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World Electr. Veh. J., Volume 15, Issue 6 (June 2024) – 46 articles

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15 pages, 3619 KiB  
Article
Mass, Centre of Gravity Location and Inertia Tensor of Electric Vehicles: Measured Data for Accurate Accident Reconstruction
by Giorgio Previati, Gianpiero Mastinu and Massimiliano Gobbi
World Electr. Veh. J. 2024, 15(6), 266; https://doi.org/10.3390/wevj15060266 - 17 Jun 2024
Abstract
Accurate accident reconstruction requires the knowledge of the mass properties of vehicles, namely the centre of gravity location, the mass and the inertia tensor. Such data are seldom available, especially in case of newly produced electric vehicles. In this paper, vehicle inertia measurements, [...] Read more.
Accurate accident reconstruction requires the knowledge of the mass properties of vehicles, namely the centre of gravity location, the mass and the inertia tensor. Such data are seldom available, especially in case of newly produced electric vehicles. In this paper, vehicle inertia measurements, performed at Politecnico di Milano, refer to a number of electric vehicles. In addition to the “simple” measurement of vehicle inertia, measured mass properties are analysed to derive the proper empirical formulae for the estimation of the centre of gravity height and the moments of inertia. Both internal combustion and electric vehicles are considered. Data show a significant difference in the mass properties of the two types of vehicles. The proposed formulae can be effectively employed to quickly obtain a reasonable estimation of the mass properties of any vehicle. The results show that electric vehicles are characterised by higher values of mass with respect to internal combustion vehicles, but they present a lower centre of gravity location and proportionally lower values of the moments of inertia. Full article
(This article belongs to the Special Issue Electric Vehicle Crash Safety Design)
31 pages, 2188 KiB  
Article
Evaluation of Sustainable Behavior and Acceptance of Electric Public Transportation: A Perspective from the Philippines
by Jill Angela C. Buenavista, Ardvin Kester S. Ong, Princess Jane Servas, Zsaliyah Kathrine Ibrahim, Kyla Catherine Gemala, Tanya Jeimiel Base, Lanz Julian L. Buenaseda, Curt Denver G. Solano and Jamilla Raye C. Yagin
World Electr. Veh. J. 2024, 15(6), 265; https://doi.org/10.3390/wevj15060265 (registering DOI) - 17 Jun 2024
Abstract
Rapid urbanization has exerted pressure for development on public transportation infrastructure. The rise in population has driven consumers to seek efficient, cost-effective, and environmentally sustainable transportation. The objective of this study was to assess the determinants influencing consumers’ behavioral intention and acceptance of [...] Read more.
Rapid urbanization has exerted pressure for development on public transportation infrastructure. The rise in population has driven consumers to seek efficient, cost-effective, and environmentally sustainable transportation. The objective of this study was to assess the determinants influencing consumers’ behavioral intention and acceptance of utilizing electric public transportation. The integrated UTAUT2 and sustainable theory of planned behavior underwent a higher-order construct using partial least squares structural equation modeling analysis to thoroughly evaluate key factors influencing the intention to accept electric public transportation. The study utilized a 55-item questionnaire distributed to 438 respondents. The findings indicated that the domains of UTAUT2 had the most significant effect, with hedonic motivation as the predominant variable, followed by effort expectancy and performance expectancy. This study indicated hedonic motivation as the primary factor influencing the intention to use electric public transportation, followed by effort expectancy. This study highlights the importance of ensuring user-friendly and convenient experience in the design and delivery of electric public transportation services. Substantial implications, both theoretical and practical, are also posited. Considering the impactful variables, this study deduced that the government, transportation sectors, and electric vehicle developers should place increased emphasis on enhancing customers’ intention to accept and use public transport in a sustainable manner. Full article
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21 pages, 3578 KiB  
Article
Assessment of User Preferences for In-Car Display Combinations during Non-Driving Tasks: An Experimental Study Using a Virtual Reality Head-Mounted Display Prototype
by Liang Li, Chacon Quintero Juan Carlos, Zijiang Yang and Kenta Ono
World Electr. Veh. J. 2024, 15(6), 264; https://doi.org/10.3390/wevj15060264 - 17 Jun 2024
Abstract
The goal of vehicular automation is to enhance driver comfort by reducing the necessity for active engagement in driving. This allows for the performance of non-driving-related tasks (NDRTs), with attention shifted away from the driving process. Despite this, there exists a discrepancy between [...] Read more.
The goal of vehicular automation is to enhance driver comfort by reducing the necessity for active engagement in driving. This allows for the performance of non-driving-related tasks (NDRTs), with attention shifted away from the driving process. Despite this, there exists a discrepancy between current in-vehicle display configurations and the escalating demands of NDRTs. This study investigates drivers’ preferences for in-vehicle display configurations within highly automated driving contexts. Utilizing virtual reality head-mounted displays (VR-HMDs) to simulate autonomous driving scenarios, this research employs Unity 3D Shape for developing sophisticated head movement tracking software. This setup facilitates the creation of virtual driving environments and the gathering of data on visual attention distribution. Employing an orthogonal experiment, this experiment methodically analyses and categorizes the primary components of in-vehicle display configurations to determine their correlation with visual immersion metrics. Additionally, this study incorporates subjective questionnaires to ascertain the most immersive display configurations and to identify key factors impacting user experience. Statistical analysis reveals that a combination of Portrait displays with Windshield Head-Up Displays (W-HUDs) is favored under highly automated driving conditions, providing increased immersion during NDRTs. This finding underscores the importance of tailoring in-vehicle display configurations to individual needs to avoid distractions and enhance user engagement. Full article
20 pages, 6629 KiB  
Article
Estimation of Road Adhesion Coefficient Based on Camber Brush Model
by Shupei Zhang, Hongcheng Zhu, Haichao Zhou, Yixiang Chen and Yue Liu
World Electr. Veh. J. 2024, 15(6), 263; https://doi.org/10.3390/wevj15060263 - 17 Jun 2024
Abstract
Electric vehicles, with their distinct power systems, weight distribution, and power control strategies compared to traditional vehicles, influence the pressure distribution in the tire contact area, thereby affecting the estimation of road adhesion coefficient. In electric vehicle research, tire adhesion coefficient serves as [...] Read more.
