Journal Description
World Electric Vehicle Journal
World Electric Vehicle Journal
is the first peer-reviewed, international, scientific journal that comprehensively covers all studies related to battery, hybrid, and fuel cell electric vehicles. The journal is owned by the World Electric Vehicle Association (WEVA) and its members, the European Association for e-Mobility (AVERE), Electric Drive Transportation Association (EDTA), and Electric Vehicle Association of Asia Pacific (EVAAP). It has been published monthly online by MDPI since Volume 9, Issue 1 (2018).
- Open Access— free for readers, with article processing charges (APC) paid by authors or their institutions.
- High Visibility: indexed within Scopus, ESCI (Web of Science), Ei Compendex, and other databases.
- Journal Rank: CiteScore - Q2 (Automotive Engineering)
- Rapid Publication: manuscripts are peer-reviewed and a first decision is provided to authors approximately 14.1 days after submission; acceptance to publication is undertaken in 3.7 days (median values for papers published in this journal in the second half of 2023).
- Recognition of Reviewers: reviewers who provide timely, thorough peer-review reports receive vouchers entitling them to a discount on the APC of their next publication in any MDPI journal, in appreciation of the work done.
Impact Factor:
2.3 (2022)
Latest Articles
A Study on Reducing Traffic Congestion in the Roadside Unit for Autonomous Vehicles Using BSM and PVD
World Electr. Veh. J. 2024, 15(3), 117; https://doi.org/10.3390/wevj15030117 - 18 Mar 2024
Abstract
With the rapid advancement of autonomous vehicles reshaping urban transportation, the importance of innovative traffic management solutions has escalated. This research addresses these challenges through the deployment of roadside units (RSUs), aimed at enhancing traffic flow and safety within the autonomous driving era.
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With the rapid advancement of autonomous vehicles reshaping urban transportation, the importance of innovative traffic management solutions has escalated. This research addresses these challenges through the deployment of roadside units (RSUs), aimed at enhancing traffic flow and safety within the autonomous driving era. Our research, conducted in diverse road settings such as straight and traffic circle roads, delves into the RSUs’ capacity to diminish traffic density and alleviate congestion. Employing vehicle-to-infrastructure communication, we can scrutinize its essential role in navigating autonomous vehicles, incorporating basic safety messages (BSMs) and probe vehicle data (PVD) to accurately monitor vehicle presence and status. This paper presupposes the connectivity of all vehicles, contemplating the integration of on-board units or on-board diagnostics in legacy vehicles to extend connectivity, albeit this aspect falls beyond the work’s current ambit. Our detailed experiments on two types of roads demonstrate that vehicle behavior is significantly impacted when density reaches critical thresholds of 3.57% on straight roads and 34.41% on traffic circle roads. However, it is important to note that the identified threshold values are not absolute. In our experiments, these thresholds represent points at which the behavior of one vehicle begins to significantly impact the flow of two or more vehicles. At these levels, we propose that RSUs intervene to mitigate traffic issues by implementing measures such as prohibiting lane changes or restricting entry to traffic circles. We propose a new message set in PVD for RSUs: road balance. Using this message, RSUs can negotiate between vehicles. This approach underscores the RSUs’ capability to actively manage traffic flow and prevent congestion, highlighting their critical role in maintaining optimal traffic conditions and enhancing road safety.
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(This article belongs to the Special Issue Autonomous Electric Vehicles Combined with Non-connected Vehicles in Smart Cities)
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Comparative Analysis of Following Distances in Different Adaptive Cruise Control Systems at Steady Speeds
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Dilshad Mohammed and Balázs Horváth
World Electr. Veh. J. 2024, 15(3), 116; https://doi.org/10.3390/wevj15030116 - 17 Mar 2024
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Adaptive Cruise Control (ACC) systems have emerged as a significant advancement in automotive technology, promising safer and more efficient driving experiences. However, the performance of ACC systems can vary significantly depending on their type and underlying algorithms. This research presents a comprehensive comparative
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Adaptive Cruise Control (ACC) systems have emerged as a significant advancement in automotive technology, promising safer and more efficient driving experiences. However, the performance of ACC systems can vary significantly depending on their type and underlying algorithms. This research presents a comprehensive comparative analysis of car-following distances in different types of Adaptive Cruise Control systems. We evaluate and compare three distinct categories of ACC systems using three different commercial vehicles brands. The study involves extensive real-world testing at Zalazone Proving Ground, to assess the performance of these systems under various driving conditions, including driving at multiple speeds and applying different car following scenarios. The study investigates how each ACC system manages the minimum following distances according to the type of ACC sensors in each tested vehicle. Our findings revealed that at low to medium ranges of constant driving speeds, there was an approximate linear increase in the average clearances between the two following vehicles for all applied scenarios, with comparatively shorter clearances obtained by the vision-based ACC system, while unstable measurements with a high level of dispersion for all ACC systems were observed at high range of driving speeds.
