World Electric Vehicle Journal

2 pages, 535 KiB

Open AccessCorrection

Correction: El Hafdaoui et al. Energy and Environmental National Assessment of Alternative Fuel Buses in Morocco. World Electr. Veh. J. 2023, 14, 105

by Hamza El Hafdaoui, Faissal Jelti, Ahmed Khallaayoun and Kamar Ouazzani

World Electr. Veh. J. 2024, 15(10), 457; https://doi.org/10.3390/wevj15100457 - 9 Oct 2024

Abstract

In the original publication [...] Full article

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17 pages, 3232 KiB

Open AccessArticle

Impact of Mixed-Vehicle Environment on Speed Disparity as a Measure of Safety on Horizontal Curves

by Tahmina Sultana and Yasser Hassan

World Electr. Veh. J. 2024, 15(10), 456; https://doi.org/10.3390/wevj15100456 - 9 Oct 2024

Abstract

Due to the transition of vehicle fleets from conventional driver-operated vehicles (DVs) to connected vehicles (CVs) and/or automated vehicles (AVs), vehicles with different technologies will soon operate on the same roads in a mixed-vehicle environment. Although a major goal of vehicle connectivity and [...] Read more.

Due to the transition of vehicle fleets from conventional driver-operated vehicles (DVs) to connected vehicles (CVs) and/or automated vehicles (AVs), vehicles with different technologies will soon operate on the same roads in a mixed-vehicle environment. Although a major goal of vehicle connectivity and automation is to improve traffic safety, negative safety impacts may persist in the mixed-vehicle environment. Speed disparity measures have been shown in the literature to be related to safety performance. Therefore, speed disparity measures are derived from the expected speed distributions of different vehicle technologies and are used as surrogate measures to assess the safety of mixed-vehicle environments and identify the efficacy of prospective countermeasures. This paper builds on speed models in the literature to predict the speed behavior of CVs, AVs, and DVs on horizontal curves on freeways and major arterials. The paper first proposes a methodology to determine speed disparity measures on horizontal curves without any control in terms of speed limit. The impact of speed limit or advisory speed, as a safety countermeasure, is modeled and assessed using different strategies to set the speed limit. The results indicated that the standard deviation of the speeds of all vehicles (

σ_{c}

) in a mixed environment would increase on arterial roads under no control compared to the case of DV-only traffic. This speed disparity can be reduced using an advisory speed as a safety countermeasure to decrease the adverse safety impacts in this environment. Moreover, it was shown that compared to the practice of a constant speed limit based on road classification, the advisory speed is more effective when it is based on the speed behavior of various vehicle types. Full article

(This article belongs to the Special Issue Vehicle Safe Motion in Mixed Vehicle Technologies Environment)

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17 pages, 7660 KiB

Open AccessArticle

Design of a Low-Cost AI System for the Modernization of Conventional Cars

by Wilver Auccahuasi, Kitty Urbano, Sandra Meza, Luis Romero-Echevarria, Arlich Portillo-Allende, Karin Rojas, Jorge Figueroa-Revilla, Giancarlo Sanchez-Atuncar, Sergio Arroyo and Percy Junior Castro-Mejia

World Electr. Veh. J. 2024, 15(10), 455; https://doi.org/10.3390/wevj15100455 - 8 Oct 2024

Abstract

Artificial intelligence techniques are beginning to be implemented in most areas. In the particular case of automobiles, new cars include integrated applications, such as cameras in different configurations, including in the rear of the car to provide assistance while reversing, as well as [...] Read more.

Artificial intelligence techniques are beginning to be implemented in most areas. In the particular case of automobiles, new cars include integrated applications, such as cameras in different configurations, including in the rear of the car to provide assistance while reversing, as well as front and side cameras; these applications also include different configurations of sensors that provide information to the driver, such as objects approaching from different directions, such as from the front and sides. In this paper, we propose a practical and low-cost methodology to provide solutions using artificial intelligence techniques, as is the purpose of YOLO architecture, version 3, using hardware based on Nvidia’s Jetson TK1 architecture, and configurations in conventional cars. The results that we present demonstrate that these technologies can be applied in conventional cars, working with independent power to avoid causing problems in these cars, and we evaluate their application in the detection of people and cars in different situations, which allows information to be provided to the driver while performing maneuvers. The methodology that we provide can be replicated and scaled according to needs. Full article

