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
Shifting towards Electric Vehicles: A Case Study of Mercedes-Benz from the Perspective of Cross-Functional Teams and Workforce Transformation
World Electr. Veh. J. 2024, 15(7), 325; https://doi.org/10.3390/wevj15070325 (registering DOI) - 22 Jul 2024
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The automotive industry’s shift towards electric vehicles (EVs) is driven by technological advancements and environmental concerns. This paper examines Mercedes-Benz’s strategy in this transition, highlighting the challenges and opportunities involved. Using thematic analysis of semi-structured interviews with key professionals at Mercedes-Benz, the study
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The automotive industry’s shift towards electric vehicles (EVs) is driven by technological advancements and environmental concerns. This paper examines Mercedes-Benz’s strategy in this transition, highlighting the challenges and opportunities involved. Using thematic analysis of semi-structured interviews with key professionals at Mercedes-Benz, the study reveals a dual strategy: integrating new talents with specific EV competencies and upskilling the existing workforce. This approach reflects the company’s recognition of evolving vehicle development requirements and commitment to maintaining a skilled workforce. Emphasis on data-driven functions highlights the industry’s shift towards technological advancements. The transition significantly impacts workforce roles, necessitating role reassignment and collaborative planning, indicating a culture of inclusivity and proactive change management. Challenges include the importance of mindset change and adaptability among employees, as well as managing overlapping traditional and EV projects, leading to increased workloads and compressed timelines. Tailored training and development strategies are essential for a comprehensive transition. Mercedes-Benz’s commitment to an electric-only strategy signals a clear future direction. However, this raises questions about workforce preparedness and ongoing skill development. The study offers insights into managing workforce transformation in the EV transition, contributing to academic discussions and providing practical guidance for industry professionals.
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Open AccessArticle
Dynamic Charging Optimization Algorithm for Electric Vehicles to Mitigate Grid Power Peaks
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Alain Aoun, Mehdi Adda, Adrian Ilinca, Mazen Ghandour and Hussein Ibrahim
World Electr. Veh. J. 2024, 15(7), 324; https://doi.org/10.3390/wevj15070324 (registering DOI) - 21 Jul 2024
Abstract
The rapid proliferation of electric vehicles (EVs) presents both opportunities and challenges for the electrical grid. While EVs offer a promising avenue for reducing greenhouse gas emissions and dependence on fossil fuels, their uncoordinated charging behavior can strain grid infrastructure, thus creating new
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The rapid proliferation of electric vehicles (EVs) presents both opportunities and challenges for the electrical grid. While EVs offer a promising avenue for reducing greenhouse gas emissions and dependence on fossil fuels, their uncoordinated charging behavior can strain grid infrastructure, thus creating new challenges for grid operators and EV owners equally. The uncoordinated nature of electric vehicle charging may lead to the emergence of new peak loads. Grid operators typically plan for peak demand periods and deploy resources accordingly to ensure grid stability. Uncoordinated EV charging can introduce unpredictability and variability into peak load patterns, making it more challenging for operators to manage peak loads effectively. This paper examines the implications of uncoordinated EV charging on the electric grid to address this challenge and proposes a novel dynamic optimization algorithm tailored to manage EV charging schedules efficiently, mitigating grid power peaks while ensuring user satisfaction and vehicle charging requirements. The proposed “Proof of Need” (PoN) charging algorithm aims to schedule the charging of EVs based on collected data such as the state of charge (SoC) of the EV’s battery, the charger power, the number of connected vehicles per household, the end-user’s preferences, and the local distribution substation’s capacity. The PoN algorithm calculates a priority index for each EV and coordinates the charging of all connected EVs at all times in a way that does not exceed the maximum allocated power capacity. The algorithm was tested under different scenarios, and the results offer a comparison of the charging power demand between an uncoordinated EV charging baseline scenario and the proposed coordinated charging model, proving the efficiency of our proposed algorithm, thus reducing the charging demand by 40.8% with no impact on the overall total charging time.