Electric vehicles, with their distinct power systems, weight distribution, and power control strategies compared to traditional vehicles, influence the pressure distribution in the tire contact area, thereby affecting the estimation of road adhesion coefficient. In electric vehicle research, tire adhesion coefficient serves as a measure of the frictional force between the vehicle and the road surface, directly impacting the vehicle’s handling performance. The accurate estimation of the adhesion coefficient aids drivers in better understanding the vehicle’s driving state. However, the existing brush models neglect differences in ground pressure distribution along the width direction of tires during tire camber, potentially leading to inaccuracies in adhesion coefficient estimation. This study proposes a camber brush tire model that considers the width-direction pressure distribution characteristics, aiming to enhance the accuracy of adhesion coefficient estimation under camber conditions. Experimental comparisons between the improved and original models reveal a significant enhancement in estimation precision. Consequently, the findings of this study provide valuable insights for deepening our understanding of tire camber dynamics and for designing control systems for electric vehicles, thereby improving vehicle stability and safety. Full article
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16 pages, 2122 KiB  
Review
Data and Energy Impacts of Intelligent Transportation—A Review
by Kaushik Rajashekara and Sharon Koppera
World Electr. Veh. J. 2024, 15(6), 262; https://doi.org/10.3390/wevj15060262 - 17 Jun 2024
Abstract
The deployment of intelligent transportation is still in its early stages and there are many challenges that need to be addressed before it can be widely adopted. Autonomous vehicles are a class of intelligent transportation that is rapidly developing, and they are being [...] Read more.
The deployment of intelligent transportation is still in its early stages and there are many challenges that need to be addressed before it can be widely adopted. Autonomous vehicles are a class of intelligent transportation that is rapidly developing, and they are being deployed in selected cities. A combination of advanced sensors, machine learning algorithms, and artificial intelligence are being used in these vehicles to perceive their environment, navigate, and make the right decisions. These vehicles leverage extensive data sourced from various sensors and computers integrated into the vehicle. Hence, massive computational power is required to process the information from various built-in sensors in milliseconds to make the right decision. The power required by the sensors and the use of additional computational power increases the energy consumption, and, hence, could reduce the range of the autonomous electric vehicle relative to a standard electric car and lead to additional emissions. A number of review papers have highlighted the environmental benefits of autonomous vehicles, focusing on aspects like optimized driving, improved route selection, fewer stops, and platooning. However, these reviews often overlook the significant energy demands of the hardware systems—such as sensors, computers, and cameras—necessary for full autonomy, which can decrease the driving range of electric autonomous vehicles. Additionally, previous studies have not thoroughly examined the data processing requirements in these vehicles. This paper provides a more detailed review of the volume of data and energy usage by various sensors and computers integral to autonomous features in electric vehicles. It also discusses the effects of these factors on vehicle range and emissions. Furthermore, the paper explores advanced technologies currently being developed by various industries to enhance processing speeds and reduce energy consumption in autonomous vehicles. Full article
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18 pages, 11151 KiB  
Article
Lightweight Type-IV Hydrogen Storage Vessel Boss Based on Optimal Sealing Structure
by Weidong Shao, Jing Wang, Donghai Hu, Dagang Lu and Yinjie Xu
World Electr. Veh. J. 2024, 15(6), 261; https://doi.org/10.3390/wevj15060261 - 15 Jun 2024
Viewed by 234
Abstract
The seal and weight of the Type IV hydrogen storage vessel are the key problems restricting the safety and driving range of fuel cell vehicles. The boss, as a metal medium connecting the inner liner of the Type IV hydrogen storage vessel with [...] Read more.
The seal and weight of the Type IV hydrogen storage vessel are the key problems restricting the safety and driving range of fuel cell vehicles. The boss, as a metal medium connecting the inner liner of the Type IV hydrogen storage vessel with the external pipeline, affects the sealing performance of the Type IV hydrogen storage vessel, and there is no academic research on the weight of the boss. Therefore, according to the force characteristics of the boss, this paper divides the upper and lower areas (valve column and plate). The valve column with seal optimization and light weight is manufactured with a 3D printing additive, while the plate bearing and transferring the internal pressure load is manufactured by forging. Firstly, a two-dimensional axisymmetric simulation model of the sealing ring was established, and the effects of different compression rates on its seal performance were analyzed. Then, the size and position of the sealing groove were sampled, simulated, and optimized based on the Latin Hypercube method, and the reliability of the optimal seal structure was verified by experiments. Finally, the Solid Isotropic Material with Penalization (SIMP) topology method was used to optimize the weight of the boss with optimal sealing structure, and the reconstructed model was checked and analyzed. The results show that the weight of the optimized boss is reduced by 9.6%. Full article
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16 pages, 1323 KiB  
Article
Global Patent Analysis of Battery Recycling Technologies: A Comparative Study of Korea, China, and the United States
by Chae-Hoon Lee
World Electr. Veh. J. 2024, 15(6), 260; https://doi.org/10.3390/wevj15060260 - 14 Jun 2024
Viewed by 724
Abstract
This study provides a comprehensive analysis of global patent trends in battery recycling, focusing on secondary batteries and related technologies across Korea, China, and the United States. The methodology involved collecting data from various patent databases, followed by quantitative analysis to identify technology [...] Read more.
This study provides a comprehensive analysis of global patent trends in battery recycling, focusing on secondary batteries and related technologies across Korea, China, and the United States. The methodology involved collecting data from various patent databases, followed by quantitative analysis to identify technology trends and guide future development. The research employed statistical tools to analyze patent activities, including the frequency and scope of patent filings, and comparative analysis to highlight differences between countries. This study reveals distinct emphases on technologies such as lithium-ion and waste battery recycling, highlighting notable differences in patent activities among key companies and countries. China’s large number of patents in battery manufacturing processes contrasts with the USA’s focus on electrochemical cell construction and storage systems, while Korea shows significant activity in waste battery technology. The novelty of this paper lies in its detailed comparative analysis of patent trends across these three major economies, providing insights into the technological focuses and priorities of each country. The study also identifies key challenges, such as the need for consistent innovation and broader geographic coverage in Korea, enhancing patent influence and international presence in China, and ensuring high patent quality and fostering innovation in lagging sectors in the United States. Addressing these challenges through enhanced collaboration, increased R&D investments, and supportive policies is crucial for strengthening the global position and driving further innovation in the battery recycling sector. Full article
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15 pages, 10922 KiB  
Article
An Automatic Emergency Braking Control Method for Improving Ride Comfort
by Fei Lai, Junbo Liu and Yuanzhi Hu
World Electr. Veh. J. 2024, 15(6), 259; https://doi.org/10.3390/wevj15060259 - 14 Jun 2024
Viewed by 264
Abstract
The contribution of this paper is to present an automatic emergency braking (AEB) optimized algorithm based on time to collision (TTC) and a professional driver fitting (PDF) braking pattern. When the TTC value is less than the given threshold, the PDF control algorithm [...] Read more.