(This article belongs to the Special Issue Deep Learning Applications for Electric Vehicles)

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18 pages, 5215 KiB

Open AccessArticle

Cascaded Vehicle State Estimation Method of 4WIDEVs Considering System Delay and Noise

by Zibin Yang, Xiang Liu and Qiu Xia

World Electr. Veh. J. 2024, 15(10), 454; https://doi.org/10.3390/wevj15100454 - 7 Oct 2024

Abstract

Considering the negative effects of time delay and noise on vehicle state estimation, a cascaded estimation means for the vehicle sideslip angle is proposed utilizing the ODUKF algorithm. To achieve strong-correlation decoupling between state variables and model interference of the EDWM, an augmented [...] Read more.

Considering the negative effects of time delay and noise on vehicle state estimation, a cascaded estimation means for the vehicle sideslip angle is proposed utilizing the ODUKF algorithm. To achieve strong-correlation decoupling between state variables and model interference of the EDWM, an augmented EDWM was constructed by introducing the tire relaxation length dynamic equation, which enables the precise model relationship between the longitudinal and transverse tire force relaxation length to be constructed while also achieving the decoupling of the system state from the unknown input. To achieve a vehicle driving state estimation, a hierarchical estimation architecture was adopted to design a cascading estimation method for the vehicle driving state. By using tire force estimation values as input for the vehicle driving state estimation, the required vehicle body postures can be estimated. At the same time, facing the problems of system delay and noise, an estimator derived from the ODUKF is designed by combining the model and cascade estimation strategy. The simulation comparative analysis and quantitative statistical results under multiple operating conditions provide evidence that the developed means effectively heighten the estimation accurateness and real-time performance while considering system time delay and noise. Full article

(This article belongs to the Special Issue Dynamic Modeling, Identification, and Advanced Control of Intelligent Electric Vehicles)

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33 pages, 10372 KiB

Open AccessArticle

Adaptive Multi-Agent Reinforcement Learning for Optimizing Dynamic Electric Vehicle Charging Networks in Thailand

by Pitchaya Jamjuntr, Chanchai Techawatcharapaikul and Pannee Suanpang

World Electr. Veh. J. 2024, 15(10), 453; https://doi.org/10.3390/wevj15100453 - 6 Oct 2024

Abstract

The rapid growth of electric vehicles (EVs) necessitates efficient management of dynamic EV charging networks to optimize resource utilization and enhance service reliability. This paper explores the application of adaptive multi-agent reinforcement learning (MARL) to address the complexities of EV charging infrastructure in [...] Read more.

The rapid growth of electric vehicles (EVs) necessitates efficient management of dynamic EV charging networks to optimize resource utilization and enhance service reliability. This paper explores the application of adaptive multi-agent reinforcement learning (MARL) to address the complexities of EV charging infrastructure in Thailand. By employing MARL, multiple autonomous agents learn to optimize charging strategies based on real-time data by adapting to fluctuating demand and varying electricity prices. Building upon previous research that applied MARL to static network configurations, this study extends the application to dynamic and real-world scenarios, integrating real-time data to refine agent learning processes and also evaluating the effectiveness of adaptive MARL in maximizing rewards and improving operational efficiency compared to traditional methods. Experimental results indicate that MARL-based strategies increased efficiency by 20% and reduced energy costs by 15% relative to conventional algorithms. Key findings demonstrate the potential of extending MARL in transforming EV charging network management, highlighting its benefits for stakeholders, including EV owners, operators, and utility providers. This research contributes insights into advancing electric mobility and energy management in Thailand through innovative AI-driven approaches. The implications of this study include significant improvements in the reliability and cost-effectiveness of EV charging networks, fostering greater adoption of electric vehicles and supporting sustainable energy initiatives. Future research directions include enhancing MARL adaptability and scalability as well as integrating predictive analytics for proactive network optimization and sustainability. These advancements promise to further refine the efficacy of EV charging networks, ensuring that they meet the growing demands of Thailand’s evolving electric mobility landscape. Full article

(This article belongs to the Special Issue Electric Vehicles and Charging Facilities for a Sustainable Transport Sector)

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20 pages, 29723 KiB

Open AccessArticle

Optimized Right-Turn Pedestrian Collision Avoidance System Using Intersection LiDAR

by Soo-Yong Park and Seok-Cheol Kee

World Electr. Veh. J. 2024, 15(10), 452; https://doi.org/10.3390/wevj15100452 - 6 Oct 2024

Abstract

The incidence of right-turning pedestrian accidents is increasing in South Korea. Most of the accidents occur when a large vehicle is turning right, and the main cause of the accidents was found to be the driver’s limited field of vision. After these accidents, [...] Read more.

