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World Electr. Veh. J., Volume 15, Issue 10 (October 2024) – 49 articles

Cover Story (view full-size image): This paper presents a method for improving energy efficiency and driving performance in a two-speed transmission all-wheel-drive (AWD) system through torque distribution and powertrain specification optimization. Based on vehicle simulations conducted in MATLAB/Simulink, a torque distribution strategy between the front and rear axles was developed using fuzzy logic, considering energy efficiency and driving stability. Furthermore, multi-objective optimization was performed using a surrogate model trained via MATLAB parallel simulations. When the optimization results were applied to various vehicle specifications, energy efficiency and acceleration performance improvements were observed compared to a baseline vehicle without optimization. View this paper
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20 pages, 2728 KiB  
Article
Scenario-Based Analysis of Electrification Effects on Value Creation and Employment Structures for the Automotive Industry in the Federal State of Baden-Wuerttemberg, Germany
by Benjamin Frieske, Samuel Hasselwander, Özcan Deniz, Sylvia Stieler and Simon Schumich
World Electr. Veh. J. 2024, 15(10), 480; https://doi.org/10.3390/wevj15100480 - 21 Oct 2024
Viewed by 751
Abstract
The transformation path to electric mobility will have fundamental impacts on the existing value chain and employment structures in the automotive industry. The purpose of this paper is to derive and examine these effects based on two different electric mobility market scenarios for [...] Read more.
The transformation path to electric mobility will have fundamental impacts on the existing value chain and employment structures in the automotive industry. The purpose of this paper is to derive and examine these effects based on two different electric mobility market scenarios for the European (EU27) passenger car as well as truck market 2040 with special focus on the highly export-oriented industrial automotive cluster in Baden-Wuerttemberg. To achieve this, both a moderate and a progressive market scenario were simulated using the scientifically validated DLR VECTOR21 vehicle technology scenario model, based on two different parameter sets derived from actual and possible framework conditions on the European car and truck markets. Based on a detailed analysis of the industrial branch, value creation, and employee structure in Baden-Wuerttemberg and its automotive cluster, the effects resulting from the transformation to electric mobility will be displayed. With detailed Fade-In and Fade-Out analysis, the shifts from internal combustion engine components to electrified components will be derived and illustrated for each employee segment in the automotive cluster in Baden-Wuerttemberg, which leads to completely new and original results at this level of detail. Furthermore, the detailed display of the automotive cluster in this study allows for regionalized statements on employment effects, considering, for the first time, not only the car but also the truck segment. The analysis shows that battery electric vehicles will achieve a share of 34% or 57% for new registrations on the German car market in 2030, depending on the scenario framework conditions. The resulting employment effects for the entire automotive cluster in Baden-Wuerttemberg reach −37,000 (−8%) or −66,000 (−14%) by 2030 with further negative development until 2040 (−155,000, −30%) for the respective scenarios. Employment segments, in particular powertrain-dependent production employees, are at risk, with a potential decline of up to −60%. R&D employees could be also be significantly, affected with a reduction in workforce of about −50%. Full article
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36 pages, 11788 KiB  
Article
Intelligent Robust Controllers Applied to an Auxiliary Energy System for Electric Vehicles
by Mario Antonio Ruz Canul, Jose A. Ruz-Hernandez, Alma Y. Alanis, Jose-Luis Rullan-Lara, Ramon Garcia-Hernandez and Jaime R. Vior-Franco
World Electr. Veh. J. 2024, 15(10), 479; https://doi.org/10.3390/wevj15100479 - 21 Oct 2024
Viewed by 734
Abstract
This paper presents two intelligent robust control strategies applied to manage the dynamics of a DC-DC bidirectional buck–boost converter, which is used in conjunction with a supercapacitor as an auxiliary energy system (AES) for regenerative braking in electric vehicles. The Neural Inverse Optimal [...] Read more.
This paper presents two intelligent robust control strategies applied to manage the dynamics of a DC-DC bidirectional buck–boost converter, which is used in conjunction with a supercapacitor as an auxiliary energy system (AES) for regenerative braking in electric vehicles. The Neural Inverse Optimal Controller (NIOC) and the Neural Sliding Mode Controller (NSMC) utilize identifiers based on Recurrent High-Order Neural Networks (RHONNs) trained with the Extended Kalman Filter (EKF) to track voltage and current references from the converter circuit. Additionally, a driving cycle test tailored specifically for typical urban driving in electric vehicles (EVs) is implemented to validate the efficacy of the proposed controller and energy improvement strategy. The proposed NSMC and NIOC are compared with a PI controller; furthermore, an induction motor and its corresponding three-phase inverter are incorporated into the EV control scheme which is implemented in Matlab/Simulink using the “Simscape Electrical” toolbox. The Mean Squared Error (MSE) is computed to validate the performance of the neural controllers. Additionally, the improvement in the State of Charge (SOC) for an electric vehicle battery through the control of buck–boost converter dynamics is addressed. Finally, several robustness tests against parameter changes in the converter are conducted, along with their corresponding performance indices. Full article
(This article belongs to the Special Issue Power and Energy Systems for E-mobility)
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24 pages, 7352 KiB  
Article
Investigation of Engine Exhaust Heat Recovery Systems Utilizing Thermal Battery Technology
by Bo Zhu, Yi Zhang and Dengping Wang
World Electr. Veh. J. 2024, 15(10), 478; https://doi.org/10.3390/wevj15100478 - 21 Oct 2024
Viewed by 529
Abstract
Over 50% of an engine’s energy dissipates via the exhaust and cooling systems, leading to considerable energy loss. Effectively harnessing the waste heat generated by the engine is a critical avenue for enhancing energy efficiency. Traditional exhaust heat recovery systems are limited to [...] Read more.