Full article
(This article belongs to the Topic Electric Vehicles Energy Management, 2nd Volume)
Open AccessArticle
YOLO-ADual: A Lightweight Traffic Sign Detection Model for a Mobile Driving System
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Simin Fang, Chengming Chen, Zhijian Li, Meng Zhou and Renjie Wei
World Electr. Veh. J. 2024, 15(7), 323; https://doi.org/10.3390/wevj15070323 (registering DOI) - 21 Jul 2024
Abstract
Traffic sign detection plays a pivotal role in autonomous driving systems. The intricacy of the detection model necessitates high-performance hardware. Real-world traffic environments exhibit considerable variability and diversity, posing challenges for effective feature extraction by the model. Therefore, it is imperative to develop
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Traffic sign detection plays a pivotal role in autonomous driving systems. The intricacy of the detection model necessitates high-performance hardware. Real-world traffic environments exhibit considerable variability and diversity, posing challenges for effective feature extraction by the model. Therefore, it is imperative to develop a detection model that is not only highly accurate but also lightweight. In this paper, we proposed YOLO-ADual, a novel lightweight model. Our method leverages the C3Dual and Adown lightweight modules as replacements for CPS and CBL modules in YOLOv5. The Adown module effectively mitigates feature loss during downsampling while reducing computational costs. Meanwhile, C3Dual optimizes the processing power for kernel feature extraction, enhancing computation efficiency while preserving network depth and feature extraction capability. Furthermore, the inclusion of the CBAM module enables the network to focus on salient information within the image, thus augmenting its feature representation capability. Our proposed algorithm achieves a [email protected] of 70.1% while significantly reducing the number of parameters and computational requirements to 51.83% and 64.73% of the original model, respectively. Compared to various lightweight models, our approach demonstrates competitive performance in terms of both computational efficiency and accuracy.
Full article
(This article belongs to the Special Issue Electric Vehicle Autonomous Driving Based on Image Recognition)
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Path Planning Algorithms for Smart Parking: Review and Prospects
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Zhonghai Han, Haotian Sun, Junfu Huang, Jiejie Xu, Yu Tang and Xintian Liu
World Electr. Veh. J. 2024, 15(7), 322; https://doi.org/10.3390/wevj15070322 (registering DOI) - 20 Jul 2024
Abstract
Path planning algorithms are crucial components in the process of smart parking. At present, there are many path planning algorithms designed for smart parking. A well-designed path planning algorithm has a significant impact on the efficiency of smart parking. Firstly, this paper comprehensively
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Path planning algorithms are crucial components in the process of smart parking. At present, there are many path planning algorithms designed for smart parking. A well-designed path planning algorithm has a significant impact on the efficiency of smart parking. Firstly, this paper comprehensively describes the principles and steps of four types of path planning algorithms: the Dijkstra algorithm (including its optimized derivatives), the A* algorithm (including its optimized derivatives), the RRT (Rapidly exploring Random Trees) algorithm (including its optimized derivatives), and the BFS (Breadth First Search) algorithm. Secondly, the Dijkstra algorithm, the A* algorithm, the BFS algorithm, and the Dynamic Weighted A* algorithm were utilized to plan the paths required for the process of smart parking. During the analysis, it was found that the Dijkstra algorithm had the drawbacks of planning circuitous paths and taking too much time in the path planning for smart parking. Although the traditional A* algorithm based on the Dijkstra algorithm had greatly reduced the planning time, the effect of path planning was still unsatisfactory. The BFS (Breadth First Search) algorithm had the shortest planning time among the four algorithms, but the paths it plans were unstable and not optimal. The Dynamic Weighted A* algorithm could achieve better path planning results, and with adjustments to the weight values, this algorithm had excellent adaptability. This review provides a reference for further research on path planning algorithms in the process of smart parking.
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(This article belongs to the Special Issue Research on Intelligent Vehicle Path Planning Algorithm)
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Single-Snapshot Direction of Arrival Estimation for Vehicle-Mounted Millimeter-Wave Radar via Fast Deterministic Maximum Likelihood Algorithm
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Hong Liu, Han Xie, Zhen Wang, Xianling Wang and Donghang Chai
World Electr. Veh. J. 2024, 15(7), 321; https://doi.org/10.3390/wevj15070321 (registering DOI) - 20 Jul 2024
Abstract
As one of the fundamental vehicular perception technologies, millimeter-wave radar’s accuracy in angle measurement affects the decision-making and control of vehicles. In order to enhance the accuracy and efficiency of the Direction of Arrival (DoA) estimation of radar systems, a super-resolution angle measurement
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As one of the fundamental vehicular perception technologies, millimeter-wave radar’s accuracy in angle measurement affects the decision-making and control of vehicles. In order to enhance the accuracy and efficiency of the Direction of Arrival (DoA) estimation of radar systems, a super-resolution angle measurement strategy based on the Fast Deterministic Maximum Likelihood (FDML) algorithm is proposed in this paper. This strategy sequentially uses Digital Beamforming (DBF) and Deterministic Maximum Likelihood (DML) in the Field of View (FoV) to perform a rough search and precise search, respectively. In a simulation with a signal-to-noise ratio of 20 dB, FDML can accurately determine the target angle in just 16.8 ms, with a positioning error of less than 0.7010. DBF, the Iterative Adaptive Approach (IAA), DML, Fast Iterative Adaptive Approach (FIAA), and FDML are subjected to simulation with two targets, and their performance is compared in this paper. The results demonstrate that under the same angular resolution, FDML reduces computation time by and angle measurement error by compared with the angular measurement results of two targets. The FDML algorithm significantly improves computational efficiency while ensuring measurement performance. It provides more reliable technical support for autonomous vehicles and lays a solid foundation for the advancement of autonomous driving technology.