The incidence of right-turning pedestrian accidents is increasing in South Korea. Most of the accidents occur when a large vehicle is turning right, and the main cause of the accidents was found to be the driver’s limited field of vision. After these accidents, the government implemented a series of institutional measures with the objective of preventing such accidents. However, despite the institutional arrangements in place, pedestrian accidents continue to occur. We focused on the many limitations that autonomous vehicles, like humans, can face in such situations. To address this issue, we propose a right-turn pedestrian collision avoidance system by installing a LiDAR sensor in the center of the intersection to facilitate pedestrian detection. Furthermore, the urban road environment is considered, as this provides the optimal conditions for the model to perform at its best. During this research, we collected data on right-turn accidents using the CARLA simulator and ROS interface and demonstrated the effectiveness of our approach in preventing such incidents. Our results suggest that the implementation of this method can effectively reduce the incidence of right-turn accidents in autonomous vehicles. Full article

(This article belongs to the Special Issue EVS37—International Electric Vehicle Symposium and Exhibition (Seoul, Republic of Korea))

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15 pages, 13016 KiB

Open AccessArticle

Rapid Screening for Retired Batteries Based on Lithium-Ion Battery IC Curve Prediction

by Shuangming Duan, Zhiyu Yu, Junhui Li, Zhiqiang Zhao and Haojun Liu

World Electr. Veh. J. 2024, 15(10), 451; https://doi.org/10.3390/wevj15100451 - 4 Oct 2024

Abstract

In order to solve the issue of low efficiency in retired battery clustering, a method for quickly obtaining a charging curve and Incremental Capacity (IC) curve based on Convolutional Neural Networks (CNN) is proposed. By training a CNN model, the method enables accurate [...] Read more.

In order to solve the issue of low efficiency in retired battery clustering, a method for quickly obtaining a charging curve and Incremental Capacity (IC) curve based on Convolutional Neural Networks (CNN) is proposed. By training a CNN model, the method enables accurate prediction of complete IC curves and V-Q curves from local charging curves starting at any beginning. The prediction accuracy was validated using the Oxford battery degradation dataset, and transfer learning was conducted by fine-tuning the model trained on LCO batteries for use with LFP batteries, which reduced the RMSE of the estimation and validated the generalizability of the model. Peak parameters were extracted from both the original and predicted IC curves for clustering, and the t-test was applied to eliminate outliers, which significantly reduced the time required to obtain clustering features and improved clustering efficiency. Full article

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33 pages, 15119 KiB

Open AccessArticle

Optimized Integration of Medium-Voltage Multimegawatt DC Charging Stations: Concepts, Guidelines and Analysis

by Sumanta Biswas, Cham Kpu Gerald, Barbara Herndler, Daniel Stahleder, Yannick Wimmer and Markus Makoschitz

World Electr. Veh. J. 2024, 15(10), 450; https://doi.org/10.3390/wevj15100450 - 3 Oct 2024

Abstract

The integration of multimegawatt fast chargers into local distribution grids is becoming increasingly relevant due to recent initiatives to push for higher charging power, especially for applications like heavy-duty vehicles. However, the high-power capacity of these chargers, especially when multiple units operate simultaneously [...] Read more.