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A Digitalized Methodology for Co-Design Structural and Performance Optimization of Battery Modules
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Theodoros Kalogiannis, Md Sazzad Hosen, Joeri Van Mierlo, Peter Van Den Bossche and Maitane Berecibar
World Electr. Veh. J. 2024, 15(3), 115; https://doi.org/10.3390/wevj15030115 - 16 Mar 2024
Abstract
In this study, we present an innovative, fully automated, and digitalized methodology to optimize the energy efficiency and cost effectiveness of Li-ion battery modules. Advancing on from today’s optimization schemes that rely on user experience and other limitations, the mechanical and thermal designs
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In this study, we present an innovative, fully automated, and digitalized methodology to optimize the energy efficiency and cost effectiveness of Li-ion battery modules. Advancing on from today’s optimization schemes that rely on user experience and other limitations, the mechanical and thermal designs are optimized simultaneously in this study by coupling 3D multi-physical behavior models to multi-objective heuristic optimization algorithms. Heat generation at various loading and ambient conditions are estimated with a physics-based, fractional-order battery cell-level model, which is extrapolated to a module that further accounts for the interconnected cells’ heat transfer phenomena. Several key performance indicators such as the surface temperature increase, the temperature variations on the cells, and heat uniformity within the module are recorded. For the air-cooled study case, the proposed coupled framework performs more than 250 module evaluations in a relatively short time for the whole available electro-thermal-mechanical design space, thereby ensuring global optimal results in the final design. The optimal module design proposed by this method is built in this work, and it is experimentally evaluated with a module composed of 12 series-connected Li-ion NMC/C 43Ah prismatic battery cells. The performance is validated at various conditions, which is achieved by accounting the thermal efficiency and pressure drop with regard to power consumption improvements. The validations presented in this study verify the applicability and overall efficiency of the proposed methodology, as well as paves the way toward better energy and cost-efficient battery systems.
Full article
(This article belongs to the Special Issue EVS36—International Electric Vehicle Symposium and Exhibition (California, USA))
Open AccessArticle
Comparative Life Cycle Assessment of Battery and Fuel Cell Electric Cars, Trucks, and Buses
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Anne Magdalene Syré, Pavlo Shyposha, Leonard Freisem, Anton Pollak and Dietmar Göhlich
World Electr. Veh. J. 2024, 15(3), 114; https://doi.org/10.3390/wevj15030114 (registering DOI) - 15 Mar 2024
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Addressing the pressing challenge of global warming, reducing greenhouse gas emissions in the transportation sector is a critical imperative. Battery and fuel cell electric vehicles have emerged as promising solutions for curbing emissions in this sector. In this study, we conducted a comprehensive
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Addressing the pressing challenge of global warming, reducing greenhouse gas emissions in the transportation sector is a critical imperative. Battery and fuel cell electric vehicles have emerged as promising solutions for curbing emissions in this sector. In this study, we conducted a comprehensive life cycle assessment (LCA) for typical passenger vehicles, heavy-duty trucks, and city buses using either proton-exchange membrane fuel cells or Li-ion batteries with different cell chemistries. To ensure accuracy, we supplemented existing studies with data from the literature, particularly for the recycling phase, as database limitations were encountered. Our results highlight that fuel cell and battery systems exhibit large emissions in the production phase. Recycling can significantly offset some of these emissions, but a comparison of the technologies examined revealed considerable differences. Overall, battery electric vehicles consistently outperform fuel cell electric vehicles regarding absolute greenhouse gas emissions. Hence, we recommend prioritizing battery electric over fuel cell vehicles. However, deploying fuel cell electric vehicles could become attractive in a hydrogen economy scenario where other factors, e.g., the conversion and storage of surplus renewable electricity via electrolysis, become important.