The contribution of this paper is to present an automatic emergency braking (AEB) optimized algorithm based on time to collision (TTC) and a professional driver fitting (PDF) braking pattern. When the TTC value is less than the given threshold, the PDF control algorithm will be started, and vice versa. According to the standard test scenarios for passenger cars and commercial vehicles, the simulation analysis on the AEB systems using four different control algorithms, namely TTC, quadratic curve deceleration, PDF and proposed optimized control algorithm, is conducted, respectively. The results show that the proposed optimization algorithm can both meet the standard requirements and improve the ride comfort. While ensuring collision avoidance with the preceding vehicle, the control algorithm proposed in this study offers better braking comfort compared to the TTC algorithm and the quadratic curve deceleration algorithm. Additionally, it provides a more appropriate stopping distance compared to the PDF algorithm. Full article
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18 pages, 931 KiB  
Article
Exploring User Attitudes and Behavioral Intentions towards Augmented Reality Automotive Assistants: A Mixed-Methods Approach
by Fucheng Wan, Jian Teng and Lisi Feng
World Electr. Veh. J. 2024, 15(6), 258; https://doi.org/10.3390/wevj15060258 - 12 Jun 2024
Viewed by 231
Abstract
As augmented reality (AR) technology is increasingly permeating the automotive industry, this study investigates users’ attitudes towards AR automotive assistants and their impact on usage behavior. Using the theory of reasoned action (TRA) and integrating insights from the Kano model, critical factors driving [...] Read more.
As augmented reality (AR) technology is increasingly permeating the automotive industry, this study investigates users’ attitudes towards AR automotive assistants and their impact on usage behavior. Using the theory of reasoned action (TRA) and integrating insights from the Kano model, critical factors driving user acceptance and engagement were identified. The research reveals that trust in AR technology, perceived utility, and ease of interaction are prioritized by users. Clustering analysis identified three distinct user groups: a ‘Safety-Conscious Group’, a ‘Technology Enthusiast Group’, and an ‘Experience-Seeking Group’, each displaying unique preferences towards AR features. Additionally, a support vector machine (SVM) model effectively predicted user behavior with a training set accuracy of 89.96%. These findings provide valuable insights for the design and marketing of AR automotive assistants, acknowledging both essential features and delighters identified through the Kano model. By understanding user preferences and expectations, tailored AR solutions can be developed to enhance user satisfaction and adoption rates in the automotive sector. Moreover, this research contributes to the sustainable development goals related to the automotive industry by fostering innovation in vehicle technology, promoting eco-friendly driving practices, and enhancing overall mobility efficiency. Full article
25 pages, 5301 KiB  
Article
Improved Model Predictive Control Path Tracking Approach Based on Online Updated Algorithm with Fuzzy Control and Variable Prediction Time Domain for Autonomous Vehicles
by Binshan Liu, Zhaoqiang Wang, Hui Guo and Guoxiang Zhang
World Electr. Veh. J. 2024, 15(6), 257; https://doi.org/10.3390/wevj15060257 - 12 Jun 2024
Viewed by 196
Abstract
The design of trajectory tracking controllers for smart driving cars still faces problems, such as uncertain parameters and it being time-consuming. To improve the tracking performance of the trajectory tracking controller and reduce the computation of the controller, this paper proposes an improved [...] Read more.
The design of trajectory tracking controllers for smart driving cars still faces problems, such as uncertain parameters and it being time-consuming. To improve the tracking performance of the trajectory tracking controller and reduce the computation of the controller, this paper proposes an improved model predictive control (MPC) method based on fuzzy control and an online update algorithm. First, a vehicle dynamics model is constructed and a feedforward MPC controller is designed; second, a real-time updating method of the time domain parameters is proposed to replace the previous method of empirically selecting the time domain parameters; lastly, a fuzzy controller is proposed for the real-time adjustment of the weight coefficient matrix of the model predictive controller according to the lateral and heading errors of the vehicle, and a state matrix-based cosine similarity updating mechanism is developed for determining the updating nodes of the state matrix to reduce the controller computation caused by the continuous updating of the state matrix when the longitudinal vehicle speed changes. Finally, the controller is compared with the traditional model prediction controller through the co-simulation of CARSIM and MATLAB/Simulink, and the results show that the controller has great improvement in terms of tracking accuracy and controller computational load. Full article
(This article belongs to the Special Issue Dynamics, Control and Simulation of Electrified Vehicles)
15 pages, 6494 KiB  
Article
Design and Construction of a Multipole Electric Motor Using an Axial Flux Configuration
by Adrián González-Parada, Francisco Moreno Del Valle and Ricard Bosch-Tous
World Electr. Veh. J. 2024, 15(6), 256; https://doi.org/10.3390/wevj15060256 - 12 Jun 2024
Viewed by 292
Abstract
In the transportation industry, the use of renewable energies has been implemented in conjunction with the development of higher-power electric motors and, consequently, the development of intelligent control systems for torque and speed control. Currently, the coupling between both systems is being developed [...] Read more.
In the transportation industry, the use of renewable energies has been implemented in conjunction with the development of higher-power electric motors and, consequently, the development of intelligent control systems for torque and speed control. Currently, the coupling between both systems is being developed through mechanical systems, affecting the efficient transmission of energy and the useful life of the components. On the other hand, new configurations of electric motors are being developed, such as axial flux motors (AFM), because these can be coupled directly without a mechanical coupling, given their characteristics of high torque at low speeds. In the present work, an innovative design of a multipole axial flux motor (MAFM) is introduced. General criteria for the design and construction are presented considering the geometry in axial flux and permanent magnets. The performance of the system is evaluated through finite element magnetic simulations (FEMM) and compared with experimental measurements of the developed prototype; confirming the effectiveness of the design, obtaining torques of up to 1.784 Nm without extra mechanical couplings and maximum speed regulation errors of 8.43%. The motor was controlled by a digital pole switching system whit six control mode, applied to a permanent magnet MFA for speed and torque control at constant speed. This control can be extended to conventional radial flux electric motor configurations and intelligent traction applications, based on torque demand. Full article
(This article belongs to the Topic Advanced Electrical Machine Design and Optimization Ⅱ)
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19 pages, 2999 KiB  
Article
Novel Deep Learning Domain Adaptation Approach for Object Detection Using Semi-Self Building Dataset and Modified YOLOv4
by Ahmed Gomaa and Ahmad Abdalrazik
World Electr. Veh. J. 2024, 15(6), 255; https://doi.org/10.3390/wevj15060255 - 12 Jun 2024
Viewed by 289
Abstract
Moving object detection is a vital research area that plays an essential role in intelligent transportation systems (ITSs) and various applications in computer vision. Recently, researchers have utilized convolutional neural networks (CNNs) to develop new techniques in object detection and recognition. However, with [...] Read more.
