Xiaotao Fei, Yunwu Han, Shaw Voon Wong, Muhammad Amin Azman
The presented research on electric wheel loaders lacks a detailed analysis of drive energy-saving during the shovel preparation phase, which is characterized by a high probability of loader tire skidding. To address this issue, this study examines the energy consumption efficiency of a two-motor distributed drive wheel loader under three drive modes including front motor drive, rear motor drive, and dual-motor drive, taking into account the change in the drive force demand caused by the bucket landing. This study finds that the motor energy conversion efficiency is the greatest in single-motor drive mode when the bucket does not generate positive pressure with the ground. In dual-motor drive mode, the total torque overcome is greater, but the motor energy conversion efficiency is the greatest when the bucket generates the greatest positive pressure with the ground. This study suggests that in future designs of electric loaders, two motors can be used to distribute the drive, but the front and rear motors should be designed to participate in the drive with a certain torque distribution ratio at different speeds and resistance to avoid the phenomenon of the bucket pressing the ground too much.
{"title":"Efficiency Comparison of Electric Wheel Loader Powertrains with Dual Motor Input in Distributed Driving Modes","authors":"Xiaotao Fei, Yunwu Han, Shaw Voon Wong, Muhammad Amin Azman","doi":"10.3390/wevj14100298","DOIUrl":"https://doi.org/10.3390/wevj14100298","url":null,"abstract":"The presented research on electric wheel loaders lacks a detailed analysis of drive energy-saving during the shovel preparation phase, which is characterized by a high probability of loader tire skidding. To address this issue, this study examines the energy consumption efficiency of a two-motor distributed drive wheel loader under three drive modes including front motor drive, rear motor drive, and dual-motor drive, taking into account the change in the drive force demand caused by the bucket landing. This study finds that the motor energy conversion efficiency is the greatest in single-motor drive mode when the bucket does not generate positive pressure with the ground. In dual-motor drive mode, the total torque overcome is greater, but the motor energy conversion efficiency is the greatest when the bucket generates the greatest positive pressure with the ground. This study suggests that in future designs of electric loaders, two motors can be used to distribute the drive, but the front and rear motors should be designed to participate in the drive with a certain torque distribution ratio at different speeds and resistance to avoid the phenomenon of the bucket pressing the ground too much.","PeriodicalId":38979,"journal":{"name":"World Electric Vehicle Journal","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135618165","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Yanling Liu, Dongyin Shi, Fu Du, Xiaofeng Yang, Kerong Zhu
Anti-roll and anti-pitch are important directions in the comprehensive research of automobiles. In order to improve the anti-roll and anti-pitch performance of the vehicle, an inerter was applied to the vehicle suspension system, and a 14 DOF vehicle nonlinear dynamics model was established. The influence of the change in inertance in the eight kinds of improved ISD (Inerter-Spring-Damper) suspension structures on the RMS (root mean square) value of performance indexes of roll, vertical, and pitch motion of the vehicle was studied. Based on this, the vehicle’s ISD structure with better performance was selected, and the NSGA-Ⅱ algorithm was adopted to optimize the selected structural parameters. The simulation results showed that the four kinds of suspension hadbetter comprehensive performance, and their structureswere, respectively, excluding the supporting spring in parallel, (1) an inerter in series with a spring and a damper in parallel, (2) a damper in series with a spring and an inerter in parallel, (3) an inerter and a damper in series, and (4) the damper in parallel with a spring and an inerter in series. The ISD suspension structure had better comprehensive performance under step steering braking, which was obviously better than the passive suspension, and effectively improved the vehicle ride comfort, anti-roll and anti-pitch performance. Under the hook steering braking, the lateral load transfer rate was used to evaluate the vehicle’s anti-rollover ability. The results showed that the ride comfort and anti-rollover ability of ISD suspension were better than those of passive suspension. Under the condition of taking into account the anti-pitching ability, the suspension consists of a supporting spring in parallel with an inerter, and a damper in series was better.