Over 50% of an engine’s energy dissipates via the exhaust and cooling systems, leading to considerable energy loss. Effectively harnessing the waste heat generated by the engine is a critical avenue for enhancing energy efficiency. Traditional exhaust heat recovery systems are limited to real-time recovery of exhaust heat primarily for engine warm-up and fail to fully optimize exhaust heat utilization. This paper introduces a novel exhaust heat recovery system leveraging thermal battery technology, which utilizes phase change materials for both heat storage and reutilization. This innovation significantly minimizes the engine’s cold start duration and provides necessary heating for the cabin during start-up. Dynamic models and thermal management system models were constructed. Parameter optimization and calculations for essential components were conducted, and the fidelity of the simulation model was confirmed through experiments conducted under idle warm-up conditions. Four distinct operational modes for engine warm-up are proposed, and strategies for transitioning between these heating modes are established. A simulation analysis was performed across four varying operational scenarios: WLTC, NEDC, 40 km/h, and 80 km/h. The results indicated that the thermal battery-based exhaust heat recovery system notably reduces warm-up time and fuel consumption. In comparison to the cold start mode, the constant speed condition at 40 km/h showcased the most significant reduction in warm-up time, achieving an impressive 22.52% saving; the highest cumulative fuel consumption reduction was observed at a constant speed of 80 km/h, totaling 24.7%. This study offers theoretical foundations for further exploration of thermal management systems in new energy vehicles that incorporate heat storage and reutilization strategies utilizing thermal batteries. Full article
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16 pages, 7998 KiB  
Article
Regional Analysis and Evaluation Method for Assessing Potential for Installation of Renewable Energy and Electric Vehicles
by Yutaro Akimoto, Raimu Okano, Keiichi Okajima and Shin-nosuke Suzuki
World Electr. Veh. J. 2024, 15(10), 477; https://doi.org/10.3390/wevj15100477 - 19 Oct 2024
Viewed by 475
Abstract
Many countries are adopting renewable energy (RE) and electric vehicles (EVs) to achieve net-zero emissions by 2050. The indicators of RE and EV potentials are different. Decision-makers want to introduce RE and EVs; however, they need a method to find suitable areas. In [...] Read more.
Many countries are adopting renewable energy (RE) and electric vehicles (EVs) to achieve net-zero emissions by 2050. The indicators of RE and EV potentials are different. Decision-makers want to introduce RE and EVs; however, they need a method to find suitable areas. In addition, this is required in the time-series analysis to provide a detailed resolution. In this study, we conducted a time-series analysis in Japan to evaluate suitable areas for the combined use of RE and EVs. The results showed the surplus RE areas and shortage RE urban areas. The time-series analysis has quantitatively shown that it is not enough to charge EV batteries using surplus RE. Moreover, a ranking methodology was developed for the evaluation based on electric demand and vehicle numbers. This enables the government’s prioritization of prefectures and the prefectures’ prioritization of municipalities according to their policies. Full article
(This article belongs to the Special Issue Power and Energy Systems for E-mobility)
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21 pages, 11342 KiB  
Article
Driving Control Strategy and Specification Optimization for All-Wheel-Drive Electric Vehicle System with a Two-Speed Transmission
by Jeonghyuk Kim, Jihyeok Ahn, Seyoung Jeong, Young-Geun Park, Hyobin Kim, Dongwook Cho and Sung-Ho Hwang
World Electr. Veh. J. 2024, 15(10), 476; https://doi.org/10.3390/wevj15100476 - 19 Oct 2024
Viewed by 553
Abstract
Equipping electric vehicles with a two-speed gearbox allows for achieving high torque and maximum speed through appropriate gear ratio adjustments. Additionally, tuning motor operating points to efficient zones, considering energy efficiency, significantly enhances the vehicle’s overall performance. This paper presents an AWD system [...] Read more.
Equipping electric vehicles with a two-speed gearbox allows for achieving high torque and maximum speed through appropriate gear ratio adjustments. Additionally, tuning motor operating points to efficient zones, considering energy efficiency, significantly enhances the vehicle’s overall performance. This paper presents an AWD system configuration method, integrating a two-speed transmission to improve energy efficiency and driving performance through front and rear motor torque distribution and powertrain specification optimization. Based on vehicle simulations conducted using MATLAB/Simulink, a strategy for torque distribution between the front/rear axles was established using fuzzy logic, considering energy efficiency and driving stability. Furthermore, a multi-objective optimization was performed using a surrogate model trained through MATLAB parallel simulations. When the optimization results were applied to various vehicle specifications, it was observed that energy efficiency was improved, and acceleration performance was increased compared to a baseline vehicle without optimization. Full article
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23 pages, 1340 KiB  
Article
Service Quality and Behavioral Intention Analysis of Passengers on Small Electric Public Transportation: A Case Study of Electric Tuktuk in the Philippines
by Tanya Jeimiel T. Base, Ardvin Kester S. Ong, Maela Madel L. Cahigas and Ma. Janice J. Gumasing
World Electr. Veh. J. 2024, 15(10), 475; https://doi.org/10.3390/wevj15100475 - 17 Oct 2024
Viewed by 1230
Abstract
Tuktuk, as a generalized connotation, serves as a widely used vehicle for urban transportation, adapted from Thailand by the Philippines. The creation of the electric-typed public vehicle has now been recognized as one of the modalities of public transportations, the etuktuk. This study [...] Read more.