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Vehicle-Integrated Photovoltaics—A Case Study for Berlin
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Philipp Hoth, Ludger Heide, Alexander Grahle and Dietmar Göhlich
World Electr. Veh. J. 2024, 15(3), 113; https://doi.org/10.3390/wevj15030113 - 15 Mar 2024
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Recent developments in vehicle-integrated photovoltaics (VIPV) offer prospects for enhancing electric vehicle range, lowering operating costs, and supporting carbon-neutral transport, particularly in urban settings. This study evaluates the solar energy potential of parking spaces in Berlin, considering challenges like building and tree shading
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Recent developments in vehicle-integrated photovoltaics (VIPV) offer prospects for enhancing electric vehicle range, lowering operating costs, and supporting carbon-neutral transport, particularly in urban settings. This study evaluates the solar energy potential of parking spaces in Berlin, considering challenges like building and tree shading using digital surface models and weather data for solar simulations. Utilizing open datasets and software, the analysis covered 48,827 parking spaces, revealing that VIPV could extend vehicle range by 7 to 14 km per day, equating to a median annual increase of 2527 km. The findings suggest median yearly cost savings of 164 euros from reduced grid charging. However, the environmental benefits of solar vehicle charging were found to be less than those of traditional grid-connected photovoltaic systems. The study introduces a method to pinpoint parking spaces that are most suitable for solar charging.
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(This article belongs to the Topic Zero Carbon Vehicles and Power Generation)
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An Investigation of Representative Customer Load Collectives in the Development of Electric Vehicle Drivetrain Durability
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Mingfei Li, Fabian Kai-Dietrich Noering, Yekta Öngün, Michael Appelt and Roman Henze
World Electr. Veh. J. 2024, 15(3), 112; https://doi.org/10.3390/wevj15030112 - 15 Mar 2024
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To ensure the precise dimensioning and effective testing of drivetrain components, it is crucial to have a thorough understanding of customer requirements, with a particular emphasis on customer stress on these components. An accurate interpretation of customer data is essential for determining representative
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To ensure the precise dimensioning and effective testing of drivetrain components, it is crucial to have a thorough understanding of customer requirements, with a particular emphasis on customer stress on these components. An accurate interpretation of customer data is essential for determining representative customer requirements, such as load collectives. The automobile industry has faced challenges in analyzing large amounts of customer driving data to obtain representative load collectives as target values in durability design. However, due to technical limitations and cost constraints, collecting data from a large sample size is not feasible. The ongoing digitalization of the automotive industry, driven by an increasing number of connected vehicles, enhances data-based and customer-oriented development. This paper investigates representative customer load collectives using cloud data from over 40,000 customer vehicles to lay the groundwork for realizing robust requirement engineering. A systematic method for analyzing big data on the cloud was introduced. The derived component-specific damage distribution from these collectives adopts a unique approach, utilizing the 1% vehicle term instead of the common 1% customer term to represent typical customer stress. This study shows that the driven mileage and the number of vehicles are crucial factors in 1% vehicle analysis. An analysis of the characteristics of the 1% vehicle is conducted, followed by an exploration to determine the required vehicle quantity for obtaining stable results. The shape parameter of the damage distribution determines the necessary number of vehicles for a reliable conclusion. Additionally, a comparative analysis of market-specific customer requirements between the US and Europe is presented, and real usage differences in customer operations are explained using an operating point frequency heatmap. The information presented in this paper provides valuable input for optimizing durability design and conducting efficient, customer-oriented tests, resulting in significant reductions in development time and costs.
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(This article belongs to the Topic Advanced Electric Vehicle Technology, 2nd Volume)
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Marketing Strategy and Preference Analysis of Electric Cars in a Developing Country: A Perspective from the Philippines
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John Robin R. Uy, Ardvin Kester S. Ong and Josephine D. German
World Electr. Veh. J. 2024, 15(3), 111; https://doi.org/10.3390/wevj15030111 - 14 Mar 2024
Abstract
The wide-scale integration of electric vehicles (EVs) in developed countries represents a significant technological innovation and a step toward reducing carbon emissions from transportation. Conversely, in developing nations like the Philippines, the adoption and availability of EVs have not been as rapid or
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The wide-scale integration of electric vehicles (EVs) in developed countries represents a significant technological innovation and a step toward reducing carbon emissions from transportation. Conversely, in developing nations like the Philippines, the adoption and availability of EVs have not been as rapid or widespread compared to other countries. In identifying this gap, this study delved into the preferences and factors influencing Filipino consumers’ willingness to purchase EVs. The study gathered 311 valid responses utilizing conjoint analysis with an orthogonal approach to assess the attributes influencing customers’ purchase decisions. Conjoint analysis tools such as IBM SPSS v25 statistics were utilized to infer consumer preference. The results determined that cost is the primary concern for consumers by a considerable margin; followed by battery type and charging method; along with the type of EV, driving range, and charging speed; and most minor concern is regenerative brakes. Therefore, there is an apparent sensitivity to price and technology. This study is the first to apply conjoint analysis to the Philippine market, delivering in-depth consumer preference insights that can help manufacturers and policymakers customize their approach to making EVs more attractive and more viable in less developed markets. The results suggest that a targeted effort to overcome cost barriers and improve technological literacy among prospective buyers should be productive for speeding up EV adoption in the Philippines. The results could be extended in future research to a broader assessment of socioeconomic and environmental benefits, laying out a broader plan for promoting sustainable solutions in transportation.