The integration of multimegawatt fast chargers into local distribution grids is becoming increasingly relevant due to recent initiatives to push for higher charging power, especially for applications like heavy-duty vehicles. However, the high-power capacity of these chargers, especially when multiple units operate simultaneously at specific locations, raises several important considerations for the optimal design and integration of multimegawatt fast chargers. These include, for example, power electronics architectures and dedicated designs, grid stability, and the incorporation of renewable energy systems. Thus, this paper provides a comprehensive analysis of the key factors influencing the optimal integration of these ultra-high-power chargers, looking into impacts on medium-voltage (MV) networks, the design considerations for medium-voltage power electronics in DC chargers, and the potential of renewable energy systems to offset grid demand. Additionally, this paper explores the potential high-level communication requirements necessary for efficient and reliable charger operation, including a proposal for a robust communication interface layer stack. This investigation aims to provide a holistic understanding of the challenges and opportunities associated with integrating multimegawatt fast chargers into existing power systems, offering insights into the enhancement of both performance and sustainability. Full article

(This article belongs to the Special Issue Data Exchange between Vehicle and Power System for Optimal Charging)

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15 pages, 966 KiB

Open AccessArticle

Orderly Charging Control of Electric Vehicles: A Smart Meter-Based Approach

by Ang Li, Yi Chen, Xinyu Xiang, Chuanzi Xu, Muchun Wan, Yingning Huo and Guangchao Geng

World Electr. Veh. J. 2024, 15(10), 449; https://doi.org/10.3390/wevj15100449 - 3 Oct 2024

Abstract

The charging load of electric vehicles (EV) is one of the most rapidly increasing loads in current power distribution systems. It may cause distribution transformer/feeder overload without proper coordination or control, especially in residential area where household load and EV charging load are [...] Read more.

The charging load of electric vehicles (EV) is one of the most rapidly increasing loads in current power distribution systems. It may cause distribution transformer/feeder overload without proper coordination or control, especially in residential area where household load and EV charging load are sharing transformer capacity. Existing smart meter-based orderly charging control (OCC) approaches commonly require costly but unreliable communication schemes to control EV charging behavior. In this work, a smart meter-based distributed controller is designed to establish a meter-to-EV communication interface with low cost and enhanced reliability, based on the state-of-the-art charging standard. An event-driven OCC algorithm is developed, and then, deployed in the data hub (concentrator) of the AMI with an easy-to-implement optimization formulation. The effectiveness of the proposed approach is validated using a numerical case study and a practical field test in Hangzhou, China. Both results indicate promising advantages of the proposed OCC approach in reducing the peak load of emerging EV charging demand by more than

30 %

. Full article

(This article belongs to the Special Issue Design, Modelling and Control Strategies for Hybrid and Electric Vehicles)

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11 pages, 1723 KiB

Open AccessArticle

Influence of an Automated Vehicle with Predictive Longitudinal Control on Mixed Urban Traffic Using SUMO

by Paul Heckelmann and Stephan Rinderknecht

World Electr. Veh. J. 2024, 15(10), 448; https://doi.org/10.3390/wevj15100448 - 30 Sep 2024

Abstract

In this paper, an approach to quantify the area of influence of an intelligent longitudinally controlled autonomous vehicle in an urban, mixed-traffic environment is proposed. The intelligent vehicle is executed with a predictive longitudinal control, which anticipates the future traffic scenario in order [...] Read more.

In this paper, an approach to quantify the area of influence of an intelligent longitudinally controlled autonomous vehicle in an urban, mixed-traffic environment is proposed. The intelligent vehicle is executed with a predictive longitudinal control, which anticipates the future traffic scenario in order to reduce unnecessary acceleration. The shown investigations are conducted within a simulated traffic environment of the city center of Darmstadt, Germany, which is carried out in the traffic simulation software “Simulation of Urban Mobility” (SUMO). The longitudinal dynamics of the not automated vehicles are considered with the Extended Intelligent Driver Model, which is an approach to simulate real human driver behavior. The results show that, in addition to the energy saving caused by a predictive longitudinal control of the ego vehicle, this system can also reduce the consumption of surrounding traffic participants significantly. The area of influence can be quantified to four vehicles and up to 250 m behind. Full article

(This article belongs to the Special Issue Vehicle Safe Motion in Mixed Vehicle Technologies Environment)

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26 pages, 11862 KiB

Open AccessArticle

Comparative Assessment of Expected Safety Performance of Freeway Automated Vehicle Managed Lanes

by Jana McLean Sarran and Yasser Hassan

World Electr. Veh. J. 2024, 15(10), 447; https://doi.org/10.3390/wevj15100447 - 29 Sep 2024

Abstract

The use of dedicated lanes, known as managed lanes (MLs), on freeways is an established traffic management strategy to reduce congestion. Allowing automated vehicles (AVs) in existing MLs or dedicating MLs for AVs, referred to as AVMLs, has been suggested in the literature [...] Read more.

