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
Ultra-Fast Nonlinear Model Predictive Control for Motion Control of Autonomous Light Motor Vehicles
World Electr. Veh. J. 2024, 15(7), 299; https://doi.org/10.3390/wevj15070299 (registering DOI) - 4 Jul 2024
Abstract
Advanced Driver Assistance System (ADAS) is the latest buzzword in the automotive industry aimed at reducing human errors and enhancing safety. In ADAS systems, the choice of control strategy is not straightforward due to the highly complex nonlinear dynamics, control objectives, and safety
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Advanced Driver Assistance System (ADAS) is the latest buzzword in the automotive industry aimed at reducing human errors and enhancing safety. In ADAS systems, the choice of control strategy is not straightforward due to the highly complex nonlinear dynamics, control objectives, and safety critical constraints. Nonlinear Model Predictive Control (NMPC) has evolved as a favorite option for optimal control due to its ability to handle such constrained, Multi-Input Multi-Output (MIMO) systems efficiently. However, NMPC suffers from a bottleneck of high computational complexity, making it unsuitable for fast real-time applications. This paper presents a generic framework using Successive Online Linearization-based NMPC (SOL-NMPC) for for the control in ADAS. The nonlinear system is linearized and solved using Linear Model Predictive Control every iteration. Furthermore, offset-free MPC is developed with the Extended Kalman Filter for reducing model mismatch. The developed SOL-NMPC is validated using the 14-Degrees-of-Freedom (DoF) model of a D-class light motor vehicle. The performance is simulated in matlab/Simulink and validated using the CarSim® software (Version 2016). The real-time implementation of the proposed strategy is tested in the Hardware-In-the-Loop (HIL) co-simulation using the STM32-Nucleo-144 development board. The detailed performance analysis is presented along with time profiling. It can be seen that the loss of accuracy can be counteracted by the fast response of the proposed framework.
Full article
(This article belongs to the Special Issue Advanced Vehicle System Dynamics and Control)
Open AccessArticle
Simple Method for Determining Loss Parameters of Electric Cars
by
Ansgar Wego and Stefan Schubotz
World Electr. Veh. J. 2024, 15(7), 298; https://doi.org/10.3390/wevj15070298 - 3 Jul 2024
Abstract
Manufacturers of electric cars provide their vehicles with many technical data that are important for the user. This includes information on dimensions, mass, performance, consumption, battery capacity, range, payload, etc. However, some interesting parameters are usually withheld from the end user. These parameters
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Manufacturers of electric cars provide their vehicles with many technical data that are important for the user. This includes information on dimensions, mass, performance, consumption, battery capacity, range, payload, etc. However, some interesting parameters are usually withheld from the end user. These parameters include, for example, the loss in the energy flow from the battery to the driving wheels or the rolling resistance of the vehicle. However, since these loss parameters have a significant influence on the vehicle’s consumption, it is of interest to know them. This article presents a method for determining these two parameters. The basis for this are simple driving tests that can be carried out by anyone on public roads.
Full article
Open AccessArticle
Enhanced Object Detection in Autonomous Vehicles through LiDAR—Camera Sensor Fusion
by
Zhongmou Dai, Zhiwei Guan, Qiang Chen, Yi Xu and Fengyi Sun
World Electr. Veh. J. 2024, 15(7), 297; https://doi.org/10.3390/wevj15070297 - 3 Jul 2024
Abstract
To realize accurate environment perception, which is the technological key to enabling autonomous vehicles to interact with their external environments, it is primarily necessary to solve the issues of object detection and tracking in the vehicle-movement process. Multi-sensor fusion has become an essential
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To realize accurate environment perception, which is the technological key to enabling autonomous vehicles to interact with their external environments, it is primarily necessary to solve the issues of object detection and tracking in the vehicle-movement process. Multi-sensor fusion has become an essential process in efforts to overcome the shortcomings of individual sensor types and improve the efficiency and reliability of autonomous vehicles. This paper puts forward moving object detection and tracking methods based on LiDAR—camera fusion. Operating based on the calibration of the camera and LiDAR technology, this paper uses YOLO and PointPillars network models to perform object detection based on image and point cloud data. Then, a target box intersection-over-union (IoU) matching strategy, based on center-point distance probability and the improved Dempster–Shafer (D–S) theory, is used to perform class confidence fusion to obtain the final fusion detection result. In the process of moving object tracking, the DeepSORT algorithm is improved to address the issue of identity switching resulting from dynamic objects re-emerging after occlusion. An unscented Kalman filter is utilized to accurately predict the motion state of nonlinear objects, and object motion information is added to the IoU matching module to improve the matching accuracy in the data association process. Through self-collected data verification, the performances of fusion detection and tracking are judged to be significantly better than those of a single sensor. The evaluation indexes of the improved DeepSORT algorithm are 66% for MOTA and 79% for MOTP, which are, respectively, 10% and 5% higher than those of the original DeepSORT algorithm. The improved DeepSORT algorithm effectively solves the problem of tracking instability caused by the occlusion of moving objects.