Moving object detection is a vital research area that plays an essential role in intelligent transportation systems (ITSs) and various applications in computer vision. Recently, researchers have utilized convolutional neural networks (CNNs) to develop new techniques in object detection and recognition. However, with the increasing number of machine learning strategies used for object detection, there has been a growing need for large datasets with accurate ground truth used for the training, usually demanding their manual labeling. Moreover, most of these deep strategies are supervised and only applicable for specific scenes with large computational resources needed. Alternatively, other object detection techniques such as classical background subtraction need low computational resources and can be used with general scenes. In this paper, we propose a new a reliable semi-automatic method that combines a modified version of the detection-based CNN You Only Look Once V4 (YOLOv4) technique and background subtraction technique to perform an unsupervised object detection for surveillance videos. In this proposed strategy, background subtraction-based low-rank decomposition is applied firstly to extract the moving objects. Then, a clustering method is adopted to refine the background subtraction (BS) result. Finally, the refined results are used to fine-tune the modified YOLO v4 before using it in the detection and classification of objects. The main contribution of this work is a new detection framework that overcomes manual labeling and creates an automatic labeler that can replace manual labeling using motion information to supply labeled training data (background and foreground) directly from the detection video. Extensive experiments using real-world object monitoring benchmarks indicate that the suggested framework obtains a considerable increase in mAP compared to state-of-the-art results on both the CDnet 2014 and UA-DETRAC datasets. Full article
(This article belongs to the Special Issue Electric Vehicle Autonomous Driving Based on Image Recognition)
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14 pages, 2442 KiB  
Article
Research on Filtering Algorithm of Vehicle Dynamic Weighing Signal
by Lingcong Xiong, Tieyi Zhang, Anlu Yuan and Zhipeng Zhang
World Electr. Veh. J. 2024, 15(6), 254; https://doi.org/10.3390/wevj15060254 - 12 Jun 2024
Viewed by 307
Abstract
This study analyzes the advantages and disadvantages of filtering algorithms for dynamic weighing signals. Highway road surface has road surface unevenness and other influencing factors. The body vibration of the vehicle driving process produces a certain amount of interference signals collected by the [...] Read more.
This study analyzes the advantages and disadvantages of filtering algorithms for dynamic weighing signals. Highway road surface has road surface unevenness and other influencing factors. The body vibration of the vehicle driving process produces a certain amount of interference signals collected by the load cell to form noise signals. In addition, piezoelectric sensors and amplification circuits introduce a large amount of electrical noise. These noise signals are non-smooth, nonlinear, and have other characteristics. We study the filtering effects of moving average (MA), wavelet transform (WT), and variational mode decomposition (VMD) filtering algorithms on axle weight signals and evaluate the performance of the filtering algorithms through the Root Mean Square Error (RMSE), signal-to-noise ratio (SNR), and Normalized Correlation Coefficient (NCC). The comprehensive analysis shows that the variational modal decomposition filtering algorithm is more advantageous for axial weight signal processing. The design of the axle weight signal noise filtering algorithm is of great significance for improving the accuracy of the overall dynamic weighing system of the vehicle. Full article
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22 pages, 1375 KiB  
Article
Multi-Cell Cooperative Resource Allocation and Performance Evaluation for Roadside-Assisted Automated Driving
by Shu Yang, Xuanhan Zhu, Yang Li, Quan Yuan and Lili Li
World Electr. Veh. J. 2024, 15(6), 253; https://doi.org/10.3390/wevj15060253 - 11 Jun 2024
Viewed by 378
Abstract
The proliferation of wireless technologies, particularly the advent of 5G networks, has ushered in transformative possibilities for enhancing vehicular communication systems, particularly in the context of autonomous driving. Leveraging sensory data and mapping information downloaded from base stations using I2V links, autonomous vehicles [...] Read more.
The proliferation of wireless technologies, particularly the advent of 5G networks, has ushered in transformative possibilities for enhancing vehicular communication systems, particularly in the context of autonomous driving. Leveraging sensory data and mapping information downloaded from base stations using I2V links, autonomous vehicles in these networks present the promise of enabling distant perceptual abilities essential to completing various tasks in a dynamic environment. However, the efficient down-link transmission of vehicular network data via base stations, often relying on spectrum sharing, presents a multifaceted challenge. This paper addresses the intricacies of spectrum allocation in vehicular networks, aiming to resolve the thorny issues of cross-station interference and coupling while adapting to the dynamic and evolving characteristics of the vehicular environment. A novel approach is suggested involving the utilization of a multi-agent option-critic reinforcement learning algorithm. This algorithm serves a dual purpose: firstly, it learns the most efficient way to allocate spectrum resources optimally. Secondly, it adapts to the ever-changing dynamics of the environment by learning various policy options tailored to different situations. Moreover, it identifies the conditions under which a switch between these policy options is warranted as the situation evolves. The proposed algorithm is structured in two layers, with the upper layer consisting of policy options that are shared across all agents, and the lower layer comprising intra-option policies executed in a distributed manner. Through experimentation, we showcase the superior spectrum efficiency and communication quality achieved by our approach. Specifically, our approach outperforms the baseline methods in terms of training average reward convergence stability and the transmission success rate. Control-variable experiments also reflect the better adaptability of the proposed method as the environmental conditions change, underscoring the significant potential of the proposed method in aiding successful down-link transmissions in vehicular networks. Full article
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22 pages, 6301 KiB  
Article
Intelligent Vehicle Formation System Based on Information Interaction
by Peng Wang, Tao Ouyang, Shixin Zhao, Xuelin Wang, Zhewen Ni and Yuezhen Fan
World Electr. Veh. J. 2024, 15(6), 252; https://doi.org/10.3390/wevj15060252 - 11 Jun 2024
Viewed by 453
Abstract
Urban traffic congestion has become an increasingly serious problem, and the transportation industry is gradually becoming a high-energy-consuming industry. Intelligent Transportation System (ITSs) that integrate technologies such as electronic sensing, data transmission, and intelligent control have emerged as a new approach to fundamentally [...] Read more.