Full article
(This article belongs to the Special Issue Advanced Vehicle Dynamics Identification, Control and Observer Methods for Autonomous, Electrified Vehicles)
Open AccessArticle
Dynamic Obstacle Avoidance for Mobile Robots Based on 2D Differential Euclidean Signed Distance Field Maps in Park Environment
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Jingze Zhong, Mengjie Zhang, Zonghai Chen and Jikai Wang
World Electr. Veh. J. 2024, 15(7), 320; https://doi.org/10.3390/wevj15070320 (registering DOI) - 20 Jul 2024
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In this paper, a novel and complete navigation system is proposed for mobile robots in a park environment, which can achieve safe and stable navigation as well as robust dynamic obstacle avoidance. The navigation system includes a global planning layer and a local
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In this paper, a novel and complete navigation system is proposed for mobile robots in a park environment, which can achieve safe and stable navigation as well as robust dynamic obstacle avoidance. The navigation system includes a global planning layer and a local planning layer. The global planner plans a series of way-points using the A* algorithm based on an offline stored occupancy grid map and sends them to the local planner. The local planner incorporates a dynamic obstacle avoidance mechanism. In contrast to existing dynamic obstacle avoidance algorithms based on trajectory tracking, we innovatively construct a two-dimensional Difference ESDF (Euclidean Signed Distance Field) map to represent obstacle motion information. The local planner outputs control actions by scoring candidate paths. A series of simulation experiments and real-world tests are conducted to verify that the navigation system can safely and robustly accomplish navigation tasks. The safety distance of the simulation experiment group with the dynamic obstacle avoidance scoring item added increased by 1.223 compared to the group without the dynamic obstacle avoidance scoring item.
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Open AccessArticle
Development of a Low-Expansion and Low-Shrinkage Thermoset Injection Moulding Compound Tailored to Laminated Electrical Sheets
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Florian Braunbeck, Florian Schönl, Timo Preußler, Hans-Christian Reuss, Martin Demleitner, Holger Ruckdäschel and Philipp Berendes
World Electr. Veh. J. 2024, 15(7), 319; https://doi.org/10.3390/wevj15070319 - 18 Jul 2024
Abstract
This study presents a thermoset moulding compound designed for electrical machines with high power densities. The compound reduces residual stresses induced by the difference in thermal expansion during use and by shrinkage in the compound during the manufacturing process. To reduce the internal
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This study presents a thermoset moulding compound designed for electrical machines with high power densities. The compound reduces residual stresses induced by the difference in thermal expansion during use and by shrinkage in the compound during the manufacturing process. To reduce the internal stresses in the compound, in the electrical sheet lamination and at their interface, first the moulding’s coefficient of thermal expansion (CTE) must match that of the lamination because the CTE of the electrical sheets cannot be altered. Second, the shrinkage of the compound needs to be minimized because the moulding compound is injected around a prefabricated electrical sheet lamination. This provides greater freedom in the design of an electric motor or generator, especially if the thermoset needs to be directly bonded to the electrical sheet. The basic suitability of the material for the injection moulding process was iteratively optimised and confirmed by spiral flow tests. Due to the reduction of the residual stresses, the compound enables efficient cooling solutions for electrical machines with high power densities. This innovative compound can have a significant impact on electric propulsion systems across industries that use laminated electrical sheets.