Tuktuk, as a generalized connotation, serves as a widely used vehicle for urban transportation, adapted from Thailand by the Philippines. The creation of the electric-typed public vehicle has now been recognized as one of the modalities of public transportations, the etuktuk. This study investigated the factors influencing passengers’ intention to use etuktuks as a mode of transportation in the Philippines by integrating the Theory of Planned Behavior with higher-order SERVQUAL dimensions. The objective was to understand how service qualities, attitudes, subjective norms, and perceived behavioral control impact passenger satisfaction and intention to use etuktuks. Data were collected from 501 respondents who had used etuktuks and were analyzed using partial least square structural equation modeling. The findings revealed that assurance, empathy, reliability, tangibility, and responsiveness significantly affected passenger satisfaction and intentions, with assurance being the most influential factor. Conversely, tangibles, such as the physical attributes of the etuktuk, were the least significant in shaping passenger preferences. Notably, a negative significant effect was observed between service quality and behavioral intention, indicating that while passengers are generally satisfied with etuktuk services, they may opt for alternative transportation options when available. These results highlight the need for improvements in etuktuk services, particularly in enhancing comfort, safety, and accessibility. Such improvements are crucial for encouraging wider adoption of etuktuks and fostering cleaner, more sustainable urban environments. By addressing the identified service quality issues and leveraging the research findings, stakeholders can better support the transition to more environmentally friendly and efficient transportation options. Full article
(This article belongs to the Special Issue New Trends in Electrical Drives for EV Applications)
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18 pages, 1011 KiB  
Systematic Review
Driving Under Cognitive Control: The Impact of Executive Functions in Driving
by Pantelis Pergantis, Victoria Bamicha, Irene Chaidi and Athanasios Drigas
World Electr. Veh. J. 2024, 15(10), 474; https://doi.org/10.3390/wevj15100474 - 16 Oct 2024
Viewed by 1334
Abstract
This review will explore the role of executive functions and the impact they have in facilitating the skills of vehicle operation. Executive functions are critical for the decision-making process, problem-solving, and multitasking. They are considered the primary factors in driving cases that demand [...] Read more.
This review will explore the role of executive functions and the impact they have in facilitating the skills of vehicle operation. Executive functions are critical for the decision-making process, problem-solving, and multitasking. They are considered the primary factors in driving cases that demand drivers to react quickly and adapt to certain situations. Based on the PRISMA 2020 guidelines, this study aims to investigate, analyze, and categorize higher mental skills and their qualities directly related to driving. The literature review was performed in the following databases: PubMed, Web of Science, Scopus, and Google Scholar, using the article collections’ snowball search technique. The results suggest that key executive functions like working memory and inhibitory control are closely related to risky behavior and driving errors that lead to accidents. This review adds valuable insight by highlighting the significance of their contribution to future research, driver educational programs, and technology for improving driver safety. Consequently, collecting recent data will contribute to understanding new parameters that influence driving behavior, creating the possibility for appropriate proposals for future research. Full article
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24 pages, 1604 KiB  
Article
Event-Triggered Two-Part Separation Control of Multiple Autonomous Underwater Vehicles Based on Extended Observer
by Yunyang Gu, Yueru Xu, Mingzuo Jiang and Zhigang Zhou
World Electr. Veh. J. 2024, 15(10), 473; https://doi.org/10.3390/wevj15100473 - 16 Oct 2024
Viewed by 526
Abstract
In this paper, we investigate the formation isolation regulation issue regarding multiple Autonomous Underwater Vehicles (AUVs) characterized by a “leader–follower” framework. Considering the cooperative–competitive relationship among the follower AUVs and the impact of unknown external disturbances, an extended state observer is designed based [...] Read more.
In this paper, we investigate the formation isolation regulation issue regarding multiple Autonomous Underwater Vehicles (AUVs) characterized by a “leader–follower” framework. Considering the cooperative–competitive relationship among the follower AUVs and the impact of unknown external disturbances, an extended state observer is designed based on backstepping to mitigate these disturbances, and an event-triggered control scheme is designed to realize the two-part consensus control within the multi-AUV system. Through rigorous theoretical analysis, it is shown that the system achieves asymptotic steadiness and is free from Zeno behavior under the proposed event-triggered control scheme. Finally, numerical simulations confirm the efficiency of the regulation strategy in achieving formation separation within the multi-AUV, where the trajectory tracking errors of individual AUVs gather in a compact vicinity close to the source, and the structure convergence is achieved, with the absence of Zeno behavior also demonstrated. Full article
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12 pages, 2337 KiB  
Article
Prediction of the Resource of the Power Plant Hybrid Vehicle
by Juraj Gerlici, Oleksiy Bazhinov, Oleksandr Kravchenko, Tatiana Bazhynova and Kateryna Kravchenko
World Electr. Veh. J. 2024, 15(10), 472; https://doi.org/10.3390/wevj15100472 - 16 Oct 2024
Viewed by 550
Abstract
This article substantiates the possibility of solving a difficult task to offer a reliable estimate of the residual resource of the power plant of a hybrid car, which is exposed during operation to the influence of a complex set of external factors. This [...] Read more.