Urban traffic congestion has become an increasingly serious problem, and the transportation industry is gradually becoming a high-energy-consuming industry. Intelligent Transportation System (ITSs) that integrate technologies such as electronic sensing, data transmission, and intelligent control have emerged as a new approach to fundamentally solving transportation problems. As one of the cores of intelligent transportation systems, multi-vehicle formation technology has the advantage of promoting vehicle information interaction, improving vehicle mobility, and enhancing traffic conditions. Due to the high cost and risk of conducting multi-vehicle formation experiments using real vehicles, experimenting with intelligent vehicles has become a viable option. Based on the leader–follower formation strategy, this study designed an intelligent vehicle formation system using the Arduino platform. It utilizes infrared sensors, ultrasonic sensors, and photoelectric encoders to perceive information about the vehicle fleet and the road. Information is aggregated to the master vehicle through ZigBee communication modules. The controller of the master vehicle applies a PID algorithm, combined with a differential steering model, to solve the speed instructions for each vehicle in the fleet. Motion control instructions are then transmitted to each slave vehicle through ZigBee communication modules, enabling the automatic adjustment of the fleet’s traveling speed and spacing. Additionally, a Bluetooth app has been designed for users to monitor and control the movement status of the fleet dynamically in real time. Experimental verification has shown that this research effectively improves intelligent fleets’ capabilities in environmental perception, intelligent decision-making, collaborative control, and motion execution. It also enhances road traffic efficiency and safety, providing new ideas and methods for the development of autonomous driving technology. Full article
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16 pages, 3821 KiB  
Article
State-Feedback and Nonsmooth Controller Design for Truck Platoon Subject to Uncertainties and Disturbances
by Jianbo Feng, Zepeng Gao and Bingying Guo
World Electr. Veh. J. 2024, 15(6), 251; https://doi.org/10.3390/wevj15060251 - 11 Jun 2024
Viewed by 375
Abstract
Intelligent truck platoons can benefit road transportation due to the short gap and better fuel economy, but they are also subject to dynamic uncertainties and external disturbances. Therefore, this paper develops a novel robust control algorithm for connected truck platoons. By introducing a [...] Read more.
Intelligent truck platoons can benefit road transportation due to the short gap and better fuel economy, but they are also subject to dynamic uncertainties and external disturbances. Therefore, this paper develops a novel robust control algorithm for connected truck platoons. By introducing a linearized expression method of platoon error dynamics based on state measurement, the state feedback mechanism combined with a nonsmooth controller for a truck platoon is proposed in the development of the distributed control method. The state-feedback controller can drive the nominal platoon system to the state of second-order consensus, and the nonsmooth controller counterparts the uncertainties and disturbances. The convergence and string stability of the proposed control algorithm are demonstrated both theoretically and experimentally, and the effectiveness and robustness are also verified by simulation tests. Full article
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24 pages, 7565 KiB  
Article
Simulation and Testing of Self-Reconfigurable Battery Advanced Functions for Automotive Application
by Rémy Thomas, Nicolas Léto, Jérome Lachaize, Sylvain Bacquet, Yan Lopez and Leandro Cassarino
World Electr. Veh. J. 2024, 15(6), 250; https://doi.org/10.3390/wevj15060250 - 8 Jun 2024
Viewed by 346
Abstract
This article presents the design and production work carried out jointly by Vitesco Technologies and the CEA in order to build a Self-Reconfigurable Battery (SRB) demonstrator representative of an electric vehicle traction battery pack. The literature demonstrates that the use of an SRB [...] Read more.
This article presents the design and production work carried out jointly by Vitesco Technologies and the CEA in order to build a Self-Reconfigurable Battery (SRB) demonstrator representative of an electric vehicle traction battery pack. The literature demonstrates that the use of an SRB allows for individual bypassing or serialization of each cell in a battery pack, enabling control of the voltage output and dynamic balancing of the battery pack during all phases of vehicle use. The simulations and tests presented in this article confirm that the use of an SRB results in a 6% reduction in energy consumption compared to a Conventional Battery Pack (CBP) on a driving profile based on WLTP cycles. Additionally, an SRB enhances fast charging performance, with a charging time that is 22% faster than a CBP. Furthermore, it is shown that an SRB without a voltage inversion capability can still be connected directly to the AC grid for charging without the need for a dedicated converter, using only a single diode bridge rectifier for the whole system. Full article
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26 pages, 1364 KiB  
Article
Joint Estimation of Driving State and Road Surface Adhesion Coefficient of a Four-Wheel Independent and Steering-Drive Electric Vehicle
by Zhixin Chen, Gang Li, Zhihua Zhang and Ruolan Fan
World Electr. Veh. J. 2024, 15(6), 249; https://doi.org/10.3390/wevj15060249 - 7 Jun 2024
Viewed by 308
Abstract
Vehicle running state parameters and road surface state are crucial to the stability of four-wheel independent drive and steering electric vehicle control. Therefore, this study explores the estimation of vehicle driving state parameters and road surface adhesion coefficients using a combination of federal [...] Read more.
Vehicle running state parameters and road surface state are crucial to the stability of four-wheel independent drive and steering electric vehicle control. Therefore, this study explores the estimation of vehicle driving state parameters and road surface adhesion coefficients using a combination of federal Kalman filtering and an intelligent bionic antlion optimization algorithm. Firstly, according to the research purpose of the paper and the focus on the accuracy of the establishment of the three degrees of freedom dynamics model, fully considering the road conditions, the paper adopts the Dugoff tire model and finally completes the establishment of the vehicle state estimation model. Secondly, the drive state estimation algorithm is developed utilizing the principles of federal Kalman filtering and volume Kalman filtering. At the same time, robust estimation theory is introduced into the sub-filter, and the antlion optimization module is designed at the lower layer of the main filter to enhance the accuracy of estimates. It is easy to see that the design of the Antlion federal Kalman travel state estimation algorithm has noticeably enhanced accuracy and traceability, according to the result. Thirdly, a joint estimation algorithm of state estimation and road surface adhesion coefficient has been devised to enhance the stability and precision of the estimation process. Finally, the results showed that the joint estimation algorithm has high accuracy in estimating vehicle driving state parameters such as the center of mass lateral deflection angle and road surface adhesion coefficient by simulation. Full article
12 pages, 3201 KiB  
Article
State of Health Prediction of Lithium-Ion Batteries Based on Multi-Kernel Relevance Vector Machine and Error Compensation
by Li Zhang, Chao Sun and Shilin Liu
World Electr. Veh. J. 2024, 15(6), 248; https://doi.org/10.3390/wevj15060248 - 6 Jun 2024
Viewed by 258
Abstract
Though lithium-ion batteries are extensively applied in electric vehicles as a power source due to their excellent advantages in recent years, the security risk has inarguably always existed. The state of health (SOH) of lithium-ion batteries is one of the most important indicators [...] Read more.