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(This article belongs to the Topic Emerging Trends in Electric Vehicles, Smart Grids and Smart Cities)
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Design and Implementation of a Wireless Power Transfer System for Electric Vehicles
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Vekil Sari
World Electr. Veh. J. 2024, 15(3), 110; https://doi.org/10.3390/wevj15030110 - 12 Mar 2024
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Wireless power transfer (WPT) systems, which have been around for decades, have recently become very popular with the widespread use of electric vehicles (EVs). In this study, an inductive coupling WPT system with a series–series compensation topology was designed and implemented for use
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Wireless power transfer (WPT) systems, which have been around for decades, have recently become very popular with the widespread use of electric vehicles (EVs). In this study, an inductive coupling WPT system with a series–series compensation topology was designed and implemented for use in EVs. Initially, a 3D Maxwell (ANSYS Electromagnetics Suite 18) model of the system was generated. The impact of individual parameters on the coupling coefficient was analyzed through systematic variations in each parameter’s values. As a result, a system with a higher coupling coefficient was obtained. Using this system, three distinct load cases were investigated for their efficiency in the Simplorer (ANSYS Electromagnetics Suite 18) circuit. Subsequently, a prototype of the system was constructed, and the experimental results were compared with the model’s results. This study shows that both the output power and the efficiency of the system increase as the load resistance increases. The results obtained in this study are anticipated to offer valuable insights for the enhancement of WPT system design.
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(This article belongs to the Special Issue Wireless Power Transfer Technology for Electric Vehicles)
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Numerical Study of Reinforced Aluminum Composites for Steering Knuckles in Last-Mile Electric Vehicles
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Carlos Santana, Luis Reyes-Osorio, Jesus Orona-Hinojos, Lizbeth Huerta, Alfredo Rios and Patricia Zambrano-Robledo
World Electr. Veh. J. 2024, 15(3), 109; https://doi.org/10.3390/wevj15030109 - 10 Mar 2024
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The steering knuckle is a critical component of the suspension and steering drive systems of electric vehicles. The electrification of last-mile vehicles presents a challenge in terms of cost, driving range and compensation of battery weight. This work presents a numerical methodology to
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The steering knuckle is a critical component of the suspension and steering drive systems of electric vehicles. The electrification of last-mile vehicles presents a challenge in terms of cost, driving range and compensation of battery weight. This work presents a numerical methodology to evaluate 60XX series aluminum metal matrix composites (AMMCs) with reinforcement ceramic particles for steering knuckle components in medium heavy-duty last-mile cargo vehicles. The use of AMMCs provides lightweight knuckles with sufficient strength, stiffness and safety conditions for electrical vehicle cargo configurations. The numerical study includes three aluminum alloys, two AMMC alloys and an Al 6061-T6 alloy as reference materials. The medium-duty heavy vehicle class < 12 t, such as electrical vehicle cargo configurations, is considered for the numerical study (class 1–4). The maximum von Mises stress for class 4 AMMC alloys exceeds 350 MPa, limited by fracture toughness. The weight reduction is about 65% when compared with commercial cast iron. Moreover, Al 6061-T6 alloys exhibit stress values surpassing 300 MPa, constraining their suitability for heavier vehicles. The study proposes assessing the feasibility of implementing AMMC alloys in critical components like steering knuckles and suggests solutions to enhance conventional vehicle suspension systems and overcome associated challenges. It aims to serve as a lightweight design guide, offering insights into stress variations with differing load conditions across various cargo vehicles.