This article substantiates the possibility of solving a difficult task to offer a reliable estimate of the residual resource of the power plant of a hybrid car, which is exposed during operation to the influence of a complex set of external factors. This task is solved by monitoring the time change of diagnostic parameters and controlling the load–speed mode of the hybrid power plant, as well as using physical and chemical processes that cause the degradation of the traction battery. Methodological principles for assessing the resource of a hybrid power plant of a car have been developed, which are based on the disclosure of cause-and-effect relationships between realized and nominal quality indicators, operating conditions, and indicators of technical condition. The choice of rational solutions for the operation of a hybrid car is given, and the external conditions are determined, under which the residual life of the hybrid power plant will change. The scientific result of the study is a theoretical generalization of the scientific provisions of forecasting the resource of the hybrid power plant of a car. This ensures the efficient use of hybrid vehicles and reduces maintenance costs. Full article
(This article belongs to the Special Issue Power and Energy Systems for E-mobility)
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21 pages, 1709 KiB  
Article
Electric Vehicle Adoption: Implications for Employment in South Africa’s Automotive Component Industry
by Nalini Sooknanan Pillay and Alaize Dall-Orsoletta
World Electr. Veh. J. 2024, 15(10), 471; https://doi.org/10.3390/wevj15100471 - 15 Oct 2024
Viewed by 747
Abstract
The transition to electric vehicles (EVs) will require significant changes in the automotive industry, particularly concerning its labour force. This study evaluates the impact of EVs on employment within South Africa’s automotive component manufacturing sector. A system dynamics model was developed to assess [...] Read more.
The transition to electric vehicles (EVs) will require significant changes in the automotive industry, particularly concerning its labour force. This study evaluates the impact of EVs on employment within South Africa’s automotive component manufacturing sector. A system dynamics model was developed to assess the effect of EV market penetration on component manufacturing employment over time. Key drivers of employment in the conventional and the EV component industries were identified and incorporated into the model. The results indicate a negative impact of EV penetration on employment of 18.3% when considering 20.0% EV sales (EV20) in 2040. Scenario analyses highlighted the influence of individual components, battery localisation, and load shedding on labour. Tyre and wheel manufacturing was found to be the most labour impactful component in the conventional industry against electrical engines in the EV counterpart. Localising 25.0% of battery production could increase employment by 6.9% and 2.7% in the EV40 and EV20 Scenarios. Load shedding has a detrimental effect on the country’s economy, assumed to reduce employment by 30.0%. However, strategic industry and policy interventions can mitigate the adverse effects of this transition. Full article
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20 pages, 9814 KiB  
Article
Research on Performance of Interior Permanent Magnet Synchronous Motor with Fractional Slot Concentrated Winding for Electric Vehicles Applications
by Zhiqiang Xi, Lianbo Niu, Xianghai Yan and Liyou Xu
World Electr. Veh. J. 2024, 15(10), 470; https://doi.org/10.3390/wevj15100470 - 14 Oct 2024
Viewed by 887
Abstract
The fractional-slot, concentrated-winding, interior permanent magnet synchronous motor (FSCW IPMSM) has advantages, such as reducing motor copper consumption, improving flux-weakening capability, and motor fault tolerance, and has certain development potential in application fields such as electric vehicles. However, fractional-slot concentrated-winding motors often contain [...] Read more.
The fractional-slot, concentrated-winding, interior permanent magnet synchronous motor (FSCW IPMSM) has advantages, such as reducing motor copper consumption, improving flux-weakening capability, and motor fault tolerance, and has certain development potential in application fields such as electric vehicles. However, fractional-slot concentrated-winding motors often contain rich harmonic components due to their winding characteristics, leading to increased motor losses and back electromotive force harmonics, thereby affecting the efficiency and constant power speed regulation range of the motor. Based on this, this article first uses the winding function method to explore the inductance and saliency ratio of the interior permanent magnet synchronous motor with different slot pole combinations in the fractional-slot concentrated- winding of electric vehicles. Secondly, this article will establish a 2D finite element parameterized model to analyze and compare the performance of fractional-slot concentrated-winding motors with different slot pole combinations, including air gap magnetic density, back electromotive force distortion rate, overload multiple, and torque. The structural parameters of the motor were optimized with the objective of minimizing the torque ripple under the constraint of minimizing the average torque reduction. The motor slot width, permanent magnet angle, and permanent magnet pole arc angle were analyzed and optimized. The simulation results showed that 12 slots and 8 poles were the optimal design schemes, providing a theoretical basis for the selection of slot pole coordination in the fractional-slot concentrated-winding interior permanent magnet synchronous motor for electric vehicles. Full article
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12 pages, 30418 KiB  
Article
Optimization Control Strategy for Light Load Efficiency of Four-Switch Buck-Boost Converter
by Siyuan Gao, Fanghua Zhang and Hongxin Mei
World Electr. Veh. J. 2024, 15(10), 469; https://doi.org/10.3390/wevj15100469 - 14 Oct 2024
Viewed by 635
Abstract
The four-switch buck-boost (FSBB) converter usually adopts a pseudo-continuous conduction mode (PCCM) soft switching (ZVS) control strategy, but there is a problem with the low efficiency of FSBB converters under light loads. Firstly, the constraints that the control variables of the FSBB converter [...] Read more.
The four-switch buck-boost (FSBB) converter usually adopts a pseudo-continuous conduction mode (PCCM) soft switching (ZVS) control strategy, but there is a problem with the low efficiency of FSBB converters under light loads. Firstly, the constraints that the control variables of the FSBB converter need to satisfy are analyzed, and it is pointed out that the fixed frequency constraint is not necessary. Then, the switching frequency is used to control the degree of freedom, and the quantitative relationship between the FSBB converter loss and the switching frequency is obtained. Finally, for different input voltages and loads, the switching frequency corresponding to the minimum power loss is calculated offline. By optimizing the switching frequency, the light-load efficiency of the FSBB converter is improved. A prototype with an input voltage range of 210 V–330 V, an output voltage of 270 V, and an output power of 3 kW was developed. The loss was reduced by 15% at 20% load, and the peak efficiency of the converter reached 99.23%. The experimental results verified the effectiveness of the proposed control strategy. Full article
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25 pages, 6995 KiB  
Article
The Control Strategies for Charging and Discharging of Electric Vehicles in the Vehicle–Grid Interaction Modes
by Tao Wang, Jihui Zhang, Xin Li, Shenhui Chen, Jinhao Ma and Honglin Han
World Electr. Veh. J. 2024, 15(10), 468; https://doi.org/10.3390/wevj15100468 - 14 Oct 2024
Viewed by 636
Abstract
In response to the challenges posed by large-scale, uncoordinated electric vehicle charging on the power grid, Vehicle-to-Grid (V2G) technology has been developed. This technology seeks to synchronize electric vehicles with the power grid, improving the stability of their connections and fostering positive energy [...] Read more.