Though lithium-ion batteries are extensively applied in electric vehicles as a power source due to their excellent advantages in recent years, the security risk has inarguably always existed. The state of health (SOH) of lithium-ion batteries is one of the most important indicators related to security, the prediction of SOH is paid close attention spontaneously. To improve the prediction accuracy of SOH, this paper constructs an SOH prediction model based on a multi-kernel relevance vector machine and error compensation (EC-MKRVM). The provided model comprises a pre-estimation model and an error compensation model, both of which use the multi-kernel relevance vector machine (MKRVM) algorithm. The pre-estimation model takes the feature factors extracted in the charging segment as the input variable and the SOH pre-estimation value as the output. The error compensation model takes the pre-estimation error sequence as the input variable and the SOH prediction error as the output. Finally, the SOH prediction error is used to compensate for the SOH pre-estimation value of the pre-estimation model, and the final SOH prediction value is obtained. To verify the effectiveness and advancement of the model, the CACLE dataset is used for comparative experimental analysis. The results show that the proposed prediction model in this paper has higher prediction accuracy. Full article
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18 pages, 8352 KiB  
Article
All-Wheel Steering Tracking Control Method for Virtual Rail Trains with Only Interoceptive Sensors
by Zhenpo Wang, Yi Zhang and Zhifu Wang
World Electr. Veh. J. 2024, 15(6), 247; https://doi.org/10.3390/wevj15060247 - 4 Jun 2024
Viewed by 424
Abstract
A virtual rail train (VRT) is a multi-articulated vehicle as well as a novel public transportation system due to its low economic cost, environmental friendliness and high transit capacity. Equipped with all-wheel steering (AWS) and a tracking control method, the super long VRT [...] Read more.
A virtual rail train (VRT) is a multi-articulated vehicle as well as a novel public transportation system due to its low economic cost, environmental friendliness and high transit capacity. Equipped with all-wheel steering (AWS) and a tracking control method, the super long VRT can travel on urban roads easily. This paper proposed a tracking control approach using only interoceptive sensors with high scene adaptivity. The kinematic model was established first under reasonable assumptions when the sensor configuration was completed simultaneously. A hierarchical controller consists of a front axle controller and a rear axle controller. The former applies virtual axles theory to avoid motion interference. The latter generates a first-axle reference path with path segmentation and a data updating method to improve storage and computational efficiency. Then, a fast curvature matching rear axles control method is developed with an actuator time delay considered. Finally, the proposed approach is verified in a hardware in loop (HIL) simulation under various situations with predefined evaluation standards, which shows better tracking performance and applicability. Full article
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14 pages, 3752 KiB  
Article
A Comparative Study of Traffic Signal Control Based on Reinforcement Learning Algorithms
by Chen Ouyang, Zhenfei Zhan and Fengyao Lv
World Electr. Veh. J. 2024, 15(6), 246; https://doi.org/10.3390/wevj15060246 - 4 Jun 2024
Viewed by 462
Abstract
In recent years, the increasing production and sales of automobiles have led to a notable rise in congestion on urban road traffic systems, particularly at ramps and intersections with traffic signals. Intelligent traffic signal control represents an effective means of addressing traffic congestion. [...] Read more.
In recent years, the increasing production and sales of automobiles have led to a notable rise in congestion on urban road traffic systems, particularly at ramps and intersections with traffic signals. Intelligent traffic signal control represents an effective means of addressing traffic congestion. Reinforcement learning methods have demonstrated considerable potential for addressing complex traffic signal control problems with multidimensional states and actions. In this research, the team propose Q-learning and Deep Q-Network (DQN) based signal control frameworks that use variable phase sequences and cycle times to adjust the order and the duration of signal phases to obtain a stable traffic signal control strategy. Experiments are simulated using the traffic simulator Simulation of Urban Mobility (SUMO) to test the average speed and the lane occupancy rate of vehicles entering the ramp to evaluate its safety performance and test the vehicle’s traveling time to assess its stability. The simulation results show that both reinforcement learning algorithms are able to control cars in dynamic traffic environments with higher average speed and lower lane occupancy rate than the no-control method and that the DQN control model improves the average speed by about 10% and reduces the lane occupancy rate by about 30% compared to the Q-learning control model, providing a higher safety performance. Full article
(This article belongs to the Special Issue Development towards Vehicle Safety in Future Smart Traffic Systems)
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21 pages, 1881 KiB  
Review
Beyond Tailpipe Emissions: Life Cycle Assessment Unravels Battery’s Carbon Footprint in Electric Vehicles
by Sharath K. Ankathi, Jessey Bouchard and Xin He
World Electr. Veh. J. 2024, 15(6), 245; https://doi.org/10.3390/wevj15060245 - 2 Jun 2024
Viewed by 444
Abstract
While electric vehicles (EVs) offer lower life cycle greenhouse gas emissions in some regions, the concern over the greenhouse gas emissions generated during battery production is often debated. This literature review examines the true environmental trade-offs between conventional lithium-ion batteries (LIBs) and emerging [...] Read more.
While electric vehicles (EVs) offer lower life cycle greenhouse gas emissions in some regions, the concern over the greenhouse gas emissions generated during battery production is often debated. This literature review examines the true environmental trade-offs between conventional lithium-ion batteries (LIBs) and emerging technologies such as solid-state batteries (SSBs) and sodium-ion batteries (SIBs). It emphasizes the carbon-intensive nature of LIB manufacturing and explores how alternative technologies can enhance efficiency while reducing the carbon footprint. We have used a keyword search technique to review articles related to batteries and their environmental performances. The study results reveal that the greenhouse gas (GHG) emissions of battery production alone range from 10 to 394 kgCO2 eq./kWh. We identified that lithium manganese cobalt oxide and lithium nickel cobalt aluminum oxide batteries, despite their high energy density, exhibit higher GHGs (20–394 kgCO2 eq./kWh) because of the cobalt and nickel production. Lithium iron phosphate (34–246 kgCO2 eq./kWh) and sodium-ion (40–70 kgCO2 eq./kWh) batteries showed lower environmental impacts because of the abundant feedstock, emerging as a sustainable choice, especially when high energy density is not essential. This review also concludes that the GHGs of battery production are highly dependent on the regional grid carbon intensity. Batteries produced in China, for example, have higher GHGs than those produced in the United States (US) and European Union (EU). Understanding the GHGs of battery production is critical to fairly evaluating the environmental impact of battery electric vehicles. Full article
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45 pages, 21564 KiB  
Article
Research on a Multi-Strategy Improved Sand Cat Swarm Optimization Algorithm for Three-Dimensional UAV Trajectory Path Planning
by Lili Liu, Yixin Lu, Bufan Yang, Longyue Yang, Jianyong Zhao, Yue Chen and Longhai Li
World Electr. Veh. J. 2024, 15(6), 244; https://doi.org/10.3390/wevj15060244 - 31 May 2024
Viewed by 219
Abstract
In response to the issues of premature convergence, lack of population diversity, and poor convergence accuracy in the traditional Sand Cat Swarm Optimization (SCSO) algorithm, a Multi-Strategy Improved SCSO (MISCSO) algorithm is proposed. Firstly, multiple population strategies are used to avoid premature convergence [...] Read more.