The use of dedicated lanes, known as managed lanes (MLs), on freeways is an established traffic management strategy to reduce congestion. Allowing automated vehicles (AVs) in existing MLs or dedicating MLs for AVs, referred to as AVMLs, has been suggested in the literature as a tool to improve traffic operation and safety performance as AVs and driver-operated vehicles (DVs) coexist in a mixed-vehicle environment. This paper focuses on investigating the safety impacts of deploying AVMLs on freeways by repurposing general-purpose lanes (GPLs). Four ML strategies considering different lane positions and access controls were implemented in a traffic microsimulation under different AV market adoption rates (MARs) and traffic demand levels, and trajectories were used to extract rear-end and lane change conflicts. The time-to-collision (TTC) surrogate safety measure was used to identify critical conflicts using a time threshold dependent on the type of following vehicle. Rates of conflicts involving different vehicle types for all ML strategies were compared to the case of heterogeneous traffic. The results indicated that the rates of rear-end conflicts involving the same vehicle type as the lead and following vehicle, namely DV-DV and AV-AV conflicts, increased with ML implementation as more vehicles of the same type traveled in the same lane(s). By comparing the aggregated conflict rates, the design options that were deemed to negatively impact traffic efficiency and capacity were also found to negatively impact traffic safety. However, other ML options were found to be feasible in terms of traffic operation and safety performance, especially at traffic demand levels below capacity. Specifically, one left-side AVML with continuous access was found to have lower or comparable aggregated conflict rates compared to heterogenous traffic at 25% and 50% MARs, and, thus, it is expected to have positive or neutral safety impacts. Full article

(This article belongs to the Special Issue Vehicle Safe Motion in Mixed Vehicle Technologies Environment)

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20 pages, 2474 KiB

Open AccessArticle

Privacy-Preserving Electric Vehicle Charging Recommendation by Incorporating Full Homomorphic Encryption and Secure Multi-Party Computing

by Yiqi Liu, Jiaxin Ju and Zhiyi Li

World Electr. Veh. J. 2024, 15(10), 446; https://doi.org/10.3390/wevj15100446 - 29 Sep 2024

Abstract

Electric vehicle (EV) charging recommendation can significantly improve global planning performance, corresponding to an increasing risk of privacy leakage. Based on this, this paper investigates the privacy data preservation strategy during the interaction between EVs and charging facilities. It proposes a privacy preservation [...] Read more.

Electric vehicle (EV) charging recommendation can significantly improve global planning performance, corresponding to an increasing risk of privacy leakage. Based on this, this paper investigates the privacy data preservation strategy during the interaction between EVs and charging facilities. It proposes a privacy preservation strategy that aims to ensure EV information security. In a cloud computing environment, users do not want other users and cloud providers to have access to their personal information, which is precisely the problem that secure multi-party computing (SMPC) can solve. At present, full homomorphic encryption (FHE) can solve the problem of user data privacy preservation in cloud computing and big data environments and can realize the whole encryption process. Therefore, a more reasonable charging station selection scheme is provided under the computation of privacy preservation strategies incorporating the FHE-SMPC method. The effectiveness and implementation feasibility of the designed privacy preservation strategy in practical applications is verified through testing and comparative analysis. The results show that the developed strategy can significantly reduce the risk of privacy leakage with limited communication resources and computation time consumption. The results provide new perspectives and methodologies for interactive privacy preservation between EVs and charging stations, with application potential. Full article

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Graphical abstract

14 pages, 2732 KiB

Open AccessArticle

Geographic Factors Impacting the Demand for Public EV Charging: An Observational Study

by Niranjan Jayanath, Nathaniel S. Pearre and Lukas G. Swan

World Electr. Veh. J. 2024, 15(10), 445; https://doi.org/10.3390/wevj15100445 - 29 Sep 2024

Abstract

The practicality and substitutability of electric vehicles depend on there being a fast, reliable way to recharge on round trips beyond the range of a single charge. Grouping such infrastructure into charging hubs benefits developers and operators through economies of scale and electric [...] Read more.