In response to the challenges posed by large-scale, uncoordinated electric vehicle charging on the power grid, Vehicle-to-Grid (V2G) technology has been developed. This technology seeks to synchronize electric vehicles with the power grid, improving the stability of their connections and fostering positive energy exchanges between them. The key component for implementing V2G technology is the bidirectional AC/DC converter. This study concentrates on the non-isolated bidirectional AC/DC converter, providing a detailed analysis of its two-stage operation and creating a mathematical model. A dual closed-loop control structure for voltage and current is designed based on nonlinear control theory, along with a constant current charge–discharge control strategy. Furthermore, midpoint potential balance is achieved through zero-sequence voltage injection control, and power signals for the switching devices are generated using Space Vector Pulse Width Modulation (SVPWM) technology. A simulation model of the V2G system is then constructed in MATLAB/Simulink for analysis and validation. The findings demonstrate that the control strategy proposed in this paper improves the system’s robustness, dynamic performance, and resistance to interference, thus reducing the effects of large-scale, uncoordinated electric vehicle charging on the power grid. Full article
(This article belongs to the Special Issue Intelligent Electric Vehicle Control, Testing and Evaluation)
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13 pages, 2709 KiB  
Article
Enhanced Vehicle Logo Detection Method Based on Self-Attention Mechanism for Electric Vehicle Application
by Shuo Yang, Yisu Liu, Ziyue Liu, Changhua Xu and Xueting Du
World Electr. Veh. J. 2024, 15(10), 467; https://doi.org/10.3390/wevj15100467 - 14 Oct 2024
Viewed by 620
Abstract
Vehicle logo detection plays a crucial role in various computer vision applications, such as vehicle classification and detection. In this research, we propose an improved vehicle logo detection method leveraging the self-attention mechanism. Our feature-sampling structure integrates multiple attention mechanisms and bidirectional feature [...] Read more.
Vehicle logo detection plays a crucial role in various computer vision applications, such as vehicle classification and detection. In this research, we propose an improved vehicle logo detection method leveraging the self-attention mechanism. Our feature-sampling structure integrates multiple attention mechanisms and bidirectional feature aggregation to enhance the discriminative power of the detection model. Specifically, we introduce the multi-head attention for multi-scale feature fusion module to capture multi-scale contextual information effectively. Moreover, we incorporate the bidirectional aggregation mechanism to facilitate information exchange between different layers of the detection network. Experimental results on a benchmark dataset (VLD-45 dataset) demonstrate that our proposed method outperforms baseline models in terms of both detection accuracy and efficiency. Our experimental evaluation using the VLD-45 dataset achieves a state-of-the-art result of 90.3% mAP. Our method has also improved AP by 10% for difficult samples, such as HAVAL and LAND ROVER. Our method provides a new detection framework for small-size objects, with potential applications in various fields. Full article
(This article belongs to the Special Issue Deep Learning Applications for Electric Vehicles)
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14 pages, 4694 KiB  
Article
Two-Stage Multiple-Vector Model Predictive Control for Multiple-Phase Electric-Drive-Reconstructed Power Management for Solar-Powered Vehicles
by Qingyun Zhu, Zhen Zhang and Zhihao Zhu
World Electr. Veh. J. 2024, 15(10), 466; https://doi.org/10.3390/wevj15100466 - 14 Oct 2024
Viewed by 573
Abstract
Electric-drive-reconstructed onboard chargers (EDROCs), also known as electric-drive-reconstructed power management systems, are a promising alternative to conventional onboard chargers due to their characteristics of low cost and high power density. The model predictive control offers a fast dynamic response, simple implementation, and the [...] Read more.
Electric-drive-reconstructed onboard chargers (EDROCs), also known as electric-drive-reconstructed power management systems, are a promising alternative to conventional onboard chargers due to their characteristics of low cost and high power density. The model predictive control offers a fast dynamic response, simple implementation, and the ability to control multiple targets simultaneously. In this paper, a two-stage multi-vector model predictive current control (MPCC) of a six-phase EDROC for solar-powered electric vehicles (EVs) is proposed. Firstly, the topology for the EDROC incorporating a six-phase symmetrical permanent magnet synchronous machine (PMSM) is introduced, and the operation principles of the DC charge mode, the drive mode, and, especially, the in-motion charge mode are analyzed in detail. After that, a two-stage multi-vector MPCC method is proposed by using the multi-vector MPC technique and designing a two-stage MPC structure to eliminate the regulation of the weighting factor of the MPC. Finally, the effectiveness of the proposed method is verified on a self-designed 2 kW EDROC platform. Full article
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23 pages, 6263 KiB  
Article
Lateral-Stability-Oriented Path-Tracking Control Design for Four-Wheel Independent Drive Autonomous Vehicles with Tire Dynamic Characteristics under Extreme Conditions
by Zhencheng Yu, Rongchen Zhao and Tengfei Yuan
World Electr. Veh. J. 2024, 15(10), 465; https://doi.org/10.3390/wevj15100465 - 13 Oct 2024
Viewed by 712
Abstract
This paper proposes a lateral-stability-oriented path-tracking controller for four-wheel independent drive (4WID) autonomous vehicles. The proposed controller aims to maintain vehicle stability under extreme conditions while minimizing lateral deviation. Firstly, a tiered control framework comprising upper-level and lower-level controllers is introduced. The upper-level [...] Read more.