In response to the issues of premature convergence, lack of population diversity, and poor convergence accuracy in the traditional Sand Cat Swarm Optimization (SCSO) algorithm, a Multi-Strategy Improved SCSO (MISCSO) algorithm is proposed. Firstly, multiple population strategies are used to avoid premature convergence and falling into local optima traps. Secondly, a distribution estimation learning strategy is introduced to represent the relationships between individuals, using probability models to improve algorithm performance. Next, the diversity of candidate solutions in the elite pool is utilized to expand the search space and enhance the algorithm’s ability to avoid local solutions. Lastly, a Cauchy disturbance strategy is adopted to accelerate the convergence speed of the algorithm, thereby improving the search efficiency and convergence accuracy. The experimental results of CEC2017 tests show that the improved algorithm balances convergence speed and global search capabilities effectively. Finally, the algorithm is applied to actual drone path planning and compared with six other intelligent algorithms, demonstrating the practicality and effectiveness of the improved algorithm. Full article
20 pages, 5062 KiB  
Article
Adaptive Fuzzy Control of an Electronic Differential Based on the Stability Criterion of the Phase Plane Method
by Shaopeng Zhu, Yekai Xu, Linlin Li, Yong Ren, Chenyang Kuang, Huipeng Chen and Jian Gao
World Electr. Veh. J. 2024, 15(6), 243; https://doi.org/10.3390/wevj15060243 - 31 May 2024
Viewed by 306
Abstract
To improve the handling stability of distributed drive electric vehicles, this paper introduces an electronic differential control strategy based on the stability criterion of the phase plane method. The strategy first plots the distributed electric vehicle’s center of mass side angle and center [...] Read more.
To improve the handling stability of distributed drive electric vehicles, this paper introduces an electronic differential control strategy based on the stability criterion of the phase plane method. The strategy first plots the distributed electric vehicle’s center of mass side angle and center of mass angular speed on the phase plane, and then it analyzes the vehicle’s stability under various working conditions to determine the parameters that ensure the stability performance. Subsequently, an adaptive fuzzy control strategy is employed to achieve fast and accurate distribution of the torque to each wheel, thereby enhancing the vehicle’s stability. A joint simulation platform is constructed using MATLAB/Simulink and CarSim. A comparison with the traditional electronic differential strategy demonstrates that the proposed distribution strategy based on phase plane stability exhibited excellent stability. Full article
(This article belongs to the Special Issue Dynamics, Control and Simulation of Electrified Vehicles)
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16 pages, 624 KiB  
Article
Research on Vertical Cooperation and Pricing Strategy of Electric Vehicle Supply Chain
by Dou-Dou Wu
World Electr. Veh. J. 2024, 15(6), 242; https://doi.org/10.3390/wevj15060242 - 30 May 2024
Viewed by 402
Abstract
To determine a vertical cooperation strategy and address the optimal pricing problem of the electric vehicle (EV) supply chain, a supply chain system consisting of two competing EV manufacturers (M1 and M2) and a battery supplier is studied. Firstly, three [...] Read more.
To determine a vertical cooperation strategy and address the optimal pricing problem of the electric vehicle (EV) supply chain, a supply chain system consisting of two competing EV manufacturers (M1 and M2) and a battery supplier is studied. Firstly, three cooperation strategy models were constructed for the battery supplier and the EV manufacturers, namely: Strategy N (neither the battery supplier nor the two manufacturers cooperate with each other); Strategy I (M1 cooperates with the battery supplier); and Strategy II (M2 cooperates with the battery supplier). Then, the Stackelberg solution method was used to obtain the optimal equilibrium decisions under the three strategic models. Finally, the effect of the preference coefficient of consumers for leasing EVs per unit on the optimal equilibrium decision was analyzed. We found that: (1) The wholesale price of batteries provided by the battery supplier to M1 is always greater than to M2. (2) Strategies I and II prompt M1 and M2 to reduce the unit and fixed rental prices of EVs to some extent, while intensifying the competition between the two manufacturers in terms of EV lease prices. (3) When the consumer preference coefficient (θ) for leasing EVs per unit provided by manufacturer M1 is relatively small, the cooperation alliance S2 and the supply chain achieve the maximum profit under Strategy II; however, while θ is large, M1, cooperative alliance S1, and the entire supply chain could benefit the most under Strategy I. Full article
17 pages, 1231 KiB  
Article
Study on Obstacle Detection Method Based on Point Cloud Registration
by Hongliang Wang, Jianing Wang, Yixin Wang, Dawei Pi, Yijie Chen and Jingjing Fan
World Electr. Veh. J. 2024, 15(6), 241; https://doi.org/10.3390/wevj15060241 - 30 May 2024
Viewed by 333
Abstract
An efficient obstacle detection system is one of the most important guarantees for improving the active safety performance of autonomous vehicles. This paper proposes an obstacle detection method based on high-precision positioning applied to blocked zones to solve the problems of the high [...] Read more.
An efficient obstacle detection system is one of the most important guarantees for improving the active safety performance of autonomous vehicles. This paper proposes an obstacle detection method based on high-precision positioning applied to blocked zones to solve the problems of the high complexity of detection results, low computational efficiency, and high load in traditional obstacle detection methods. Firstly, an NDT registration method which uses the likelihood function as the optimal value of the registration score function to calculate the registration parameters is designed to match the scanning point cloud and the target point cloud. Secondly, a target reduction method combined with threshold judgment and the binary tree search algorithm is designed to filter the point cloud of non-road obstacles to improve the processing speed of the computing platform. Meanwhile, KD-tree is used to speed up the clustering process. Finally, a vehicle remote control simulation platform with the combination of a cloud platform and mobile terminal is designed to verify the effectiveness of the strategy in practical application. The results prove that the proposed obstacle detection method can improve the efficiency and accuracy of detection. Full article
19 pages, 12741 KiB  
Article
Theoretical Analysis of Plate-Type Thermoelectric Generator for Fluid Waste Heat Recovery Using Thermal Resistance and Numerical Models
by Yongfei Jia, Ruochen Wang and Jie Chen
World Electr. Veh. J. 2024, 15(6), 240; https://doi.org/10.3390/wevj15060240 - 30 May 2024
Viewed by 266
Abstract
In current research, there are excessive assumptions and simplifications in the mathematical models developed for thermoelectric generators. In this study, a comprehensive mathematical model was developed based on a plate-type thermoelectric generator divided into multiple thermoelectric units. The model takes into account temperature-dependent [...] Read more.