Full article
(This article belongs to the Special Issue Advanced Vehicle Dynamics Identification, Control and Observer Methods for Autonomous, Electrified Vehicles)
Open AccessArticle
Robot Motion Planning Based on an Adaptive Slime Mold Algorithm and Motion Constraints
by
Rong Chen, Huashan Song, Ling Zheng and Bo Wang
World Electr. Veh. J. 2024, 15(7), 296; https://doi.org/10.3390/wevj15070296 - 3 Jul 2024
Abstract
The rapid advancement of artificial intelligence technology has significantly enhanced the intelligence of mobile robots, facilitating their widespread utilization in unmanned driving, smart home systems, and various other domains. As the scope, scale, and complexity of robot deployment continue to expand, there arises
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The rapid advancement of artificial intelligence technology has significantly enhanced the intelligence of mobile robots, facilitating their widespread utilization in unmanned driving, smart home systems, and various other domains. As the scope, scale, and complexity of robot deployment continue to expand, there arises a heightened demand for enhanced computational power and real-time performance, with path planning emerging as a prominent research focus. In this study, we present an adaptive Lévy flight–rotation slime mold algorithm (LRSMA) for global robot motion planning, which incorporates LRSMA with the cubic Hermite interpolation. Unlike traditional methods, the algorithm eliminates the need for a priori knowledge of appropriate interpolation points. Instead, it autonomously detects the convergence status of LRSMA, dynamically increasing interpolation points to enhance the curvature of the motion curve when it surpasses the predefined threshold. Subsequently, it compares path lengths resulting from two different objective functions to determine the optimal number of interpolation points and the best path. Compared to LRSMA, this algorithm reduced the minimum path length and average processing time by (2.52%, 3.56%) and (38.89%, 62.46%), respectively, along with minimum processing times. Our findings demonstrate that this method effectively generates collision-free, smooth, and curvature-constrained motion curves with the least processing time.
Full article
(This article belongs to the Special Issue Design Theory, Method and Control of Intelligent and Safe Vehicles)
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Open AccessArticle
Research on a Path Tracking Control Strategy for Autonomous Vehicles Based on State Parameter Identification
by
Dapai Shi, Fulin Chu, Qingling Cai, Zhanpeng Wang, Zhilong Lv and Jiaheng Wang
World Electr. Veh. J. 2024, 15(7), 295; https://doi.org/10.3390/wevj15070295 - 2 Jul 2024
Abstract
With the rapid development of autonomous driving technology, estimating and controlling key vehicle state parameters under complex road conditions have become critical challenges. This study combines Unscented Kalman Filtering (UKF) and Sliding Mode Control (SMC) methods to propose an integrated control model for
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With the rapid development of autonomous driving technology, estimating and controlling key vehicle state parameters under complex road conditions have become critical challenges. This study combines Unscented Kalman Filtering (UKF) and Sliding Mode Control (SMC) methods to propose an integrated control model for achieving more efficient control. First, a three-degrees-of-freedom vehicle dynamics model based on the Dugoff tire model is constructed to accurately estimate key vehicle state parameters. Next, UKF is used to estimate road friction coefficients and key vehicle state parameters, and its performance is compared with Extended Kalman Filtering (EKF) under various conditions. The results show the superiority of UKF in identifying road friction coefficients. Based on SMC theory, a sliding surface is designed, and the functional relationship between state variables and control variables is derived to establish the corresponding control model. Joint simulations using Carsim and Simulink under different conditions validate the real-time performance and effectiveness of the designed UKF-SMC integrated control strategy in the presence of external disturbances and system uncertainties. Simulation results indicate that this strategy effectively enhances the overall performance and safety of autonomous vehicles, providing an accurate real-time solution capable of handling complex and variable road conditions. The proposed UKF-SMC integrated control strategy not only proves its theoretical superiority but also demonstrates promising practical applications in simulation experiments. This study provides reliable technical support for the development of autonomous driving technology under complex road conditions.