This paper proposes a lateral-stability-oriented path-tracking controller for four-wheel independent drive (4WID) autonomous vehicles. The proposed controller aims to maintain vehicle stability under extreme conditions while minimizing lateral deviation. Firstly, a tiered control framework comprising upper-level and lower-level controllers is introduced. The upper-level controller is a lateral stability path-tracking controller that incorporates tire dynamic characteristics, developed using model predictive control (MPC) theory. This controller dynamically updates the tire lateral force constraints in real time to account for variations in tire dynamics under extreme conditions. Additionally, it enhances lateral stability and reduces path-tracking errors by applying additional yaw torque based on minimum tire utilization. The lower-level controllers execute the required steering angles and yaw moments through the appropriate component equipment and torque distribution. The joint simulation results from CarSim and MATLAB/Simulink show that, compared to the traditional MPC controller with unstable sideslip, this controller can maintain vehicle lateral stability under extreme conditions. Compared to the MPC controller, which only considers lateral force constraints, this controller can significantly reduce lateral tracking errors, with an average yaw rate reduction of 31.62% and an average sideslip angle reduction of 40.21%. Full article
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14 pages, 3885 KiB  
Article
Simulation and Experimental Study on Heat Transfer Performance of Bionic Structure-Based Battery Liquid Cooling Plate
by Zhizhong Wang, Dinghong Liu, Zhaoyang Li, Xin Qi and Chaoyi Wan
World Electr. Veh. J. 2024, 15(10), 464; https://doi.org/10.3390/wevj15100464 - 12 Oct 2024
Viewed by 544
Abstract
This study presents a bionic structure-based liquid cooling plate designed to address the heat generation characteristics of prismatic lithium-ion batteries. The size of the lithium-ion battery is 148 mm × 26 mm × 97 mm, the positive pole size is 20 mm × [...] Read more.
This study presents a bionic structure-based liquid cooling plate designed to address the heat generation characteristics of prismatic lithium-ion batteries. The size of the lithium-ion battery is 148 mm × 26 mm × 97 mm, the positive pole size is 20 mm × 20 mm × 3 mm, and the negative pole size is 22 mm × 20 mm × 3 mm. Experimental testing of the Li-ion battery’s heat generation model parameters, in conjunction with bionic structure and micro-channel features, has led to the development of this innovative cooling system. The traditional bionic liquid cooling plate’s structure is often singular; however, the flow path of the liquid cooling plate designed in this paper is based on the combination of the distribution of human blood vessel branches and the structure of insect wing veins. The external dimension of the liquid cooling plate is 152 mm × 100 mm × 6 mm (length × width × height). Utilizing numerical simulation and thermodynamic principles, we analyzed the heat transfer efficacy of the bionic liquid cooling module for power batteries. Specifically, we investigated the impact of varying coolant flow rates and the contact radius between flow channels on the thermal performance of the bionic battery modules. Our findings indicate that a liquid flow rate of 0.6 m/s achieves a stable maximum surface temperature and temperature differential across the bionic battery liquid cooling module, with a relatively low overall system power consumption, suggesting room for further enhancement of heat transfer performance. By augmenting the contact radius between flow channels, we observed an initial increase in the maximum surface temperature, temperature differential, and inlet–outlet pressure differential at a flow rate of 0.2 m/s. However, at flow rates equal to or exceeding 0.4 m/s, these parameters stabilized across different design Scenarios. Notably, the pump power consumption remained consistent across various scenarios and flow rates. This study’s outcomes offer valuable insights for the development of liquid-cooled battery thermal management systems that are energy-efficient and offer superior heat transfer capabilities. Full article
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27 pages, 12606 KiB  
Article
Dynamic Wireless Charging of Electric Vehicles Using PV Units in Highways
by Tamer F. Megahed, Diaa-Eldin A. Mansour, Donart Nayebare, Mohamed F. Kotb, Ahmed Fares, Ibrahim A. Hameed and Haitham El-Hussieny
World Electr. Veh. J. 2024, 15(10), 463; https://doi.org/10.3390/wevj15100463 - 12 Oct 2024
Viewed by 940
Abstract
Transitioning from petrol or gas vehicles to electric vehicles (EVs) poses significant challenges in reducing emissions, lowering operational costs, and improving energy storage. Wireless charging EVs offer promising solutions to wired charging limitations such as restricted travel range and lengthy charging times. This [...] Read more.