Full article
(This article belongs to the Special Issue Advances in Electrification and Thermal Management of Propulsion Systems)
Open AccessArticle
The Impact of Consumer Sentiment on Sales of New Energy Vehicles: Evidence from Textual Analysis
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Yaqin Liu, Mengya Zhang, Xi Chen, Ke Li and Liwei Tang
World Electr. Veh. J. 2024, 15(7), 318; https://doi.org/10.3390/wevj15070318 - 18 Jul 2024
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The advancement of new energy vehicles (NEVs) represents a strategic initiative to combatting climate change, mitigating the energy crisis, and fostering green growth. Using provincial panel data from China between 2017 and 2022, in this study, we applied machine learning techniques for sentiment
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The advancement of new energy vehicles (NEVs) represents a strategic initiative to combatting climate change, mitigating the energy crisis, and fostering green growth. Using provincial panel data from China between 2017 and 2022, in this study, we applied machine learning techniques for sentiment analysis of textual reviews, used word frequency statistics to explore consumers’ views on the attributes of new energy vehicles, and constructed a consumer sentiment index to study the impact of consumer sentiment on NEV sales. Considering the dependence of NEVs on a charging station, this paper explores the nonlinear impact of the popularity of charging stations on the relationship between consumer sentiment and sales of new energy vehicles. The findings indicate the potential for enhancement in the areas of space, interior design, and comfort of NEVs. Additionally, consumer sentiment was found to facilitate the diffusion of NEVs, with this effect being heterogeneous across different educational backgrounds, income levels, and ages. Furthermore, the availability of per capita public charging stations was shown to significantly reduce range anxiety and encourage consumer purchasing behavior.
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Open AccessArticle
Optimizing Electric Racing Car Performance through Telemetry-Integrated Battery Charging: A Response Surface Analysis Approach
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A. F. Villa-Salazar, I. N. Gomez-Miranda, A. F. Romero-Maya, J. D. Velásquez-Gómez and K. Lemmel-Vélez
World Electr. Veh. J. 2024, 15(7), 317; https://doi.org/10.3390/wevj15070317 - 18 Jul 2024
Abstract
The link between the world of communications and the world of racing is provided by the telemetry systems in electric racing cars. These systems send real-time data about the vehicle’s behavior and systems to enable informed decisions during the race. The objective of
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The link between the world of communications and the world of racing is provided by the telemetry systems in electric racing cars. These systems send real-time data about the vehicle’s behavior and systems to enable informed decisions during the race. The objective of this research was to integrate telemetry into the battery bank of an electric racing car in order to find the optimal values of current and voltage that optimize the charging process and thus improve the performance of the vehicle in competition using Response Surface Analysis. Specifically, the telemetry system consisted of an Arduino Mega, a digital wattmeter, and temperature sensors, all installed in the vehicle. Once the telemetry data were obtained, a response surface design was fitted with current, voltage, and temperature as factors varying from low to high values, with the objective function being to minimize the battery charging time. Using the response surface methodology and the steepest descent algorithm, it was found that all factors significantly affect the charging time, with the minimum charging time being 6961 s, obtained with a current of 2.4 amps and voltages of 50.5 volts and 43.6 volts.
Full article
(This article belongs to the Special Issue Lithium-Ion Batteries for Electric Vehicle)
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Open AccessArticle
Fractional Sliding Mode Observer Control Strategy for Three-Phase PWM Rectifier
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Tao Wang, Xin Li, Jihui Zhang, Shenhui Chen, Jinghao Ma and Cunhao Lin
World Electr. Veh. J. 2024, 15(7), 316; https://doi.org/10.3390/wevj15070316 - 18 Jul 2024
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This research presents a novel current loop control strategy for a three-phase PWM rectifier system aimed at mitigating challenges related to substandard power quality, excessive current harmonics, and insufficient robustness. The suggested approach combines an extended state observer (ESO) with dual-power sliding mode
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This research presents a novel current loop control strategy for a three-phase PWM rectifier system aimed at mitigating challenges related to substandard power quality, excessive current harmonics, and insufficient robustness. The suggested approach combines an extended state observer (ESO) with dual-power sliding mode control that is further enhanced by fractional-order micro-integral operators. This amalgamation enhances the adaptability of the controller to system dynamics and augments the flexibility of the current loop control mechanism. The results of this integration include diminished system oscillations, heightened immunity to external disturbances, and improved robustness and dynamics of the overall system. Through MATLAB/Simulink simulations, the effectiveness of the proposed control methodology is validated, demonstrating superior performance in terms of robustness, dynamic response, power quality enhancement, and mitigation of current harmonics when compared to conventional PI control and standard fractional-order dual-power sliding mode control techniques.