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Indicators of Potential Use of Electric Vehicles in Urban Areas: A Real-Life Survey-Based Study in Hail, Saudi Arabia
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Abdulmohsen A. Al-fouzan and Radwan A. Almasri
World Electr. Veh. J. 2024, 15(3), 108; https://doi.org/10.3390/wevj15030108 - 09 Mar 2024
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This study aimed to uncover the attitudes, preferences, and perceptions of Hail residents toward electric vehicles (EVs) by employing a real-life survey-based approach. This paper presents an in-depth analysis of the potential adoption and impact of EVs to clarify the picture of the
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This study aimed to uncover the attitudes, preferences, and perceptions of Hail residents toward electric vehicles (EVs) by employing a real-life survey-based approach. This paper presents an in-depth analysis of the potential adoption and impact of EVs to clarify the picture of the transition from using traditional vehicles to using EVs in Hail City, Saudi Arabia. Hail is rapidly becoming a more urbanized city; in the past few decades, the city’s area has expanded from 3242 to 17,526 hectares, and its population has increased dramatically from 82,900 in 1984 to 344,111 in present day. As a result, the city is facing increasingly difficult challenges related to rising vehicle emissions and environmental degradation. A survey was conducted among a diverse group of 346 participants. The survey results show an average of 3.15 cars per family, which indicates a strong connection with personal vehicles. The survey provides a comprehensive picture of the respondents’ socioeconomic background, indicating an average household size of 5.8 people and an average monthly income of SR 13,350. The key findings from the survey reveal that approximately 52.3% of the respondents have 3–4 family members, and nearly half of the families own one or two cars. Government employees formed a major proportion of the respondents. The results show a significant inclination toward EVs, with 78.6% of the participants being aware of EV charging stations and 37.9% expressing a positive attitude towards switching to electric vehicles. Despite this, a large majority (88.7%) have never driven an electric car. The respondents’ driving habits are further explored in the survey, which reveals an average of 2.1 h of daily driving. Furthermore, the respondents disclosed an average weekly fuel expenditure of SR 235. The results also highlight that the average weekly cost for fuel varies among the respondents, with most respondents spending no more than SR 300 per week. Additionally, this study examined the daily vehicle mileage, revealing that 37.9% of the respondents have a daily mileage of 51 to 100 km, which impacts the planning of charging station capacities and locations. The findings suggest a growing interest in EVs and highlight the need for strategic infrastructure development to support the anticipated surge in EV adoption.
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Open AccessArticle
Research on Vehicle Frame Optimization Methods Based on the Combination of Size Optimization and Topology Optimization
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Qun He, Xinning Li, Wenjie Mao, Xianhai Yang and Hu Wu
World Electr. Veh. J. 2024, 15(3), 107; https://doi.org/10.3390/wevj15030107 - 09 Mar 2024
Abstract
The efficient development of electric vehicles is essential to drive society towards sustainable development. Designing a lightweight frame is a key strategy to improve the economy and environment, increase energy efficiency, and reduce carbon emissions. Taking an automatic loading and unloading mixer truck
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The efficient development of electric vehicles is essential to drive society towards sustainable development. Designing a lightweight frame is a key strategy to improve the economy and environment, increase energy efficiency, and reduce carbon emissions. Taking an automatic loading and unloading mixer truck as the research object, a force analysis of its frame was conducted under six typical working conditions. A size optimization method based on a hybrid model of the Kriging model and the analytic hierarchy process (AHP) is proposed. An approximate model of the mass and maximum stress of the frame was established using the Kriging model, and the Kriging model was optimized by using the multi-objective genetic optimization algorithm and the AHP method. Meanwhile, topology optimization was introduced to improve the structural performance of the frame and reduce its weight. The optimization results show that the overall weight of the frame is reduced by 11.96% compared to the pre-optimization period, though it still meets the material performance specifications. By comparing the iterative curves of the single Kriging model with those of the AHP model, it can be seen that the initial optimization efficiency of the hybrid model is about twice as much as that of the AHP model, and the final optimization result is improved by about 3.6% compared with the Kriging model. This validates the hybrid model as an effective tool for the multi-objective optimization of electric vehicle frames, providing more efficient and accurate optimization results for frame design.
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(This article belongs to the Topic Advanced Electric Vehicle Technology, 2nd Volume)
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Design and Implementation of Improved Gate Driver Circuit for Sensorless Permanent Magnet Synchronous Motor Control
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Indra Ferdiansyah and Tsuyoshi Hanamoto
World Electr. Veh. J. 2024, 15(3), 106; https://doi.org/10.3390/wevj15030106 - 09 Mar 2024
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Reliable motor control is important for electric vehicle applications. The control process requires accurate measurements of the current and rotor position information to establish correct motor control design, particularly in sensorless permanent magnet synchronous motor control systems. Practical issues regarding the motor control
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Reliable motor control is important for electric vehicle applications. The control process requires accurate measurements of the current and rotor position information to establish correct motor control design, particularly in sensorless permanent magnet synchronous motor control systems. Practical issues regarding the motor control circuit, such as the effects of parasitic element behavior on the switching components in the insulated gate bipolar transistor-driven inverter, were discussed in this study. It analyzed the effects of parasitic elements that can cause the ringing of switching losses and affect the spike of the signal in the motor current, which must be avoided in the implementation of motor control. The gate driver circuit topology was improved to reduce this effect in motor control devices. The proposed gate driver circuit design with the ringing suppression circuit configuration achieved good performance by keeping the signal spike at less than 10% in the motor current. Furthermore, a signal spike or noise was not observed in the estimation results of rotor position when using current information as the parameter control process. Both conditions were verified by experiments on the designed motor control devices. Under these conditions, signal precision can be achieved in motor control.