Transitioning from petrol or gas vehicles to electric vehicles (EVs) poses significant challenges in reducing emissions, lowering operational costs, and improving energy storage. Wireless charging EVs offer promising solutions to wired charging limitations such as restricted travel range and lengthy charging times. This paper presents a comprehensive approach to address the challenges of wireless power transfer (WPT) for EVs by optimizing coupling frequency and coil design to enhance efficiency while minimizing electromagnetic interference (EMI) and heat generation. A novel coil design and adaptive hardware are proposed to improve power transfer efficiency (PTE) by defining the optimal magnetic resonant coupling WPT and mitigating coil misalignment, which is considered a significant barrier to the widespread adoption of WPT for EVs. A new methodology for designing and arranging roadside lanes and facilities for dynamic wireless charging (DWC) of EVs is introduced. This includes the optimization of transmitter coils (TCs), receiving coils (RCs), compensation circuits, and high-frequency inverters/converters using the partial differential equation toolbox (pdetool). The integration of wireless charging systems with smart grid technology is explored to enhance energy distribution and reduce peak load issues. The paper proposes a DWC system with multiple segmented transmitters integrated with adaptive renewable photovoltaic (PV) units and a battery system using the utility main grid as a backup. The design process includes the determination of the required PV array capacity, station battery sizing, and inverters/converters to ensure maximum power point tracking (MPPT). To validate the proposed system, it was tested in two scenarios: charging a single EV at different speeds and simultaneously charging two EVs over a 1 km stretch with a 50 kW system, achieving a total range of 500 km. Experimental validation was performed through real-time simulation and hardware tests using an OPAL-RT platform, demonstrating a power transfer efficiency of 90.7%, thus confirming the scalability and feasibility of the system for future EV infrastructure. Full article
(This article belongs to the Special Issue Wireless Power Transfer Technology for Electric Vehicles)
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20 pages, 5342 KiB  
Article
Optimal EV Charging and PV Siting in Prosumers towards Loss Reduction and Voltage Profile Improvement in Distribution Networks
by Christina V. Grammenou, Magdalini Dragatsika and Aggelos S. Bouhouras
World Electr. Veh. J. 2024, 15(10), 462; https://doi.org/10.3390/wevj15100462 - 11 Oct 2024
Viewed by 672
Abstract
In this paper, the problem of simultaneous charging of Electrical Vehicles (EVs) in distribution networks (DNs) is examined in order to depict congestion issues, increased power losses, and voltage constraint violations. To this end, this paper proposes an optimal EV charging schedule in [...] Read more.
In this paper, the problem of simultaneous charging of Electrical Vehicles (EVs) in distribution networks (DNs) is examined in order to depict congestion issues, increased power losses, and voltage constraint violations. To this end, this paper proposes an optimal EV charging schedule in order to allocate the charging of EVs in non-overlapping time slots, aiming to avoid overloading conditions that could stress the DN operation. The problem is structured as a linear optimization problem in GAMS, and the linear Distflow is utilized for the power flow analysis required. The proposed approach is compared to the one where EV charging is not optimally scheduled and each EV is expected to start charging upon its arrival at the residential charging spot. Moreover, the analysis is extended to examine the optimal siting of small-sized residential Photovoltaic (PV) systems in order to provide further relief to the DN. A mixed-integer quadratic optimization model was formed to integrate the PV siting into the optimization problem as an additional optimization variable and is compared to a heuristic-based approach for determining the sites for PV installation. The proposed methodology has been applied in a typical low-voltage (LV) DN as a case study, including real power demand data for the residences and technical characteristics for the EVs. The results indicate that both the DN power losses and the voltage profile are further improved in regard to the heuristic-based approach, and the simultaneously scheduled penetration of EVs and PVs could yield up to a 66.3% power loss reduction. Full article
(This article belongs to the Special Issue Data Exchange between Vehicle and Power System for Optimal Charging)
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26 pages, 4218 KiB  
Article
Optimal Scheduling of Integrated Energy System Considering Virtual Heat Storage and Electric Vehicles
by Yinjun Liu, Yongqing Zhu, Shunjiang Yu, Zhibang Wang, Zhen Li, Changming Chen, Li Yang and Zhenzhi Lin
World Electr. Veh. J. 2024, 15(10), 461; https://doi.org/10.3390/wevj15100461 - 11 Oct 2024
Viewed by 625
Abstract
Integrated energy systems (IESs) are complex multisource supply systems with integrated source, grid, load, and storage systems, which can provide various flexible resources. Nowadays, there exists the phenomenon of a current power system lacking flexibility. Thus, more research focuses on enhancing the flexibility [...] Read more.
Integrated energy systems (IESs) are complex multisource supply systems with integrated source, grid, load, and storage systems, which can provide various flexible resources. Nowadays, there exists the phenomenon of a current power system lacking flexibility. Thus, more research focuses on enhancing the flexibility of power systems by considering the participation of IESs in distribution network optimization scheduling. Therefore, the optimal scheduling of IESs considering virtual heat storage and electric vehicles (EVs) is proposed in this paper. Firstly, the basic structure of IESs and mathematical models for the operation of the relevant equipment are presented. Then, an optimal scheduling strategy of an IES considering virtual heat storage and electric vehicles is proposed. Finally, an IES with an IEEE 33-node distribution network, 20-node Belgian natural gas network, and 44-node heating network topologies is selected to validate the proposed strategy. The proposed models of integrated demand response (IDR), EV orderly charging participation, virtual heat storage, and actual multitype energy storage devices play the role of peak shaving and valley filling, which also helps to reduce the scheduling cost from CNY 11,253.0 to CNY 11,184.4. The simulation results also demonstrate that the proposed model can effectively improve the operational economy of IESs, and the scheduling strategy can promote the consumption of renewable energy, with the wind curtailment rate decreasing from 63.62% to 12.50% and the solar curtailment rate decreasing from 56.92% to 21.34%. Full article
(This article belongs to the Special Issue Power and Energy Systems for E-mobility)
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17 pages, 5756 KiB  
Article
Physics-Informed Neural Network-Based Nonlinear Model Predictive Control for Automated Guided Vehicle Trajectory Tracking
by Yinping Li and Li Liu
World Electr. Veh. J. 2024, 15(10), 460; https://doi.org/10.3390/wevj15100460 - 10 Oct 2024
Viewed by 1074
Abstract
This paper proposes a nonlinear Model Predictive Control (MPC) method based on Physics-Informed Neural Networks (PINNs), aimed at enhancing the trajectory tracking performance of Automated Guided Vehicles (AGVs) in complex dynamic environments. Traditional physical models often face the challenges of computational inefficiency and [...] Read more.