Full article
Open AccessArticle
Exploring the Relationship between Supply Chain Agility, Consumer and Electric Vehicle Characteristics, and Purchase Intentions in Thailand: A Structural Equation Modeling Approach
by
Adisak Suvittawat
World Electr. Veh. J. 2024, 15(7), 294; https://doi.org/10.3390/wevj15070294 - 2 Jul 2024
Abstract
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This research on electric vehicle purchasing intentions in Thailand using Structural Equation Modeling aimed to achieve the following objectives: Firstly, to investigate the factors influencing consumers’ intentions to purchase electric vehicles. Secondly, to examine the impact of consumer characteristics on supply chain agility
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This research on electric vehicle purchasing intentions in Thailand using Structural Equation Modeling aimed to achieve the following objectives: Firstly, to investigate the factors influencing consumers’ intentions to purchase electric vehicles. Secondly, to examine the impact of consumer characteristics on supply chain agility (SCA). Thirdly, to analyze how electric vehicle characteristics influence supply chain agility. Lastly, to assess the influence of supply chain agility on consumers’ purchasing intentions. The study sampled individuals in Thailand holding personal driver’s licenses and intending to purchase electric cars, totaling 350 respondents selected randomly. Data analysis employed descriptive statistics including frequency, percentage, and mean values. The validity and reliability of the questionnaires were ensured through factor loading and Cronbach’s Alpha tests. Our findings indicated that consumer characteristics, electric vehicle features, and supply chain agility significantly affect purchasing intentions. Consumer-specific factors like social influence, environmental concern, and perceptions of electric vehicles were found to impact purchase intentions. Electric vehicle characteristics such as battery longevity, perceived benefits, and value also influenced purchase intentions. Additionally, supply chain agility factors including flexibility, speed in innovation, and responsiveness to customer needs were identified as influential. The research underscores the importance for manufacturers to prioritize initiatives that enhance customer experience with electric vehicles, alleviating concerns and fostering confidence in their use, thereby encouraging adoption without apprehensions about potential issues.
Full article
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Open AccessArticle
A Novel Robust H∞ Control Approach Based on Vehicle Lateral Dynamics for Practical Path Tracking Applications
by
Jie Wang, Baichao Wang, Congzhi Liu, Litong Zhang and Liang Li
World Electr. Veh. J. 2024, 15(7), 293; https://doi.org/10.3390/wevj15070293 - 30 Jun 2024
Abstract
This paper proposes a robust lateral control scheme for the path tracking of autonomous vehicles. Considering the discrepancies between the model parameters and the actual values of the vehicle and the fluctuation of parameters during driving, the norm-bounded uncertainty is utilized to deal
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This paper proposes a robust lateral control scheme for the path tracking of autonomous vehicles. Considering the discrepancies between the model parameters and the actual values of the vehicle and the fluctuation of parameters during driving, the norm-bounded uncertainty is utilized to deal with the uncertainty of model parameters. Because some state variables in the model are difficult to measure, an observer is designed to estimate state variables and provide accurate state information to improve the robustness of path tracking. An state feedback controller is proposed to suppress system nonlinearity and uncertainty and produce the desired steering wheel angle to solve the path tracking problem. A feedforward control is designed to deal with road curvature and further reduce tracking errors. In summary, a path tracking method with performance is established based on the linear matrix inequality (LMI) technique, and the gains in observer and controller can be obtained directly. The hardware-in-the-loop (HIL) test is built to validate the real-time processing performance of the proposed method to ensure excellent practical application potential, and the effectiveness of the proposed control method is validated through the utilization of urban road and highway scenes. The experimental results indicate that the suggested control approach can track the desired trajectory more precisely compared with the model predictive control (MPC) method and make tracking errors within a small range in both urban and highway scenarios.
Full article
(This article belongs to the Special Issue Dynamics, Control and Simulation of Electrified Vehicles)
Open AccessArticle
Research on Unmanned Vehicle Path Planning Based on the Fusion of an Improved Rapidly Exploring Random Tree Algorithm and an Improved Dynamic Window Approach Algorithm
by
Shuang Wang, Gang Li and Boju Liu
World Electr. Veh. J. 2024, 15(7), 292; https://doi.org/10.3390/wevj15070292 - 30 Jun 2024
Abstract
Aiming at the problem that the traditional rapidly exploring random tree (RRT) algorithm only considers the global path of unmanned vehicles in a static environment, which has the limitation of not being able to avoid unknown dynamic obstacles in real time, and that
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Aiming at the problem that the traditional rapidly exploring random tree (RRT) algorithm only considers the global path of unmanned vehicles in a static environment, which has the limitation of not being able to avoid unknown dynamic obstacles in real time, and that the traditional dynamic window approach (DWA) algorithm is prone to fall into a local optimum during local path planning, this paper proposes a path planning method for unmanned vehicles that integrates improved RRT and DWA algorithms. The RRT algorithm is improved by introducing strategies such as target-biased random sampling, adaptive step size, and adaptive radius node screening, which enhance the efficiency and safety of path planning. The global path key points generated by the improved RRT algorithm are used as the subtarget points of the DWA algorithm, and the DWA algorithm is optimized through the design of an adaptive evaluation function weighting method based on real-time obstacle distances to achieve more reasonable local path planning. Through simulation experiments, the fusion algorithm shows promising results in a variety of typical static and dynamic mixed driving scenarios, can effectively plan a path that meets the driving requirements of an unmanned vehicle, avoids unknown dynamic obstacles, and shows higher path optimization efficiency and driving stability in complex environments, which provides strong support for an unmanned vehicle’s path planning in complex environments.