防侧倾和防俯仰是汽车综合研究的重要方向。为了提高车辆的抗侧倾和抗俯仰性能，在车辆悬架系统中加入了一个干涉器，建立了14自由度车辆非线性动力学模型。研究了8种改进型ISD (inter - spring -阻尼器)悬架结构的惯性变化对车辆横倾、垂直和俯仰运动性能指标均方根值的影响。在此基础上，选取性能较好的车辆ISD结构，并采用NSGA-Ⅱ算法对选取的结构参数进行优化。仿真结果表明，四种悬架综合性能较好，其结构分别为:不支持弹簧并联、(1)减振器与弹簧并联、(2)减振器与弹簧并联、减振器与减振器并联、(3)减振器与减振器串联、(4)减振器与弹簧并联、减振器串联。ISD悬架结构在台阶转向制动下具有较好的综合性能，明显优于被动悬架，有效提高了车辆的平顺性、抗侧倾和抗俯仰性能。在钩式转向制动下，采用横向载荷传递率评价车辆的抗侧翻能力。结果表明，ISD悬架的平顺性和抗侧翻能力均优于被动悬架。在考虑抗俯仰能力的情况下，悬架采用支撑弹簧与惯性器并联的方式，减振器串联的方式较好。
{"title":"Topological Optimization of Vehicle ISD Suspension under Steering Braking Condition","authors":"Yanling Liu, Dongyin Shi, Fu Du, Xiaofeng Yang, Kerong Zhu","doi":"10.3390/wevj14100297","DOIUrl":"https://doi.org/10.3390/wevj14100297","url":null,"abstract":"Anti-roll and anti-pitch are important directions in the comprehensive research of automobiles. In order to improve the anti-roll and anti-pitch performance of the vehicle, an inerter was applied to the vehicle suspension system, and a 14 DOF vehicle nonlinear dynamics model was established. The influence of the change in inertance in the eight kinds of improved ISD (Inerter-Spring-Damper) suspension structures on the RMS (root mean square) value of performance indexes of roll, vertical, and pitch motion of the vehicle was studied. Based on this, the vehicle’s ISD structure with better performance was selected, and the NSGA-Ⅱ algorithm was adopted to optimize the selected structural parameters. The simulation results showed that the four kinds of suspension hadbetter comprehensive performance, and their structureswere, respectively, excluding the supporting spring in parallel, (1) an inerter in series with a spring and a damper in parallel, (2) a damper in series with a spring and an inerter in parallel, (3) an inerter and a damper in series, and (4) the damper in parallel with a spring and an inerter in series. The ISD suspension structure had better comprehensive performance under step steering braking, which was obviously better than the passive suspension, and effectively improved the vehicle ride comfort, anti-roll and anti-pitch performance. Under the hook steering braking, the lateral load transfer rate was used to evaluate the vehicle’s anti-rollover ability. The results showed that the ride comfort and anti-rollover ability of ISD suspension were better than those of passive suspension. Under the condition of taking into account the anti-pitching ability, the suspension consists of a supporting spring in parallel with an inerter, and a damper in series was better.","PeriodicalId":38979,"journal":{"name":"World Electric Vehicle Journal","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135888737","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Stephan Lacock, Armand André du Plessis, Marthinus Johannes Booysen
The nascent electrification of transport has heralded a new chapter in the driving force of mobility. Developing regions such as sub-Saharan Africa already lag in this transformative transport transition. A potential transitional step towards full-scale electric mobility is the retrofitting of the existing fleet of internal combustion-based vehicles. This paper proposes a novel approach to the design of a retrofit electric drivetrain for an internal combustion engine vehicle. Specifically, a minibus taxi, which dominates the region’s informal paratransit industry, is electrified. This retrofit is the first formal research presented with a focus on sub-Saharan Africa and its unique challenges. A generic methodology is presented to systematically specify and select drivetrain components and assess the suitability and characteristics of those components. Unique about the presented methodology is the application of driving-cycle data of internal combustion engine vehicles, which provides quantitative insights into the performance and characteristics of the selected components for a retrofit. Finally, a real-world use case is presented to provide a tangible example and to validate the feasibility of the presented approach.