Full article
(This article belongs to the Special Issue Intelligent Electric Vehicle Control, Testing and Evaluation)
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Open AccessArticle
Study of an Electric Vehicle Charging Strategy Considering Split-Phase Voltage Quality
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Fulu Yan, Mian Hua, Feng Zhao and Xuan Liang
World Electr. Veh. J. 2024, 15(7), 315; https://doi.org/10.3390/wevj15070315 - 18 Jul 2024
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Slow-charging electric vehicle (EV) loads are single-phase loads in the power distribution network (PDN). The random access of these EVs to the network brings to the forefront the split-phase voltage quality issues. Therefore, a two-layer EV charging strategy considering split-phase voltage quality is
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Slow-charging electric vehicle (EV) loads are single-phase loads in the power distribution network (PDN). The random access of these EVs to the network brings to the forefront the split-phase voltage quality issues. Therefore, a two-layer EV charging strategy considering split-phase voltage quality is proposed in this paper. Issues with voltage unbalance (VU), split-phase voltage deviation (VD), and split-phase voltage harmonics (VHs) are included in the optimization objective model. An upgraded version of the multi-objective non-dominated sorting genetic algorithm (NSGA-II) is used in the inner layer of the model and to pass the generated EV phase selection scheme to the outer layer. The outer layer consists of a split-phase harmonic current algorithm based on the forward–backward generation method, and feeds the voltage quality calculation results to the inner layer. After several iterations, the optimal EV phase selection scheme can be obtained when the inner layer algorithm satisfies the convergence condition. The results gained for the example indicate that the suggested EV charging approach can effectively handle the PDN’s split-phase voltage quality. Furthermore, it enhances the energy efficiency of PDN operations and promotes further energy consumption.
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
Energy Consumption Estimation Method of Battery Electric Buses Based on Real-World Driving Data
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Peng Wang, Qiao Liu, Nan Xu, Yang Ou, Yi Wang, Zaiqiang Meng, Ning Liu, Jiyao Fu and Jincheng Li
World Electr. Veh. J. 2024, 15(7), 314; https://doi.org/10.3390/wevj15070314 - 18 Jul 2024
Abstract
The estimation of energy consumption under real-world driving conditions is a prerequisite for optimizing bus scheduling and meeting the requirements of route operation, thereby promoting the large-scale application of battery electric buses. However, the limitation of data accuracy and the uncertainty of many
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The estimation of energy consumption under real-world driving conditions is a prerequisite for optimizing bus scheduling and meeting the requirements of route operation, thereby promoting the large-scale application of battery electric buses. However, the limitation of data accuracy and the uncertainty of many factors, such as weather conditions, traffic conditions, and driving styles, etc. make accurate energy consumption estimation complicated. In response to these challenges, a new method for estimating the energy consumption of battery electric buses (BEBs) is proposed in this research. This method estimates the speed profiles of different driving styles and the energy consumption extremes using real-world driving data. First, this research provides the constraints on speed formed by environmental factors including weather conditions, route characteristics, and traffic characteristics. On this basis, there are two levels of estimation for energy consumption. The first level classifies different driving styles and constructs the corresponding speed profiles with the time interval (10 s), the same as real-world driving data. The second level further constructs the speed profiles with the time interval of 1 s by filling in the first-level speed profiles and estimating the energy consumption extremes. Finally, the estimated maximum and minimum value of energy consumption were compared with the true value and the results showed that the real energy consumption did not exceed the extremes we estimated, which proves the method we proposed is reasonable and useful. Therefore, this research can provide a theoretical foundation for the deployment of battery electric buses.
Full article
(This article belongs to the Special Issue Power and Energy Systems for E-mobility)
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Open AccessArticle
The Influence of Brand Greenwashing on EV Purchase Intention: The Moderating Role of Consumer Innovativeness and Peer Brand Attitude
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Yuting Liao and Liang Wu
World Electr. Veh. J. 2024, 15(7), 313; https://doi.org/10.3390/wevj15070313 - 17 Jul 2024
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In the context of new energy Electric Vehicles (EVs), certain car manufacturers engage in deceptive behaviors known as “greenwashing”, including activities such as “subsidy cheating”, “exaggerating carbon reduction claims”, and “selective disclosure of environmental information”. These behaviors have a negative impact on industry
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In the context of new energy Electric Vehicles (EVs), certain car manufacturers engage in deceptive behaviors known as “greenwashing”, including activities such as “subsidy cheating”, “exaggerating carbon reduction claims”, and “selective disclosure of environmental information”. These behaviors have a negative impact on industry progress. While previous studies suggest that consumers’ perceptions of greenwashing towards individual brands extend to the industry as a whole and influence their overall purchase intentions, there remains a gap in understanding how these behaviors specifically affect consumers’ willingness to purchase EVs. To address this gap and enrich the literature on the relationship between greenwashing and consumer choice, this study uses ABC attitude theory and experimental methods to investigate the impact of greenwashing in the EV sector on consumers’ vehicle preferences in three experiments. The results show that consumers’ perceptions of greenwashing in one EV brand negatively influence their purchase intentions towards other brands, mediated by a general skepticism towards environmental claims in the industry. In addition, consumers’ innovativeness and attitudes towards other brands play a negative moderating role in this relationship. The research findings provide comprehensive insights into the complex impact of brand greenwashing on consumer behavior within the EV industry.