Full article
(This article belongs to the Special Issue Research on Intelligent Vehicle Path Planning Algorithm)
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Open AccessArticle
Parametric Correlation Analysis between Equivalent Electric Circuit Model and Mechanistic Model Interpretation for Battery Internal Aging
by
Humberto Velasco-Arellano, Néstor Castillo-Magallanes, Nancy Visairo-Cruz, Ciro Alberto Núñez-Gutiérrez and Isabel Lázaro
World Electr. Veh. J. 2024, 15(7), 291; https://doi.org/10.3390/wevj15070291 - 29 Jun 2024
Abstract
In modern electric vehicle applications, understanding the evolution of the internal electrochemical reaction throughout the aging of batteries is as relevant as knowing their state of health. This article demonstrates the feasibility of correlating a mechanistic model of the battery internal electrochemical reactions
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In modern electric vehicle applications, understanding the evolution of the internal electrochemical reaction throughout the aging of batteries is as relevant as knowing their state of health. This article demonstrates the feasibility of correlating a mechanistic model of the battery internal electrochemical reactions with an equivalent electrical circuit (EEC) model, providing a practical and understandable interpretation of the internal reactions for electrical specialists. By way of electrochemical impedance spectroscopy analysis and automatic control theory, a methodology for correlating the resistance and capacitance variations of the EEC model and how they reflect the electrochemical reaction changes is proposed. These changes are represented through the time constants of the three parallel arrays from an EEC model. PS-260 lead–acid batteries were analyzed throughout the SOC and their useful life to validate this methodology. The result analysis allows us to establish that the first array corresponds to the negative electrode reactions in the range of 1.48 Hz to 10 kHz, the second array to the positive electrode reactions and generation of sulfates in the range of 0.5 to 1.48 Hz, and the third array to the generation of sulfates and their diffusion in the range of 0.01 to 0.5 Hz.
Full article
(This article belongs to the Topic Battery Design and Management)
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Open AccessReview
A Review of Key Technologies for Environment Sensing in Driverless Vehicles
by
Yuansheng Huo and Chengwei Zhang
World Electr. Veh. J. 2024, 15(7), 290; https://doi.org/10.3390/wevj15070290 - 29 Jun 2024
Abstract
Environment perception technology is the most important part of driverless technology, and driverless vehicles need to realize decision planning and control by virtue of perception feedback. This paper summarizes the most promising technology methods in the field of perception, namely visual perception technology,
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Environment perception technology is the most important part of driverless technology, and driverless vehicles need to realize decision planning and control by virtue of perception feedback. This paper summarizes the most promising technology methods in the field of perception, namely visual perception technology, radar perception technology, state perception technology, and information fusion technology. Regarding the current development status in the field, the development of the main perception technology is mainly the innovation of information fusion technology and the optimization of algorithms. Multimodal perception and deep learning are becoming popular. The future of the field can be transformed by intelligent sensors, promote edge computing and cloud collaboration, improve system data processing capacity, and reduce the burden of data transmission. Regarding driverless vehicles as a future development trend, the corresponding technology will become a research hotspot.