The practicality and substitutability of electric vehicles depend on there being a fast, reliable way to recharge on round trips beyond the range of a single charge. Grouping such infrastructure into charging hubs benefits developers and operators through economies of scale and electric vehicle drivers in terms of travel logistics and passed-through cost savings. The need for charging capacity at en-route charging hubs is impacted by the following four identifiable geo-social parameters: (a) highway travel volumes, reflecting the rate at which electric vehicles are expending energy in the area; (b) local population, reflecting both the increased needs of electric vehicle owners without dedicated home chargers and the reduced needs of those commuting into a metropolitan center; (c) the quantity of competing charging stations; and (d) being on a critical interprovincial route. Twelve charging stations located in diverse locations around Nova Scotia, Canada, were evaluated in terms of these four parameters, and their recorded use was investigated from a dataset of 26,000 charging events between April 2022 and April 2024. The regression reveals that there are strong positive correlations between demand for fast charging and (a) traffic volumes (45%) and (c) being on an interprovincial route (42%), while there is only a very weak correlation with (b) local population (2%). Interestingly, there is only a weak negative correlation with (c) the number and capacity of nearby competing chargers (−6%), suggesting that either in short-term route choice or longer-term vehicle choice, the presence of chargers encourages electric vehicles. Full article

(This article belongs to the Special Issue Electric Vehicles and Charging Facilities for a Sustainable Transport Sector)

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23 pages, 5900 KiB

Open AccessReview

Degradation Mechanism and Online Electrical Monitoring Techniques of Stator Winding Insulation in Inverter-Fed Machines: A Review

by Zihan Zou, Senyi Liu and Jinsong Kang

World Electr. Veh. J. 2024, 15(10), 444; https://doi.org/10.3390/wevj15100444 - 29 Sep 2024

Abstract

Inverter-fed machines are widely used in electric vehicle drive systems and have shown a trend toward high voltage and frequency in recent years. They are subjected to multiple types of stress during operation, causing potential short-circuit fault damage to the stator winding insulation. [...] Read more.

Inverter-fed machines are widely used in electric vehicle drive systems and have shown a trend toward high voltage and frequency in recent years. They are subjected to multiple types of stress during operation, causing potential short-circuit fault damage to the stator winding insulation. Online condition monitoring of the insulation before or in the early stage of the short circuit fault can effectively reduce maintenance costs and ensure its health. This paper reviews and summarizes the deterioration mechanism and the recent online electrical monitoring techniques. First, four types of failure stress and each type’s failure factors and mechanisms are analyzed. The coupling effect and overall process of multi-physical fields on stator insulation failure are considered. Secondly, the latest online electrical monitoring technologies are summarized. Each technique’s principles, methods, advantages, and disadvantages are analyzed. Finally, existing problems and possible directions for improvement in current research are discussed, focusing on their feasibility and accuracy in practical applications. Full article

(This article belongs to the Topic Advanced Electric Vehicle Technology, 2nd Volume)

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13 pages, 9505 KiB

Open AccessArticle

Research on Braking Characteristics of Hybrid Excitation Rotary Eddy Current Retarder

by Fei Wang, Wenguang Guo, Guijun Wu and Shi Li

World Electr. Veh. J. 2024, 15(10), 443; https://doi.org/10.3390/wevj15100443 - 28 Sep 2024

Abstract

According to the different excitation methods, automotive eddy current retarders (ECRs) can be divided into electrically excited retarders (EERs) and permanent magnet excited retarders (PMERs), and EERs and PMERs have certain complementarity in control and braking characteristics. Therefore, based on literature research, this [...] Read more.

According to the different excitation methods, automotive eddy current retarders (ECRs) can be divided into electrically excited retarders (EERs) and permanent magnet excited retarders (PMERs), and EERs and PMERs have certain complementarity in control and braking characteristics. Therefore, based on literature research, this article proposes a hybrid excitation rotary electromagnetic retarder (HERER) and conducts numerical simulation analysis and experimental research on the braking performance of the HERER. Firstly, the structure and working principle of the HERER are introduced. Secondly, based on the principles of electromagnetics, an equivalent magnetic circuit analysis model of the HERER is established. Then, a finite element analysis model of the HERER is established using Jmag 14 electromagnetic simulation software, and the braking performance of the HERER under different current and speed conditions is studied. Finally, bench tests are conducted on the air loss torque and eddy current braking performance of the HERER. The effectiveness of the finite element analysis model and equivalent magnetic circuit model of the HERER is verified. Full article

