Detection Method Based on Point Cloud Registration
by
Hongliang Wang, Jianing Wang, Yixin Wang, Dawei Pi, Yijie Chen and Jingjing Fan
World Electr. Veh. J. 2024, 15(6), 241; https://doi.org/10.3390/wevj15060241 - 30 May 2024
Abstract
An efficient obstacle detection system is one of the most important guarantees for improving the active safety performance of autonomous vehicles. This paper proposes an obstacle detection method based on high-precision positioning applied to blocked zones to solve the problems of the high
[...] Read more.
An efficient obstacle detection system is one of the most important guarantees for improving the active safety performance of autonomous vehicles. This paper proposes an obstacle detection method based on high-precision positioning applied to blocked zones to solve the problems of the high complexity of detection results, low computational efficiency, and high load in traditional obstacle detection methods. Firstly, an NDT registration method which uses the likelihood function as the optimal value of the registration score function to calculate the registration parameters is designed to match the scanning point cloud and the target point cloud. Secondly, a target reduction method combined with threshold judgment and the binary tree search algorithm is designed to filter the point cloud of non-road obstacles to improve the processing speed of the computing platform. Meanwhile, KD-tree is used to speed up the clustering process. Finally, a vehicle remote control simulation platform with the combination of a cloud platform and mobile terminal is designed to verify the effectiveness of the strategy in practical application. The results prove that the proposed obstacle detection method can improve the efficiency and accuracy of detection.
Full article
![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?1718274570)
Conferences
Special Issues
Special Issue in
WEVJ
Dynamic Control of Traction Motors for EVs
Guest Editors: Rezkallah Miloud, Ambrish ChandraDeadline: 20 June 2024
Special Issue in
WEVJ
Data Exchange between Vehicle and Power System for Optimal Charging
Guest Editors: Jennifer Leijon, Boel Ekergård, Valeria CastellucciDeadline: 30 June 2024
Special Issue in
WEVJ
Intelligent Modelling & Simulation Technology of E-Mobility
Guest Editors: Zonghai Chen, Chunlin Chen, Kailong Liu, Yujie WangDeadline: 20 July 2024
Special Issue in
WEVJ
Dynamics Modelling and Control of Electrified Chassis for Intelligent Vehicles
Guest Editors: Junnian Wang, Hongqing ChuDeadline: 31 July 2024