{"title":"Using Driving-Cycle Data to Retrofit and Electrify Sub-Saharan Africa’s Existing Minibus Taxis for a Circular Economy","authors":"Stephan Lacock, Armand André du Plessis, Marthinus Johannes Booysen","doi":"10.3390/wevj14100296","DOIUrl":"https://doi.org/10.3390/wevj14100296","url":null,"abstract":"The nascent electrification of transport has heralded a new chapter in the driving force of mobility. Developing regions such as sub-Saharan Africa already lag in this transformative transport transition. A potential transitional step towards full-scale electric mobility is the retrofitting of the existing fleet of internal combustion-based vehicles. This paper proposes a novel approach to the design of a retrofit electric drivetrain for an internal combustion engine vehicle. Specifically, a minibus taxi, which dominates the region’s informal paratransit industry, is electrified. This retrofit is the first formal research presented with a focus on sub-Saharan Africa and its unique challenges. A generic methodology is presented to systematically specify and select drivetrain components and assess the suitability and characteristics of those components. Unique about the presented methodology is the application of driving-cycle data of internal combustion engine vehicles, which provides quantitative insights into the performance and characteristics of the selected components for a retrofit. Finally, a real-world use case is presented to provide a tangible example and to validate the feasibility of the presented approach.","PeriodicalId":38979,"journal":{"name":"World Electric Vehicle Journal","volume":"50 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136114523","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Jiantong Qiao, Shangru Yang, Jiaming Zhao, Haoyuan Li, Yuezhen Fan
The Dual Credit Policy is an important policy to promote the development of new energy vehicles unique to China. There is a lack of research that intuitively reflects the impact of the Dual Credit Policy on industrial development through an industry-based factual comparison of this policy. Based on the Taylor expansion and Cross-Entropy description, this article obtains the development regression function by the quantitative analysis of five indicators—the number of new energy vehicle-related patents, sales volume, production volume, the number of newly registered enterprises, infrastructure construction (the number of charging piles) before and after the implementation of the policy, and describes them quantitatively using the Taylor expansion to obtain the CPTI index. The CPCEI index is obtained by calculating the Cross-Entropy of the distribution of each indicator before and after policy implementation. The above two indices were compared for the growth trend and growth quantity, respectively. Finally, the following conclusions were obtained: 1. the Dual Credit Policy is more significantly promoted at the market level than the impact on the technical level; 2. although there is also incentive in infrastructure construction, it cannot fully react to the market demand; 3. the number of start-up’s operating in the new energy field increases, but the overall growth trend gradually slows down and fails to significantly change the existing structure of the market. This study suggests that the government should launch a special incentive policy for charging piles, and new energy manufacturers should expand their production capacity to meet the market demand.
{"title":"A Quantitative Study on the Impact of China’s Dual Credit Policy on the Development of New Energy Industry Based on Taylor Expansion Description and Cross-Entropy Theory","authors":"Jiantong Qiao, Shangru Yang, Jiaming Zhao, Haoyuan Li, Yuezhen Fan","doi":"10.3390/wevj14100295","DOIUrl":"https://doi.org/10.3390/wevj14100295","url":null,"abstract":"The Dual Credit Policy is an important policy to promote the development of new energy vehicles unique to China. There is a lack of research that intuitively reflects the impact of the Dual Credit Policy on industrial development through an industry-based factual comparison of this policy. Based on the Taylor expansion and Cross-Entropy description, this article obtains the development regression function by the quantitative analysis of five indicators—the number of new energy vehicle-related patents, sales volume, production volume, the number of newly registered enterprises, infrastructure construction (the number of charging piles) before and after the implementation of the policy, and describes them quantitatively using the Taylor expansion to obtain the CPTI index. The CPCEI index is obtained by calculating the Cross-Entropy of the distribution of each indicator before and after policy implementation. The above two indices were compared for the growth trend and growth quantity, respectively. Finally, the following conclusions were obtained: 1. the Dual Credit Policy is more significantly promoted at the market level than the