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(This article belongs to the Special Issue Permanent Magnet Motors and Driving Control for Electric Vehicles)
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Open AccessArticle
Reuse of Retired Lithium-Ion Batteries (LIBs) for Electric Vehicles (EVs) from the Perspective of Extended Producer Responsibility (EPR) in Taiwan
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Yu-Sen Chuang, Hong-Ping Cheng and Chin-Chi Cheng
World Electr. Veh. J. 2024, 15(3), 105; https://doi.org/10.3390/wevj15030105 - 08 Mar 2024
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Over the last 50 years since Whittingham created the world’s first lithium-ion battery (LIB) in 1970, LIBs have continued to develop and have become mainstream for electric vehicle (EV) batteries. However, when an LIB for an EV reaches 80% of its state of
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Over the last 50 years since Whittingham created the world’s first lithium-ion battery (LIB) in 1970, LIBs have continued to develop and have become mainstream for electric vehicle (EV) batteries. However, when an LIB for an EV reaches 80% of its state of health (SOH), although it still retains about 80% of its capacity, it is no longer suitable for use in general EVs and must be retired. This is problematic because not only is a retired LIB still viable for use and not totally obsolete, if not properly disposed of, a retired LIB may cause environmental pollution on top of being a waste of resources. Therefore, the reuse of retired LIBs from EVs is increasingly important. This paper uses circular economy (CE) and extended producer responsibility (EPR) as a theoretical basis to deal with the disposal of retired LIBs from EVs in Taiwan from legal, technical, and economic perspectives, and hopes to provide suggestions for the reuse of retired LIBs from EVs in Taiwan.
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Open AccessArticle
Inclined Obstacle Recognition and Ranging Method in Farmland Based on Improved YOLOv8
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Xianghai Yan, Bingxin Chen, Mengnan Liu, Yifan Zhao and Liyou Xu
World Electr. Veh. J. 2024, 15(3), 104; https://doi.org/10.3390/wevj15030104 - 08 Mar 2024
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Unmanned tractors under ploughing conditions suffer from body tilting, violent shaking and limited hardware resources, which can reduce the detection accuracy of unmanned tractors for field obstacles. We optimize the YOLOv8 model in three aspects: improving the accuracy of detecting tilted obstacles, computational
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Unmanned tractors under ploughing conditions suffer from body tilting, violent shaking and limited hardware resources, which can reduce the detection accuracy of unmanned tractors for field obstacles. We optimize the YOLOv8 model in three aspects: improving the accuracy of detecting tilted obstacles, computational reduction, and adding a visual ranging mechanism. By introducing Funnel ReLU, a self-constructed inclined obstacle dataset, and embedding an SE attention mechanism, these three methods improve detection accuracy. By using MobileNetv2 and Bi FPN, computational reduction, and adding camera ranging instead of LIDAR ranging, the hardware cost is reduced. After completing the model improvement, comparative tests and real-vehicle validation are carried out, and the validation results show that the average detection accuracy of the improved model reaches 98.84% of the mAP value, which is 2.34% higher than that of the original model. The computation amount of the same image is reduced from 2.35 billion floating-point computations to 1.28 billion, which is 45.53% less than the model computation amount. The monitoring frame rate during the movement of the test vehicle reaches 67 FPS, and the model meets the performance requirements of unmanned tractors under normal operating conditions.
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Open AccessArticle
Predicting the Torque Demand of a Battery Electric Vehicle for Real-World Driving Maneuvers Using the NARX Technique
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Muhammed Alhanouti and Frank Gauterin
World Electr. Veh. J. 2024, 15(3), 103; https://doi.org/10.3390/wevj15030103 - 08 Mar 2024
Abstract
An identification technique is proposed to create a relation between the accelerator pedal position and the corresponding driving moment. This step is beneficial to replace the complex physical model of the vehicle control unit, especially when the sufficient information needed to model certain
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An identification technique is proposed to create a relation between the accelerator pedal position and the corresponding driving moment. This step is beneficial to replace the complex physical model of the vehicle control unit, especially when the sufficient information needed to model certain functionalities of the vehicle control unit are unavailable. We utilized the nonlinear autoregressive exogenous model to regenerate the electric motor torque demand, given the accelerator pedal position, the motor’s angular speed, and the vehicle’s speed. This model proved to be extremely efficient in representing this highly complex relationship. The data employed for the identification process were chosen from an actual three-dimensional route with sudden changes of a dynamic nature in the driving mode, different speed limits, and elevations, as an attempt to thoroughly cover the driving moment scope based on the alternation of the given inputs. Analyzing the selected route data points showed the widespread coverage of the motor’s operational scope compared to a standard driving cycle. The training outcome revealed that linear modeling is inadequate for identifying the targeted system, and has a substantial estimation error. Adding the nonlinearity feature to the model led to an exceptionally high accuracy for the estimation and validation datasets. The main finding of this work is that the combined model from the nonlinear autoregressive exogenous and the sigmoid network enables the accurate modeling of highly nonlinear dynamic systems. Accordingly, the maximum absolute estimation error for the motor’s moment was less than 10 Nm during the real-world driving maneuver. The highest errors are found around the maximum motor’s moment. Finally, the model is validated with measurements from an actual field test maneuver. The identified model predicted the driving moment with a correlation of 0.994.