In current research, there are excessive assumptions and simplifications in the mathematical models developed for thermoelectric generators. In this study, a comprehensive mathematical model was developed based on a plate-type thermoelectric generator divided into multiple thermoelectric units. The model takes into account temperature-dependent thermoelectric material parameters and fluid flow. The model was validated, and a maximum error of 6.4% was determined. Moreover, the model was compared and analyzed with a numerical model, with a maximum discrepancy of 7.2%. The model revealed the factors and their degree of influence on the performance of the thermoelectric generator unit. In addition, differences in temperature distribution, output power, and conversion efficiency between multiple thermoelectric units were clearly studied. This study can guide modeling and some optimization measures to improve the overall performance of thermoelectric generators. Full article
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19 pages, 7279 KiB  
Article
Decoupled Adaptive Motion Control for Unmanned Tracked Vehicles in the Leader-Following Task
by Jingjing Fan, Pengxiang Yan, Ren Li, Yi Liu, Falong Wang, Yingzhe Liu and Chang Chen
World Electr. Veh. J. 2024, 15(6), 239; https://doi.org/10.3390/wevj15060239 - 30 May 2024
Viewed by 277
Abstract
As a specific task for unmanned tracked vehicles, leader-following imposes high-precision requirements on the vehicle’s motion control, especially the steering control. However, due to characteristics such as the frequent changes in off-road terrain and steering resistance coefficients, controlling tracked vehicles poses significant challenges, [...] Read more.
As a specific task for unmanned tracked vehicles, leader-following imposes high-precision requirements on the vehicle’s motion control, especially the steering control. However, due to characteristics such as the frequent changes in off-road terrain and steering resistance coefficients, controlling tracked vehicles poses significant challenges, making it difficult to achieve stable and precise leader-following. This paper decouples the leader-following control into speed and curvature control to address such issues. It utilizes model reference adaptive control to establish reference models for the speed and curvature subsystems and designs corresponding parameter adaptive control laws. This control method enables the actual vehicle speed and curvature to effectively track the response of the reference model, thereby addressing the impact of frequent changes in the steering resistance coefficient. Furthermore, this paper demonstrates significant improvements in leader-following performance through a series of simulations and experiments. Compared with the traditional PID control method, the results shows that the maximum following distance has been reduced by at least approximately 12% (ensuring the ability to keep up with the leader), the braking distance has effectively decreased by 22% (ensuring a safe distance in an emergency braking scenario and improving energy recovery), the curvature tracking accuracy has improved by at least 11% (improving steering performance), and the speed tracking accuracy has increased by at least 3.5% (improving following performance). Full article
(This article belongs to the Special Issue Intelligent Electric Vehicle Control, Testing and Evaluation)
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19 pages, 2656 KiB  
Article
A Study on the Performance Improvement of a Conical Bucket Detection Algorithm Based on YOLOv8s
by Xu Li, Gang Li and Zhe Zhang
World Electr. Veh. J. 2024, 15(6), 238; https://doi.org/10.3390/wevj15060238 - 29 May 2024
Viewed by 255
Abstract
In driverless formula car racing, cone detection faces two significant challenges: one is recognizing cones at long distances accurately, and the other is being prone to leakage under bright light conditions. These challenges directly affect the detection accuracy and response speed. In order [...] Read more.
In driverless formula car racing, cone detection faces two significant challenges: one is recognizing cones at long distances accurately, and the other is being prone to leakage under bright light conditions. These challenges directly affect the detection accuracy and response speed. In order to cope with these problems, the thesis is based on YOLOv8s to improve the cone bucket detection algorithm. Firstly, a P2 detection layer for detecting tiny objects is added on top of YOLOv8s to detect small targets with 160 × 160 pixels, which improves the detection of small conical buckets in the distant view. At the same time, to reduce the network’s complexity to achieve lightweightness, the original 20 × 20 pixel detection header is deleted. Second, the head of the original YOLOv8 is replaced with a multi-scale fusion Dynamic Head, designed to improve the head’s ability in scale, space, and task perception to enhance the detection performance of the model in complex scenes. Again, a novel loss function, MPDIoU, is introduced, which has advantages in simplifying the bounding box similarity comparison, and it can adapt to the overlapping or non-overlapping situation of the bounding box more effectively. It reduces the phenomenon of missed detection caused by overlapping conical buckets. Finally, the LAMP pruning method is used to trim the model to make the model lightweight. By adding and modifying the above modules, the improved algorithm improves the detection accuracy from 92.2% to 95.2%, the recall rate from 84.2% to 91.8%, and the average accuracy from 91.3% to 96%, while the number of parameters is reduced from 28.7 M to 26.6 M. The detection speed still meets the real-time requirement in real-vehicle testing compared to the original algorithm. In the real car test, compared with the original algorithm, the improved algorithm shows apparent advantages in reducing the missed detection of cones and barrels, which meets the demand for high accuracy of cones and barrel detection in the complex race environment and also meets the conditions for deployment on small devices with limited resources. Full article
(This article belongs to the Special Issue Electric Vehicle Autonomous Driving Based on Image Recognition)
19 pages, 771 KiB  
Article
Vehicle Trajectory-Prediction Method Based on Driver Behavior-Classification and Informer Models
by Jianyu Su, Muyang Li, Langqian Zhu, Sijia Zhang and Mingjian Liu
World Electr. Veh. J. 2024, 15(6), 237; https://doi.org/10.3390/wevj15060237 - 29 May 2024
Viewed by 306
Abstract
In order to improve the accuracy of vehicle trajectories and ensure driving safety, and considering the differences in driver behavior and the impact of these differences on vehicle trajectories, a vehicle trajectory-prediction method (DBC-Informer) based on the categorization of driver behavior is proposed: [...] Read more.
In order to improve the accuracy of vehicle trajectories and ensure driving safety, and considering the differences in driver behavior and the impact of these differences on vehicle trajectories, a vehicle trajectory-prediction method (DBC-Informer) based on the categorization of driver behavior is proposed: firstly, the characteristic driver feature data are extracted through data preprocessing; secondly, descriptive statistical data are obtained through the classification of the driver’s behavior into categories; finally, based on the Informer model, a two-layer driver category trajectory-prediction network architecture is established, which inputs the vehicle trajectories of different driving types into independent prediction sub-networks, respectively, to realize the accurate prediction of vehicle trajectories. The experimental results show that the MAE and MSE values of trajectory prediction of the DBC-Informer model in different time domains are much smaller than those of other comparative models, and the improvement of accuracy is more obvious in the long-term domain trajectory-prediction task scenario, and the increase in prediction error of the DBC-Informer model is significantly reduced after the prediction time exceeds 1 s. The on-line behavioral categorization is achieved by comparing different categorization models; it reaches 98% in classification accuracy and, according to the results of ablation experiments, the addition of the driver behavior-classification method to the prediction model improves the accuracy of prediction in longitudinal and lateral motion by 56% and 61%, respectively, which verifies the effectiveness of the driver behavior-classification method. It can be seen that the DBC-Informer model can more accurately portray the effects of different driving behaviors on vehicle trajectories and improve the accuracy of vehicle trajectory prediction, which provides important data support for vehicle warning systems. Full article
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