impact on the technical level; 2. although there is also incentive in infrastructure construction, it cannot fully react to the market demand; 3. the number of start-up’s operating in the new energy field increases, but the overall growth trend gradually slows down and fails to significantly change the existing structure of the market. This study suggests that the government should launch a special incentive policy for charging piles, and new energy manufacturers should expand their production capacity to meet the market demand.","PeriodicalId":38979,"journal":{"name":"World Electric Vehicle Journal","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136114031","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Santiago J. Cachumba-Suquillo, Mariel Alfaro-Ponce, Sergio G. Torres-Cedillo, Jacinto Cortés-Pérez, Moises Jimenez-Martinez
Recently, there has been renewed interest in lightweight structures; however, a small structure change can strongly affect vehicle dynamic behavior. Therefore, this study provides new insights into non-parametric modeling based on artificial neural networks (ANNs). This work is then motivated by the requirement for a reliable substitute for virtual instrumentation in electric car development to enable the prediction of the current value of the vehicle slip from a given time history of the vehicle (input) and previous values of synthetic data (feedback). The training data are generated from a multi-body simulation using MSC Adams Car; the simulation involves a double lane-change maneuver. This test is commonly used to evaluate vehicle stability. Based on dynamic considerations, this study implements the nonlinear autoregressive exogenous (NARX) identification scheme used in time-series modeling. This work presents an ANN that is able to predict the side slip angle from simulated training data generated employing MSC Adams Car. This work is a specific solution to overtake maneuvers, avoiding the loss of vehicle control and increasing driving safety.
{"title":"Vehicle Dynamics in Electric Cars Development Using MSC Adams and Artificial Neural Network","authors":"Santiago J. Cachumba-Suquillo, Mariel Alfaro-Ponce, Sergio G. Torres-Cedillo, Jacinto Cortés-Pérez, Moises Jimenez-Martinez","doi":"10.3390/wevj14100293","DOIUrl":"https://doi.org/10.3390/wevj14100293","url":null,"abstract":"Recently, there has been renewed interest in lightweight structures; however, a small structure change can strongly affect vehicle dynamic behavior. Therefore, this study provides new insights into non-parametric modeling based on artificial neural networks (ANNs). This work is then motivated by the requirement for a reliable substitute for virtual instrumentation in electric car development to enable the prediction of the current value of the vehicle slip from a given time history of the vehicle (input) and previous values of synthetic data (feedback). The training data are generated from a multi-body simulation using MSC Adams Car; the simulation involves a double lane-change maneuver. This test is commonly used to evaluate vehicle stability. Based on dynamic considerations, this study implements the nonlinear autoregressive exogenous (NARX) identification scheme used in time-series modeling. This work presents an ANN that is able to predict the side slip angle from simulated training data generated employing MSC Adams Car. This work is a specific solution to overtake maneuvers, avoiding the loss of vehicle control and increasing driving safety.","PeriodicalId":38979,"journal":{"name":"World Electric Vehicle Journal","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136185059","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Given the influence of the randomness of driving conditions on the energy management strategy of vehicles, deep reinforcement learning considering driving conditions prediction was proposed. A working condition prediction model based on the BP neural network was established, and the correction coefficient of vehicle demand torque was determined according to the working condition prediction results. An energy management strategy and deep reinforcement learning were integrated to build an energy management strategy with deep reinforcement learning based on driving condition prediction. Simulation experiments were conducted according to the actual collected working condition data. The experimental results show that the energy management strategy, i.e., deep reinforcement learning considering working condition prediction, has faster convergence speed and more vital self-learning ability, and the equivalent fuel consumption per 100 km under different driving conditions is 6.411 L/100 km, 6.327 L/100 km, and 6.388 L/100 km, respectively. Compared with the unimproved strategy, the fuel economy can be improved by 3.18%, 3.08%, and 2.83%. The research shows that the energy management strategy, the deep reinforcement learning based on driving condition prediction, is effective and adaptive.