Full article
(This article belongs to the Special Issue The Contribution of Electric Vehicles to Realization of Dual Carbon Goal)
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Open AccessArticle
Analytical Calculation of Magnetic Field and Analysis of Rotor Permeability Effects on Permanent Magnet Synchronous Motor with Fractional Slot Concentrated Winding
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Xuandong Wu, Huaiyuan Zhang, Cunxiang Yang and Hongbo Qiu
World Electr. Veh. J. 2024, 15(7), 312; https://doi.org/10.3390/wevj15070312 - 16 Jul 2024
Abstract
Accurate calculation of the flux and the magnetic field distribution of fractional slot concentrated winding permanent magnet synchronous motor (FSCW PMSM) is the basis for motor performance analysis, and rapid calculation is key. In this paper, to solve the problem of difficult modeling
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Accurate calculation of the flux and the magnetic field distribution of fractional slot concentrated winding permanent magnet synchronous motor (FSCW PMSM) is the basis for motor performance analysis, and rapid calculation is key. In this paper, to solve the problem of difficult modeling and accuracy guarantee of the flux linkage differential method, a method is proposed to calculate the flux and the no-load back EMF by the slotless subdomain model. By introducing the leakage flux calculation link, the calculation accuracy is improved, the analytical method results are compared with the finite element method results, and the effectiveness of the proposed method is verified. On this basis, the nonlinear variations of the magnetic field and the no-load back EMF with rotor permeability are determined, and the influence mechanism of rotor length and rotor permeability on the main magnetic circuit is revealed. Finally, an experiment of the prototype is carried out, and the correctness and accuracy of the analytical method and the finite element method is verified by comparing with the experimental results.
Full article
(This article belongs to the Special Issue Advanced Electrical Machine and Power Electronics for the Charging and Drive System of Electric Vehicles (EVs))
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Open AccessArticle
Teleoperated Driving with Virtual Twin Technology: A Simulator-Based Approach
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Keonil Kim and Seok-Cheol Kee
World Electr. Veh. J. 2024, 15(7), 311; https://doi.org/10.3390/wevj15070311 - 16 Jul 2024
Abstract
This study introduces an innovative Teleoperated Driving (ToD) system integrated with virtual twin technology using the MORAI simulator. The system minimizes the need for extensive video data transmission by utilizing text-based vehicle information, significantly reducing the communication load. Key technical advancements include the
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This study introduces an innovative Teleoperated Driving (ToD) system integrated with virtual twin technology using the MORAI simulator. The system minimizes the need for extensive video data transmission by utilizing text-based vehicle information, significantly reducing the communication load. Key technical advancements include the use of high-precision GNSS devices for accurate vehicle location tracking, robust data communication via the MQTT protocol, and the implementation of the Ego Ghost mode in the MORAI simulator for precise vehicle simulation. The integration of these technologies enables efficient data transmission and enhanced system reliability, effectively mitigating issues such as communication blackouts and delays. Our findings demonstrate that this approach ensures stable and efficient operation, optimizing communication resource management and enhancing operational stability, which is crucial for scenarios requiring high video quality and real-time response. This research represents a significant advancement in ToD technology, establishing a precedent for integrating virtual twin systems to create more resource-efficient and reliable autonomous driving backup solutions. The virtual twin-based ToD system provides a robust platform for remote vehicle operation, ensuring safety and reliability in various driving conditions.