Full article
Open AccessArticle
An Effective Strategy for Achieving Economic Reliability by Optimal Coordination of Hybrid Thermal–Wind–EV System in a Deregulated System
by
Ravindranadh Chowdary Vankina, Sadhan Gope, Subhojit Dawn, Ahmed Al Mansur and Taha Selim Ustun
World Electr. Veh. J. 2024, 15(7), 289; https://doi.org/10.3390/wevj15070289 - 28 Jun 2024
Abstract
This paper describes an effective operating strategy for electric vehicles (EVs) in a hybrid facility that leverages renewable energy sources. The method is to enhance the profit of the wind–thermal–EV hybrid plant while maintaining the grid frequency (fPG) and energy level
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This paper describes an effective operating strategy for electric vehicles (EVs) in a hybrid facility that leverages renewable energy sources. The method is to enhance the profit of the wind–thermal–EV hybrid plant while maintaining the grid frequency (fPG) and energy level of the EV battery storage system. In a renewable-associated power network, renewable energy producers must submit power supply proposals to the system operator at least one day before operations begin. The market managers then combine the power plans for the next several days based on bids from both power providers and distributors. However, due to the unpredictable nature of renewable resources, the electrical system cannot exactly adhere to the predefined power supply criteria. When true and estimated renewable power generation diverges, the electrical system may experience an excess or shortage of electricity. If there is a disparity between true and estimated wind power (TWP, EWP), the EV plant operates to minimize this variation. This lowers the costs associated with the discrepancy between actual and projected wind speeds (TWS, EWS). The proposed method effectively reduces the uncertainty associated with wind generation while being economically feasible, which is especially important in a deregulated power market. This study proposes four separate energy levels for an EV battery storage system (EEV,max, EEV,opt, EEV,low, and EEV,min) to increase system profit and revenue, which is unique to this work. The optimum operating of these EV battery energy levels is determined by the present electric grid frequency and the condition of TWP and EWP. The proposed approach is tested on a modified IEEE 30 bus system and compared to an existing strategy to demonstrate its effectiveness and superiority. The entire work was completed using the optimization technique called sequential quadratic programming (SQP).
Full article
(This article belongs to the Special Issue Data Exchange between Vehicle and Power System for Optimal Charging)
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Open AccessArticle
Design of an Electric Vehicle Charging System Consisting of PV and Fuel Cell for Historical and Tourist Regions
by
Suleyman Emre Dagteke and Sencer Unal
World Electr. Veh. J. 2024, 15(7), 288; https://doi.org/10.3390/wevj15070288 - 28 Jun 2024
Abstract
One of the most important problems in the widespread use of electric vehicles is the lack of charging infrastructure. Especially in tourist areas where historical buildings are located, the installation of a power grid for the installation of electric vehicle charging stations or
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One of the most important problems in the widespread use of electric vehicles is the lack of charging infrastructure. Especially in tourist areas where historical buildings are located, the installation of a power grid for the installation of electric vehicle charging stations or generating electrical energy by installing renewable energy production systems such as large-sized PV (photovoltaic) and wind turbines poses a problem because it causes the deterioration of the historical texture. Considering the need for renewable energy sources in the transportation sector, our aim in this study is to model an electric vehicle charging station using PVPS (photovoltaic power system) and FC (fuel cell) power systems by using irradiation and temperature data from historical regions. This designed charging station model performs electric vehicle charging, meeting the energy demand of a house and hydrogen production by feeding the electrolyzer with the surplus energy from producing electrical energy with the PVPS during the daytime. At night, when there is no solar radiation, electric vehicle charging and residential energy demand are met with an FC power system. One of the most important advantages of this system is the use of hydrogen storage instead of a battery system for energy storage and the conversion of hydrogen into electrical energy with an FC. Unlike other studies, in our study, fossil energy sources such as diesel generators are not included for the stable operation of the system. The system in this study may need hydrogen refueling in unfavorable climatic conditions and the energy storage capacity is limited by the hydrogen fuel tank capacity.
Full article
(This article belongs to the Special Issue Electric Vehicle Technology Development, Energy and Environmental Implications, and Decarbonization)
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Open AccessArticle
Harmonic Resonance Mechanisms and Influencing Factors of Distributed Energy Grid-Connected Systems
by
Minrui Xu, Zhixin Li, Shufeng Lu, Tianyang Xu, Zhanqi Zhang and **angjun Quan
World Electr. Veh. J. 2024, 15(7), 287; https://doi.org/10.3390/wevj15070287 - 26 Jun 2024
Abstract
With the rapid development of global energy transformation and new power system, ensuring the stability of distributed energy grid connections is the key to maintaining the reliable operation of the whole power system. This paper constructs detailed impedance models of grid-following (GFL) and
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With the rapid development of global energy transformation and new power system, ensuring the stability of distributed energy grid connections is the key to maintaining the reliable operation of the whole power system. This paper constructs detailed impedance models of grid-following (GFL) and grid-forming (GFM) inverters using a harmonic linearization method and thoroughly investigates the mechanisms of resonance when inverters are connected to the grid, as well as the impact of model parameters on the stability of the grid system. This paper also briefly analyzes the scenario where distributed energy and electric vehicles are integrated into the grid simultaneously, demonstrating that grid system stability can be ensured in complex grid situations through reasonable parameter design. Lastly, the accuracy of the proposed impedance models and analysis is verified through MATLAB/Simulink simulations.