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(This article belongs to the Special Issue Design, Modelling and Control Strategies for Hybrid and Electric Vehicles)
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Open AccessArticle
BI-TST_YOLOv5: Ground Defect Recognition Algorithm Based on Improved YOLOv5 Model
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Jiahao Qin, Xiaofeng Yang, Tianyi Zhang and Shuilan Bi
World Electr. Veh. J. 2024, 15(3), 102; https://doi.org/10.3390/wevj15030102 - 07 Mar 2024
Abstract
Pavement defect detection technology stands as a pivotal component within intelligent driving systems, demanding heightened precision and rapid detection rates. Addressing the complexities arising from diverse defect types and intricate backgrounds in visual sensing, this study introduces an enhanced approach to augment the
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Pavement defect detection technology stands as a pivotal component within intelligent driving systems, demanding heightened precision and rapid detection rates. Addressing the complexities arising from diverse defect types and intricate backgrounds in visual sensing, this study introduces an enhanced approach to augment the network structure and activation function within the foundational YOLOv5 algorithm. Initially, modifications to the YOLOv5′s architecture incorporate an adjustment to the Leaky ReLU activation function, thereby enhancing regression stability and accuracy. Subsequently, the integration of bi-level routing attention into the network’s head layer optimizes the attention mechanism, notably improving overall efficiency. Additionally, the replacement of the YOLOv5 backbone layer’s C3 module with the C3-TST module enhances initial convergence efficiency in target detection. Comparative analysis against the original YOLOv5s network reveals a 2% enhancement in map50 and a 1.8% improvement in F1, signifying an overall advancement in network performance. The initial convergence rate of the algorithm has been improved, and the accuracy and operational efficiency have also been greatly improved, especially on models with small-scale training sets.
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(This article belongs to the Special Issue Advanced Vehicle System Dynamics and Control)
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A Review of Capacity Allocation and Control Strategies for Electric Vehicle Charging Stations with Integrated Photovoltaic and Energy Storage Systems
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Ming Yao, Danning Da, Xinchun Lu and Yuhang Wang
World Electr. Veh. J. 2024, 15(3), 101; https://doi.org/10.3390/wevj15030101 - 06 Mar 2024
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Electric vehicles (EVs) play a major role in the energy system because they are clean and environmentally friendly and can use excess electricity from renewable sources. In order to meet the growing charging demand for EVs and overcome its negative impact on the
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Electric vehicles (EVs) play a major role in the energy system because they are clean and environmentally friendly and can use excess electricity from renewable sources. In order to meet the growing charging demand for EVs and overcome its negative impact on the power grid, new EV charging stations integrating photovoltaic (PV) and energy storage systems (ESSs) have emerged. However, the output of solar PV systems and the charging demand of EVs are both characterized by uncertainty and dynamics. These may lead to large power fluctuations in the grid and frequent alternation of peak and valley loads, which are not conducive to the stability of the distribution network. The study of reasonable capacity configuration and control strategy issues is conducive to the efficient use of solar energy, fast charging of EVs, stability of the distribution network, and maximization of the economic benefits of the system. In this paper, the concept, advantages, capacity allocation methods and algorithms, and control strategies of the integrated EV charging station with PV and ESSs are reviewed. On the basis of the above research, the current problems and challenges are analyzed, and corresponding solutions and ideas are proposed.