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(This article belongs to the Special Issue EVS37—International Electric Vehicle Symposium and Exhibition (Seoul, Republic of Korea))
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Open AccessArticle
Evaluation of Vehicle Lateral and Longitudinal Dynamic Behavior of the New Package-Saving Multi-Link Torsion Axle (MLTA) for BEVs
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Jens Olschewski and Xiangfan Fang
World Electr. Veh. J. 2024, 15(7), 310; https://doi.org/10.3390/wevj15070310 - 15 Jul 2024
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To increase the package space for the battery pack in the rear of battery electric vehicles (BEVs), and thus extend their driving range, a novel rear axle concept called the multi-link torsion axle (MLTA) has been developed. In this work, the kinematic design
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To increase the package space for the battery pack in the rear of battery electric vehicles (BEVs), and thus extend their driving range, a novel rear axle concept called the multi-link torsion axle (MLTA) has been developed. In this work, the kinematic design was extended with an elastokinematic concept, and the MLTA was designed in CAD and realized as a prototype. It was then integrated into a B-class series-production vehicle by adding masses in different locations of the vehicle to replicate the mass distribution of a BEV. Both objective and subjective vehicle dynamic evaluations were conducted, which included kinematic and compliance tests, constant-radius cornering, straight-line braking, and a frequency response test, as well as subjective evaluations by both expert and normal drivers. These test results were analyzed and compared to a production vehicle. It can be concluded that the vehicle dynamic performance of the MLTA-equipped vehicle is, overall, 0.67 grades lower than that of the comparable production vehicle on a 10-grade scale. According to OEM experts, this deficit can be eliminated by tuning the different components of the MLTA and meeting the tolerance requirements of series production vehicles.
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Open AccessArticle
CCBA-NMS-YD: A Vehicle Pedestrian Detection and Tracking Method Based on Improved YOLOv7 and DeepSort
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Zhenhao Yuan, Zhiwen Wang and Ruonan Zhang
World Electr. Veh. J. 2024, 15(7), 309; https://doi.org/10.3390/wevj15070309 - 14 Jul 2024
Abstract
In this paper, we propose a vehicle pedestrian detection and tracking method based on the improved YOLOv7 and DeepSort algorithms. We aim to improve the quality of vehicle pedestrian detection and tracking, addressing the challenges that current commercially available autonomous driving technologies face
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In this paper, we propose a vehicle pedestrian detection and tracking method based on the improved YOLOv7 and DeepSort algorithms. We aim to improve the quality of vehicle pedestrian detection and tracking, addressing the challenges that current commercially available autonomous driving technologies face in complex and changing road traffic situations. First, the NMS (non-maximum suppression) algorithm in YOLOv7 is replaced with a modified Soft-NMS algorithm to ensure that targets can be accurately detected at high densities, and second, the CCBA (coordinate channel attention module) attention mechanism is incorporated to improve the feature extraction and perception capabilities of the network. Finally, a multi-scale feature network is introduced to extract features of small targets more accurately. Finally, the MobileNetV3 lightweight module is introduced into the feature extraction network of DeepSort, which not only reduces the number of model parameters and network complexity, but also improves the tracking performance of the target. The experimental results show that the improved YOLOv7 algorithm improves the average detection accuracy by 3.77% compared to that of the original algorithm; on the MOT20 dataset, the refined DeepSort model achieves a 1.6% increase in MOTA and a 1.9% improvement in MOTP; in addition, the model volume is one-eighth of the original algorithm. In summary, our model is able to achieve the desired real-time and accuracy, which is more suitable for autonomous driving.
Full article
(This article belongs to the Special Issue Advanced Vehicle Dynamics Identification, Control and Observer Methods for Autonomous, Electrified Vehicles)
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Open AccessArticle
Regression Machine Learning Models for the Short-Time Prediction of Genetic Algorithm Results in a Vehicle Routing Problem
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Ivan Kristianto Singgih and Moses Laksono Singgih
World Electr. Veh. J. 2024, 15(7), 308; https://doi.org/10.3390/wevj15070308 - 14 Jul 2024
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Machine learning techniques have advanced rapidly, leading to better prediction accuracy within a short computational time. Such advancement encourages various novel applications, including in the field of operations research. This study introduces a novel way to utilize regression machine learning models to predict
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Machine learning techniques have advanced rapidly, leading to better prediction accuracy within a short computational time. Such advancement encourages various novel applications, including in the field of operations research. This study introduces a novel way to utilize regression machine learning models to predict the objectives of vehicle routing problems that are solved using a genetic algorithm. Previous studies have generally discussed how (1) operations research methods are used independently to generate optimized solutions and (2) machine learning techniques are used independently to predict values from a given dataset. Some studies have discussed the collaborations between operations research and machine learning fields as follows: (1) using machine learning techniques to generate input data for operations research problems, (2) using operations research techniques to optimize the hyper-parameters of machine learning models, and (3) using machine learning to improve the quality of operations research algorithms. This study differs from the types of collaborative studies listed above. This study focuses on the prediction of the objective of the vehicle routing problem directly given the input and output data, without optimizing the problem using operations research algorithms. This study introduces a straightforward framework that captures the input data characteristics for the vehicle routing problem. The proposed framework is applied by generating the input and output data using the genetic algorithm and then using regression machine learning models to predict the obtained objective values. The numerical experiments show that the best models are random forest regression, a generalized linear model with a Poisson distribution, and ridge regression with cross-validation.