Full article
(This article belongs to the Special Issue Active Voltage and Frequency Support Control by the EV, New Energy and Energy Storages)
Open AccessReview
A Comprehensive Review on Smart Electromobility Charging Infrastructure
by
Idowu Adetona Ayoade and Omowunmi Mary Longe
World Electr. Veh. J. 2024, 15(7), 286; https://doi.org/10.3390/wevj15070286 - 26 Jun 2024
Abstract
This study thoroughly analyses Smart Electromobility Charging Infrastructure (SECI), exploring its multifaceted dimensions and advancements. Delving into the intricate landscape of SECI, the study critically evaluates existing technologies, integration methodologies, and emerging trends. Through a systematic examination of literature and empirical studies, the
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This study thoroughly analyses Smart Electromobility Charging Infrastructure (SECI), exploring its multifaceted dimensions and advancements. Delving into the intricate landscape of SECI, the study critically evaluates existing technologies, integration methodologies, and emerging trends. Through a systematic examination of literature and empirical studies, the article elucidates the evolving ecosystem of smart charging solutions, considering aspects including advancements in charging protocols. Additionally, the review highlights challenges and prospects in the SECI domain, providing insightful information for scholars, practitioners, and policymakers involved in the dynamic field of electromobility. Technical potentials, including functionalities and integration with the smart grid, have been thoroughly reviewed. An analysis is conducted on the effects of intelligent charging on power distribution systems and strategies to lessen these effects. This study also examines the development of intelligent charging algorithms, optimisation methods, and security analysis. This paper, therefore, contributes to fostering a more thorough comprehension of the current state and future trajectories of Smart Electromobility Charging Infrastructure.
Full article
(This article belongs to the Special Issue Smart Charging Strategies for Plug-In Electric Vehicles)
Open AccessArticle
CTM-YOLOv8n: A Lightweight Pedestrian Traffic-Sign Detection and Recognition Model with Advanced Optimization
by
Qiang Chen, Zhongmou Dai, Yi Xu and Yuezhen Gao
World Electr. Veh. J. 2024, 15(7), 285; https://doi.org/10.3390/wevj15070285 - 26 Jun 2024
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Traffic-sign detection and recognition (TSDR) is crucial to avoiding harm to pedestrians, especially children, from intelligent connected vehicles and has become a research hotspot. However, due to motion blurring, partial occlusion, and smaller sign sizes, pedestrian TSDR faces increasingly significant challenges. To overcome
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Traffic-sign detection and recognition (TSDR) is crucial to avoiding harm to pedestrians, especially children, from intelligent connected vehicles and has become a research hotspot. However, due to motion blurring, partial occlusion, and smaller sign sizes, pedestrian TSDR faces increasingly significant challenges. To overcome these difficulties, a CTM-YOLOv8n model is proposed based on the YOLOv8n model. With the aim of extracting spatial features more efficiently and making the network faster, the C2f Faster module is constructed to replace the C2f module in the head, which applies filters to only a few input channels while leaving the remaining ones untouched. To enhance small-sign detection, a tiny-object-detection (TOD) layer is designed and added to the first C2f layer in the backbone. Meanwhile, the seventh Conv layer, eighth C2f layer, and connected detection head are deleted to reduce the quantity of model parameters. Eventually, the original CIoU is replaced by the MPDIoU, which is better for training deep models. During experiments, the dataset is augmented, which contains the choice of categories ‘w55’ and ‘w57’ in the TT100K dataset and a collection of two types of traffic signs around the schools in Tian**. Empirical results demonstrate the efficacy of our model, showing enhancements of 5.2% in precision, 10.8% in recall, 7.0% in F1 score, and 4.8% in mAP@0.50. However, the number of parameters is reduced to 0.89M, which is only 30% of the YOLOv8n model. Furthermore, the proposed CTM-YOLOv8n model shows superior performance when tested against other advanced TSDR models.