(This article belongs to the Topic Advanced Electric Vehicle Technology, 2nd Volume)

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18 pages, 2018 KiB

Open AccessArticle

Investigation and Analysis of the Contribution of Chinese Electric Vehicle Social Organizations’ Standardization Innovation to Intelligent Optimization Research and Development Investment

by Linfeng Wu, Chi Tian, Yiming Liu, Junhui Liu and Dan Cong

World Electr. Veh. J. 2024, 15(10), 442; https://doi.org/10.3390/wevj15100442 - 28 Sep 2024

Abstract

Intelligent design has been the direction pursued by international electric vehicle (EV) research and development (R&D) teams in recent years. This paper analyzes the problems of unsustainable development in the current product design of EVs in China, such as high R&D investment, high [...] Read more.

Intelligent design has been the direction pursued by international electric vehicle (EV) research and development (R&D) teams in recent years. This paper analyzes the problems of unsustainable development in the current product design of EVs in China, such as high R&D investment, high innovation risks, and low R&D input–output ratios. It explores the issues related to intelligent design, R&D investment, car prices, and safety in the field of EVs in China, and it proposes the concept of optimizing intelligence to optimize the design investment of EVs in China. On the basis of the development situation and the existing problems of social organization standards that gather innovative technologies for EVs, this paper used data from the national social organization standard information platform as the research object and analyzed important data, such as the quantity of the information of relevant social organizations and professional fields of social organization standards, through mathematical methods. The article proposes an optimization design scheme for EV products in China, combining intelligence and practicality from the perspective of the optimizing intelligent design, and it models the construction of EV optimization design. The quantitative relationship between the two schemes before and after optimization design is compared in terms of cost savings in intelligent design, the improvement of social benefits, and the enhancement of EV cost performance. The comparative study found that intelligent optimization design reduced the R&D cost of EVs by 45.24%, and the social benefits of R&D investment increased by 29.51%. Full article

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18 pages, 730 KiB

Open AccessArticle

Electric Vehicle Battery Remanufacturing: Circular Economy Leadership and Workforce Development

by Bianca Ifeoma Chigbu, Fhulu H. Nekhwevha and Ikechukwu Umejesi

World Electr. Veh. J. 2024, 15(10), 441; https://doi.org/10.3390/wevj15100441 - 28 Sep 2024

Abstract

Given the increasing momentum globally towards sustainable transportation, the remanufacturing of used electric vehicle lithium-ion batteries (EV LIBs) emerges as a critical opportunity to promote the principles of the circular economy. Existing research highlights the significance of remanufacturing in resource conservation and waste [...] Read more.

Given the increasing momentum globally towards sustainable transportation, the remanufacturing of used electric vehicle lithium-ion batteries (EV LIBs) emerges as a critical opportunity to promote the principles of the circular economy. Existing research highlights the significance of remanufacturing in resource conservation and waste reduction. Nevertheless, detailed insights into South Africa’s (SA’s) specific capabilities and strategic approaches in the context of used EV LIBs remain sparse. By utilizing in-depth interviews with fifteen key industry stakeholders and drawing on institutional theory, this qualitative study evaluates SA’s infrastructure, technical expertise, and regulatory frameworks in the EV LIB remanufacturing sector to address this gap. The findings reveal proactive strategies, including technical expertise, sustainable infrastructure, and robust regulatory frameworks aligned with global standards. This study proposes strategic initiatives like the Interdisciplinary Innovation Hub and Mobile Remanufacturing Labs, which are analytically derived from stakeholder insights and aim to predict potential pathways for workforce development, especially in rural areas. Innovative training programs, including the Virtual Reality Consortium, Circular Economy Institutes, and the Real-world Challenges Program, will ensure a skilled workforce committed to sustainability and circular economy principles. The conclusions highlight SA’s potential to become a leader in EV LIB remanufacturing by integrating circular economy principles, enhancing technical expertise, and fostering international collaboration. Full article

(This article belongs to the Special Issue Propulsion Systems of EVs 2.0)

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30 pages, 4391 KiB

Open AccessReview

Artificial Intelligence-Based Electric Vehicle Smart Charging System in Malaysia

by Siow Jat Shern, Md Tanjil Sarker, Gobbi Ramasamy, Siva Priya Thiagarajah, Fahmid Al Farid and S. T. Suganthi

World Electr. Veh. J. 2024, 15(10), 440; https://doi.org/10.3390/wevj15100440 - 28 Sep 2024

Abstract

The worldwide transition to electric vehicles (EVs) is gaining momentum, propelled by the imperative to reduce carbon emissions and foster sustainable transportation. In Malaysia, the government is facilitating this transformation through targeted initiatives aimed at promoting the use of electric vehicles (EVs) and [...] Read more.