This paper proposes a nonlinear Model Predictive Control (MPC) method based on Physics-Informed Neural Networks (PINNs), aimed at enhancing the trajectory tracking performance of Automated Guided Vehicles (AGVs) in complex dynamic environments. Traditional physical models often face the challenges of computational inefficiency and insufficient control precision when dealing with complex dynamic systems. However, by integrating physical laws directly into the training process of neural networks, PINNs can effectively learn and capture the kinematic characteristics of vehicles, replacing traditional nonlinear ordinary differential equation models and thus significantly enhancing computational efficiency and control performance. During the model-training phase, this study further incorporates the Theory of Functional Connections (TFC) and adaptive loss balancing strategies to efficiently solve ODE problems without relying on numerical integration and optimize the control strategy. This combined approach not only reduces computational complexity, but also improves the robustness and precision of the control strategy in varying environments. Numerical simulations demonstrate that this method offers significant advantages in AGV trajectory-tracking tasks, manifested in higher computational efficiency and precise control performance. The proposal of the PINN-MPC method provides new theoretical support and innovative methods for real-time complex system control, with important research and application potential, and is expected to play a key role in future intelligent control systems. Full article
(This article belongs to the Special Issue Intelligent Electric Vehicle Control, Testing and Evaluation)
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28 pages, 24761 KiB  
Article
Investigation of Drive Performance of Motors in Electric Loaders with Unequal Transmission Ratios—A Case Study
by Xiaotao Fei, Shaw Voon Wong, Muhammad Amin Azman, Peng Liu and Yunwu Han
World Electr. Veh. J. 2024, 15(10), 459; https://doi.org/10.3390/wevj15100459 - 10 Oct 2024
Viewed by 579
Abstract
Research on electric wheel loaders (EWLs) has predominantly focused on battery management, hybrid technologies, and energy recovery. However, the influence of motor types and drivetrains on the drive performance of EWLs has received little attention in previous studies. This case study addresses this [...] Read more.
Research on electric wheel loaders (EWLs) has predominantly focused on battery management, hybrid technologies, and energy recovery. However, the influence of motor types and drivetrains on the drive performance of EWLs has received little attention in previous studies. This case study addresses this gap by examining different EWL configurations and analyzing the drive theory and force requirements by integrating classic vehicle theory with EWL-specific characteristics. The study compares an original EWL, equipped with Permanent Magnet Synchronous Motors (PMSMs) on both the front and rear axles with identical transmission ratios of 22.85, to a modified EWL, which features a Switched Reluctance Motor (SRM) on the front axle and a transmission ratio of 44.05. Walking and shoveling tests were conducted to evaluate performance. The walking test results reveal that, at motor speeds of 200 rpm, 400 rpm, and 600 rpm, energy consumption in R-drive mode is 68.56%, 71.88%, and 74.87% of that in F-drive mode when two PMSMs are used. When an SRM is applied with a transmission ratio of 44.05, these values shift to 73.90%, 70.35%, and 67.72%, respectively. This demonstrates that using the rear motor alone for driving under walking conditions can yield greater energy savings. The shoveling test results indicate that distributing torque according to wheel load reduces rear wheel slippage, and the SRM with a transmission ratio of 44.05 delivers sufficient drive force while operating within a high-efficiency speed range for the EWL. Full article
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17 pages, 1941 KiB  
Article
An Approach for Estimating the Contributions of Various Real-World Usage Conditions towards the Attained Utility Factor of Plug-In Hybrid Electric Vehicles
by Karim Hamza, Kenneth Laberteaux and Kang-Ching Chu
World Electr. Veh. J. 2024, 15(10), 458; https://doi.org/10.3390/wevj15100458 - 9 Oct 2024
Viewed by 585
Abstract
Plug-in hybrid electric vehicles (PHEVs) are designed to enable the electrification of a large portion of the distance vehicles travel while utilizing relatively small batteries via taking advantage of the fact that long-distance travel days tend to be infrequent for many vehicle owners. [...] Read more.
Plug-in hybrid electric vehicles (PHEVs) are designed to enable the electrification of a large portion of the distance vehicles travel while utilizing relatively small batteries via taking advantage of the fact that long-distance travel days tend to be infrequent for many vehicle owners. PHEVs also relieve range anxiety through seamless switching to hybrid driving—an efficient mode of fuel-powered operation—whenever the battery reaches a low state of charge. Stemming from the perception that PHEVs are a well-rounded solution to reducing greenhouse gas (GHG) emissions, various metrics exist to infer the effectiveness of GHG reduction, with utility factor (UF) being prominent among such metrics. Recently, articles in the literature have called into question whether the theoretical values of UF agree with the real-world performance of PHEVs, while also suggesting that infrequent charging was the likely cause for observed deviations. However, it is understood that other reasons could also be responsible for UF mismatch. This work proposes an approach that combines theoretical modeling of UF under progressively relaxed assumptions (including the statistical distribution of daily traveled distance, charging behavior, and attainable electric range), along with vehicle data logs, to quantitatively infer the contributions of various real-world factors towards the observed mismatch between theoretical and real-world UF. A demonstration of the proposed approach using data from three real-world vehicles shows that all contributing factors could be significant. Although the presented results (via the small sample of vehicles) are not representative of the population, the proposed approach can be scaled to larger datasets. Full article
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2 pages, 535 KiB  
Correction
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
Viewed by 305
Abstract
In the original publication [...] Full article
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17 pages, 3232 KiB  
Article
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
Viewed by 521
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 (σc) 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  
Article
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
Viewed by 565
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  
Article
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
Viewed by 599
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
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33 pages, 10372 KiB  
Article
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
Viewed by 611
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
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20 pages, 29723 KiB  
Article
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
Viewed by 440
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
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15 pages, 13016 KiB  
Article
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
Viewed by 543
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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