Full article
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Open AccessArticle
Factors Influencing the Adoption of Electric Jeepneys: A Philippine Perspective
by
Ma. Janice J. Gumasing, Elgene Dayne R. Ramos, Joshua Nathaniel C. Corpuz, Angelo James B. Ofianga, Juan Miguel R. Palad, Lyce Gariel B. Urbina, Mellicynt M. Mascariola and Ardvin Kester S. Ong
World Electr. Veh. J. 2024, 15(7), 284; https://doi.org/10.3390/wevj15070284 - 26 Jun 2024
Abstract
The implementation of e-jeepneys stands as a change process that will eventually transition to the modernization of the public transport system in the Philippines. To address concerns about jeepneys’ effects on the environment, energy use, society, the economy, and policies, their acceptability in
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The implementation of e-jeepneys stands as a change process that will eventually transition to the modernization of the public transport system in the Philippines. To address concerns about jeepneys’ effects on the environment, energy use, society, the economy, and policies, their acceptability in the Philippines must be considered. This research study aims to identify the sources of influence on Filipinos’ adoption of e-jeepney utilization as a mode of transportation using the extended Pro-Environmental Planned Behavior (PEBP) model. A total of 502 commuters voluntarily answered the survey questionnaire. Based on the findings, perceived environmental concern (PEC) is the most significant determinant influencing attitude (AT) and, thus, affecting the Filipinos’ behavioral intention (BI) towards the adoption of e-jeepneys. Conversely, AT was the primary determinant of BI, which strongly supported the notion of AT as a strong driving force sha** behavioral decisions. Moreover, perceived authority support (PAS) emerged as a strong predictor of subjective norms (SNs), demonstrating the influence of institutional support on societal perceptions. As a result, more environmentally conscious people are more likely to view e-jeepneys positively and intend to use them as a mode of transportation. The endorsement or support from authoritative figures or institutions notably influences subjective norms, which are individuals’ perceptions of social pressures regarding the use of e-jeepneys.
Full article
Open AccessArticle
Design, Analysis, and Comparison of Electric Vehicle Drive Motor Rotors Using Injection-Molded Carbon-Fiber-Reinforced Plastics
by
Huai Cong Liu, Jang Soo Park and Il Hwan An
World Electr. Veh. J. 2024, 15(7), 283; https://doi.org/10.3390/wevj15070283 - 25 Jun 2024
Abstract
Due to their excellent mechanical strength, corrosion resistance, and ease of processing, carbon fiber and carbon-fiber-reinforced plastics are finding wide application in diverse fields, including aerospace, industry, and automobiles. This research explores the feasibility of integrating carbon fiber solutions into the rotors of
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Due to their excellent mechanical strength, corrosion resistance, and ease of processing, carbon fiber and carbon-fiber-reinforced plastics are finding wide application in diverse fields, including aerospace, industry, and automobiles. This research explores the feasibility of integrating carbon fiber solutions into the rotors of 85-kilowatt electric vehicle interior permanent magnet synchronous motors. Two novel configurations are proposed: a carbon fiber wire-wound rotor and a carbon fiber injection-molded rotor. A finite element analysis compares the performance of these models against a basic designed rotor, considering factors like no-load back electromotive force, no-load voltage harmonics, cogging torque, load torque, torque ripple, efficiency, and manufacturing cost. Additionally, a comprehensive analysis of system efficiency and energy loss based on hypothetical electric vehicle parameters is presented. Finally, mechanical strength simulations assess the feasibility of the proposed carbon fiber composite rotor designs.
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(This article belongs to the Special Issue Design and Control of Electrical Machines in Electric Vehicles, 2nd Edition)
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Open AccessArticle
Impact of Temperature Variations on Torque Capacity in Shrink-Fit Junctions of Water-Jacketed Permanent Magnet Synchronous Motors (PMSMs)
by
David Sebastian Puma-Benavides, Luis Mixquititla-Casbis, Edilberto Antonio Llanes-Cedeño and Juan Carlos Jima-Matailo
World Electr. Veh. J. 2024, 15(7), 282; https://doi.org/10.3390/wevj15070282 - 25 Jun 2024
Abstract
This study investigates the impact of temperature variations on the torque capacity of shrink-fit junctions in water-jacketed permanent magnet synchronous motors. Focusing on both baseline and improved designs; torque capacities were evaluated across a temperature range from −40 °C to 120 °C under
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This study investigates the impact of temperature variations on the torque capacity of shrink-fit junctions in water-jacketed permanent magnet synchronous motors. Focusing on both baseline and improved designs; torque capacities were evaluated across a temperature range from −40 °C to 120 °C under different material conditions: Least material condition, nominal, and maximum material condition. The baseline design exhibited torque capacities from 7648 Nm to 9032 Nm at −40 °C, decreasing significantly to 549 Nm to 1533 Nm at 120 °C. The improved design showed enhanced performance, with torque capacities ranging from 8055 Nm to 9247 Nm at −40 °C and from 842 Nm to 1618 Nm at 120 °C. The maximum improvement was observed at 120 °C for least material conditions, with a 55.4% increase, and the minimum improvement at −40 °C for maximum material conditions, with a 2.4% increase. Our findings demonstrate a significant increase in torque capacity by up to 20% under varied thermal conditions. These results underscore the effectiveness of design modifications in enhancing thermal stability and torque capacity, making the improved design a more reliable choice for high-performance applications subject to significant thermal fluctuations. This research highlights the critical role of material selection, thermal management, and precise design adjustments in optimizing the performance and reliability of permanent magnet synchronous motors.