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Open AccessArticle
Real-Time Multimodal 3D Object Detection with Transformers
by
Hengsong Liu and Tongle Duan
World Electr. Veh. J. 2024, 15(7), 307; https://doi.org/10.3390/wevj15070307 - 12 Jul 2024
Abstract
The accuracy and real-time performance of 3D object detection are key factors limiting its widespread application. While cameras capture detailed color and texture features, they lack depth information compared to LiDAR. Multimodal detection combining both can improve results but incurs significant computational overhead,
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The accuracy and real-time performance of 3D object detection are key factors limiting its widespread application. While cameras capture detailed color and texture features, they lack depth information compared to LiDAR. Multimodal detection combining both can improve results but incurs significant computational overhead, affecting real-time performance. To address these challenges, this paper presents a real-time multimodal fusion model called Fast Transfusion that combines the benefits of LiDAR and camera sensors and reduces the computational burden of their fusion. Specifically, our Fast Transfusion method uses QConv (Quick Convolution) to replace the convolutional backbones compared to other models. QConv concentrates the convolution operations at the feature map center, where the most information resides, to expedite inference. It also utilizes deformable convolution to better match the actual shapes of detected objects, enhancing accuracy. And the model incorporates EH Decoder (Efficient and Hybrid Decoder) which decouples multiscale fusion into intra-scale interaction and cross-scale fusion, efficiently decoding and integrating features extracted from multimodal data. Furthermore, our proposed semi-dynamic query selection refines the initialization of object queries. On the KITTI 3D object detection dataset, our proposed approach reduced the inference time by 36 ms and improved 3D AP by 1.81% compared to state-of-the-art methods.
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(This article belongs to the Special Issue Advanced Vehicle Dynamics Identification, Control and Observer Methods for Autonomous, Electrified Vehicles)
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Open AccessArticle
Experimental Study on Structure Optimization and Dynamic Characteristics of Articulated Steering for Hydrogen Fuel Cell Engineering Vehicles
by
Qinguo Zhang, Xiaoyang Wang, Zheming Tong, Zhewu Cheng and Xiaojian Liu
World Electr. Veh. J. 2024, 15(7), 306; https://doi.org/10.3390/wevj15070306 - 12 Jul 2024
Abstract
The prominent problem of articulated steering structure of engineering vehicle is that there is pressure oscillation in the hydraulic system during steering, which seriously affects the performance of steering system. To solve this problem, the maximum stroke difference of left and right cylinders
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The prominent problem of articulated steering structure of engineering vehicle is that there is pressure oscillation in the hydraulic system during steering, which seriously affects the performance of steering system. To solve this problem, the maximum stroke difference of left and right cylinders and the minimum maximum cylinder pressure are the optimization objectives, and the position of cylinder hinge point is the design variable. The multi-objective optimization design of articulated steering system is carried out by using the particle swarm optimization algorithm. After optimization, the maximum pressure of the steering system is reduced by 13.5%, and the oscillation amplitude is reduced by 16%, so the optimization effect is obvious. The dynamic characteristics of the hydraulic steering system under different loads, such as pressure and flow rate, are obtained through field steering tests of wheel loaders. The results show that the load has an important effect on the pressure response of the system, and the causes and influencing factors of pressure and flow fluctuation are determined. The relationship between mileage and hydrogen consumption is obtained, which provides data support for vehicle control strategy. The high-pressure overflow power consumption accounts for 60% of the total work, and the work lost on the steering gear reaches 36 kJ. The test results verify the rationality and correctness of the optimization method of steering mechanism and provide data support for the improvement in steering hydraulic system.
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(This article belongs to the Special Issue Advanced Vehicle Dynamics Identification, Control and Observer Methods for Autonomous, Electrified Vehicles)
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