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(This article belongs to the Special Issue Electric Vehicles and Charging Facilities for a Sustainable Transport Sector)
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Electric Trolley Prototype for Goods and People Transport on Ziplines
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Ettore Bianco, Claudio Giannuzzi, Andrés Felipe Corredor Pablos, Vicente Alfredo Peña Reyes and Davide Berti Polato
World Electr. Veh. J. 2024, 15(3), 100; https://doi.org/10.3390/wevj15030100 - 06 Mar 2024
Abstract
The increasing demand for efficient and cost-effective transportation solutions has led to the exploration of unconventional modes of transportation, such as ziplines. This paper presents the development of an electric prototype for a trolley that can be used for the simultaneous transport of
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The increasing demand for efficient and cost-effective transportation solutions has led to the exploration of unconventional modes of transportation, such as ziplines. This paper presents the development of an electric prototype for a trolley that can be used for the simultaneous transport of goods and people on ziplines. The prototype is designed with a modular system that allows for easy customization based on the cargo’s weight and size. Two lightweight Maxon motors have been integrated for traction purposes with two Maxon inverters and a low-voltage swappable battery pack. The trolley’s chassis is made of lightweight materials, such as aluminum, making it highly maneuverable and capable of traveling at high speeds. The lightweight permits the operators to detach the trolley from the zipline when needed. The prototype’s traction and braking systems are controlled through a user-friendly interface, making it easy to operate, and with the possibility of a robust and automatic routine for goods transport. In this article, we present the simulation for the design and testing of the prototype, as well as its potential applications in various industries, such as mining, agriculture, and emergency services. Our results show that the prototype is a viable solution for zipline-based transportation, with high efficiency and performance standards. Further research and development are being conducted to optimize the prototype’s performance and expand its applications.
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(This article belongs to the Special Issue Design, Modelling and Control Strategies for Hybrid and Electric Vehicles)
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A Review of Decision-Making and Planning for Autonomous Vehicles in Intersection Environments
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Shanzhi Chen, Xinghua Hu, Jiahao Zhao, Ran Wang and Min Qiao
World Electr. Veh. J. 2024, 15(3), 99; https://doi.org/10.3390/wevj15030099 - 06 Mar 2024
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Decision-making and planning are the core aspects of autonomous driving systems. These factors are crucial for improving the safety, driving experience, and travel efficiency of autonomous vehicles. Intersections are crucial nodes in urban road traffic networks. The objective of this study is to
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Decision-making and planning are the core aspects of autonomous driving systems. These factors are crucial for improving the safety, driving experience, and travel efficiency of autonomous vehicles. Intersections are crucial nodes in urban road traffic networks. The objective of this study is to comprehensively review the latest issues and research progress in decision-making and planning for autonomous vehicles in intersection environments. This paper reviews the research progress in the behavioral prediction of traffic participants in terms of machine learning-based behavioral prediction, probabilistic model behavioral prediction, and mixed-method behavioral prediction. Then, behavioral decision-making is divided into reactive decision-making, learning decision-making, and interactive decision-making, each of which is analyzed. Finally, a comparative analysis of motion planning and its applications is performed from a methodological viewpoint, including search, sampling, and numerical methods. First, key issues and major research progress related to end-to-end decision-making and path planning are summarized and analyzed. Second, the impact of decision-making and path planning on the intelligence level of autonomous vehicles in intersecting environments is discussed. Finally, future development trends and technical challenges are outlined.
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Systemic Evaluation of PV Self-Consumption Optimization Using Electric Vehicles
by
Kirstin Ganz, Timo Kern and Michael Hinterstocker
World Electr. Veh. J. 2024, 15(3), 98; https://doi.org/10.3390/wevj15030098 - 05 Mar 2024
Abstract
The shift to electric transportation is crucial to fighting climate change. However, Germany’s goal of 15 million electric vehicles (EVs) by 2030 remains distant. Therefore, enhancing their economic viability is essential to promoting EV adoption. One promising option to increase the economics for
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The shift to electric transportation is crucial to fighting climate change. However, Germany’s goal of 15 million electric vehicles (EVs) by 2030 remains distant. Therefore, enhancing their economic viability is essential to promoting EV adoption. One promising option to increase the economics for the user is PV self-consumption optimization using smart charging EVs. Yet, more research is needed to explore the use case’s impacts on the German/European energy systems. Therefore, PV self-consumption optimization using EVs is integrated into an energy system model, assessing its impact on the energy system in 2030. For this purpose, the use case is modeled for different groups of people—personas—which are defined in a way that creates a diverse set of personas reflecting the distribution of different statistical values within Germany. The modified (dis)charging profiles are then aggregated and integrated into the energy system model. With a high implementation of PV self-consumption optimization in Germany in 2030, a positive system effect (with a system cost reduction of 53 million EUR/a) can be observed with a lower need for further storage and less curtailment of renewable energies (RES). Furthermore, the market values for RES increase by 0.7%, which fosters the integration of RES.
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(This article belongs to the Special Issue Power and Energy Systems for E-mobility)
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