Full article
(This article belongs to the Special Issue The Energy Efficiency of Electric Vehicle Charging Stations with Minimal Grid Impact)
Open AccessArticle
Motor Bearing Fault Diagnosis Based on Current Signal Using Time–Frequency Channel Attention
by
Zhiqiang Wang, Chao Guan, Shangru Shi, Guozheng Zhang and **n Gu
World Electr. Veh. J. 2024, 15(7), 281; https://doi.org/10.3390/wevj15070281 - 24 Jun 2024
Abstract
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As they are the core components of the drive motor in electric vehicles, the accurate fault diagnosis of rolling bearings is the key to ensuring the safe operation of electric vehicles. At present, intelligent diagnostic methods based on current signals (CSs) are widely
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As they are the core components of the drive motor in electric vehicles, the accurate fault diagnosis of rolling bearings is the key to ensuring the safe operation of electric vehicles. At present, intelligent diagnostic methods based on current signals (CSs) are widely used owing to the advantages of the easy collection, low cost, and non-invasiveness of CSs. However, in practical applications, the fault characteristics of the CS are weak, resulting in diagnostic performance that fails to meet the expected standards. In this paper, a diagnosis method is proposed to address this problem and enhance the diagnosis accuracy. Firstly, CSs from two phases are processed by periodic resampling to enhance data features, which are then fused through splicing operations. Subsequently, a feature enhancement module is constructed using multi-scale feature fusion for decomposing the input. Finally, a diagnosis model is constructed by using an improved channel attention module (CAM) for enhancing the diagnosis performance. The results from experiments containing two different types of bearing datasets show that the proposed method can extract high-quality fault features and improve the diagnosis accuracy, presenting great potential in intelligent fault diagnosis and the maintenance of electric vehicles.
Full article
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Figure 1
Open AccessArticle
Lithium-Ion Battery SOH Estimation Method Based on Multi-Feature and CNN-BiLSTM-MHA
by
Yujie Zhou, Chaolong Zhang, Xulong Zhang and Ziheng Zhou
World Electr. Veh. J. 2024, 15(7), 280; https://doi.org/10.3390/wevj15070280 - 24 Jun 2024
Abstract
Electric vehicles can reduce the dependence on limited resources such as oil, which is conducive to the development of clean energy. An accurate battery state of health (SOH) is beneficial for the safety of electric vehicles. A multi-feature and Convolutional Neural Network–Bidirectional Long
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Electric vehicles can reduce the dependence on limited resources such as oil, which is conducive to the development of clean energy. An accurate battery state of health (SOH) is beneficial for the safety of electric vehicles. A multi-feature and Convolutional Neural Network–Bidirectional Long Short-Term Memory–Multi-head Attention (CNN-BiLSTM-MHA)-based lithium-ion battery SOH estimation method is proposed in this paper. First, the voltage, energy, and temperature data of the battery in the constant current charging phase are measured. Then, based on the voltage and energy data, the incremental energy analysis (IEA) is performed to calculate the incremental energy (IE) curve. The IE curve features including IE, peak value, average value, and standard deviation are extracted and combined with the thermal features of the battery to form a complete multi-feature sequence. A CNN-BiLSTM-MHA model is set up to map the features to the battery SOH. Experiments were conducted using batteries with different charging currents, and the results showed that even if the nonlinearity of battery SOH degradation is significant, this method can still achieve a fast and accurate estimation of the battery SOH. The Mean Absolute Error (MAE) is 0.1982%, 0.1873%, 0.1652%, and 0.1968%, and the Root-Mean-Square Error (RMSE) is 0.2921%, 0.2997%, 0.2130%, and 0.2625%, respectively. The average Coefficient of Determination (R2) is above 96%. Compared to the BiLSTM model, the training time is reduced by an average of about 36%.
Full article
(This article belongs to the Special Issue Intelligent Modelling & Simulation Technology of E-Mobility)
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2 July 2024
Meet Us at the 26th International Conference on Electrical Machines (ICEM 2024), 1–4 September 2024, Turin, Italy
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