The worldwide transition to electric vehicles (EVs) is gaining momentum, propelled by the imperative to reduce carbon emissions and foster sustainable transportation. In Malaysia, the government is facilitating this transformation through targeted initiatives aimed at promoting the use of electric vehicles (EVs) and developing the required infrastructure. This paper investigates the crucial role of artificial intelligence (AI) in developing intelligent electric vehicle (EV) charging infrastructure, specifically focusing on the context of Malaysia. The paper examines the current electric vehicle (EV) charging infrastructure in Malaysia, highlights advancements led by artificial intelligence (AI), and references both local and international case studies. Fluctuations in the Total Industry Volume (TIV) and Total Industry Production (TIP) reflect changes in market demand and production capabilities, with notable peaks in March 2023 and March 2024. The research reveals that AI technologies, such as machine learning and predictive analytics, can enhance charging efficiency, improve user experience, and support grid stability. A mathematical model for an AI-based smart charging system was developed, and the implemented system achieved 30% energy savings and a 20.38% reduction in costs compared to traditional methods. These findings underscore the system’s energy and cost efficiency. In addition, we outline the potential advantages and challenges associated with incorporating artificial intelligence (AI) into Malaysia’s electric vehicle (EV) charging infrastructure. Furthermore, we offer recommendations for researchers, industry stakeholders, and regulators. Malaysia can enhance the uptake of electric vehicles and make a positive impact on the environment by leveraging artificial intelligence (AI) to enhance its electric vehicle charging system (EVCS). Full article

(This article belongs to the Special Issue Electric Vehicles and Charging Facilities for a Sustainable Transport Sector)

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31 pages, 5552 KiB

Open AccessArticle

Methodology to Improve an Extended-Range Electric Vehicle Module and Control Integration Based on Equivalent Consumption Minimization Strategy

by David Sebastian Puma-Benavides, Juan de Dios Calderon-Najera, Javier Izquierdo-Reyes, Renato Galluzzi and Edilberto Antonio Llanes-Cedeño

World Electr. Veh. J. 2024, 15(10), 439; https://doi.org/10.3390/wevj15100439 - 27 Sep 2024

Abstract

The continuous expansion of the vehicle fleet contributes to escalating emissions, with the transportation sector accounting for approximately 21% of CO₂ emissions, based on 2023 data. Focused on reducing emissions and reliance on fossil fuels, the study observes the shift from internal [...] Read more.

The continuous expansion of the vehicle fleet contributes to escalating emissions, with the transportation sector accounting for approximately 21% of CO₂ emissions, based on 2023 data. Focused on reducing emissions and reliance on fossil fuels, the study observes the shift from internal combustion vehicles to electric and hybrid models since 2017. Despite advancements, these vehicles still lack optimal efficiency and suffer from limited range, deterring potential buyers. This article aims to evaluate the range-extending technologies for electric vehicles, emphasizing efficiency, low pollution, and integration compatibility. An algorithm incorporating equations representing mechanical or electrical component curves is developed for Extended-Range Electric Vehicles, facilitating insight into potential range extender behavior. The core objectives of this study involve optimizing the entire powertrain system to ensure peak efficiency. Experimental tests demonstrate that integrating an auxiliary power unit enhances range, with an internal combustion engine generator configuration extending the travel distance by 35.35% at a constant speed. Moreover, with the use of an Equivalent Consumption Minimization Strategy control, the distance traveled increases up to 39.28% on standard driving cycles. The proposed methodology, validated through practical implementations, allows for comprehensive energy analyses, providing a precise understanding of vehicle platform performance with integrated range extenders. Full article

(This article belongs to the Special Issue The Energy Efficiency of Electric Vehicle Charging Stations with Minimal Grid Impact)

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World Electric Vehicle Journal

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