{"title":"Research on Energy Management Strategy of a Hybrid Commercial Vehicle Based on Deep Reinforcement Learning","authors":"Jianguo Xi, Jingwei Ma, Tianyou Wang, Jianping Gao","doi":"10.3390/wevj14100294","DOIUrl":"https://doi.org/10.3390/wevj14100294","url":null,"abstract":"Given the influence of the randomness of driving conditions on the energy management strategy of vehicles, deep reinforcement learning considering driving conditions prediction was proposed. A working condition prediction model based on the BP neural network was established, and the correction coefficient of vehicle demand torque was determined according to the working condition prediction results. An energy management strategy and deep reinforcement learning were integrated to build an energy management strategy with deep reinforcement learning based on driving condition prediction. Simulation experiments were conducted according to the actual collected working condition data. The experimental results show that the energy management strategy, i.e., deep reinforcement learning considering working condition prediction, has faster convergence speed and more vital self-learning ability, and the equivalent fuel consumption per 100 km under different driving conditions is 6.411 L/100 km, 6.327 L/100 km, and 6.388 L/100 km, respectively. Compared with the unimproved strategy, the fuel economy can be improved by 3.18%, 3.08%, and 2.83%. The research shows that the energy management strategy, the deep reinforcement learning based on driving condition prediction, is effective and adaptive.","PeriodicalId":38979,"journal":{"name":"World Electric Vehicle Journal","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136185071","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Xiaojing Zhou, Yunjia Feng, Xu Li, Zijian Zhu, Yanzhong Hu
For autonomous vehicles driving in off-road environments, it is crucial to have a sensitive environmental perception ability. However, semantic segmentation in complex scenes remains a challenging task. Most current methods for off-road environments often have the problems of single scene and low accuracy. Therefore, this paper proposes a semantic segmentation network based on LiDAR called Multi-scale Augmentation Point-Cylinder Network (MAPC-Net). The network uses a multi-layer receptive field fusion module to extract features from objects of different scales in off-road environments. Gated feature fusion is used to fuse PointTensor and Cylinder for encoding and decoding. In addition, we use CARLA to build off-road environments for obtaining datasets, and employ linear interpolation to enhance the training data to solve the problem of sample imbalance. Finally, we design experiments to verify the excellent semantic segmentation ability of MAPC-Net in an off-road environment. We also demonstrate the effectiveness of the multi-layer receptive field fusion module and data augmentation.
{"title":"Off-Road Environment Semantic Segmentation for Autonomous Vehicles Based on Multi-Scale Feature Fusion","authors":"Xiaojing Zhou, Yunjia Feng, Xu Li, Zijian Zhu, Yanzhong Hu","doi":"10.3390/wevj14100291","DOIUrl":"https://doi.org/10.3390/wevj14100291","url":null,"abstract":"For autonomous vehicles driving in off-road environments, it is crucial to have a sensitive environmental perception ability. However, semantic segmentation in complex scenes remains a challenging task. Most current methods for off-road environments often have the problems of single scene and low accuracy. Therefore, this paper proposes a semantic segmentation network based on LiDAR called Multi-scale Augmentation Point-Cylinder Network (MAPC-Net). The network uses a multi-layer receptive field fusion module to extract features from objects of different scales in off-road environments. Gated feature fusion is used to fuse PointTensor and Cylinder for encoding and decoding. In addition, we use CARLA to build off-road environments for obtaining datasets, and employ linear interpolation to enhance the training data to solve the problem of sample imbalance. Finally, we design experiments to verify the excellent semantic segmentation ability of MAPC-Net in an off-road environment. We also demonstrate the effectiveness of the multi-layer receptive field fusion module and data augmentation.","PeriodicalId":38979,"journal":{"name":"World Electric Vehicle Journal","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135858381","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
A collaborative control strategy for distributed drive electric vehicles (DDEVs) focusing on differential drive assisted steering (DDAS) and active front steering (AFS) is proposed to address the issues of sudden torque changes, reduced steering characteristics, and weak collaborative control capabilities caused by the coupling of the AFS and DDAS systems in DDEVs. This paper establishes a coupled dynamic model of the AFS and DDAS systems and, on this basis, designs AFS controllers for yaw velocity feedback control and DDAS controllers for steering wheel torque control, respectively. Additionally, it analyzes the interference factors of the two control systems and develops a collaborative control strategy for DDAS and AFS; this control strategy establishes a corner motor correction module, steering wheel torque correction module, and assistance correction module. Co-simulation is carried out on Matlab/Simulink and the Carsim platform to verify the correctness of the model under typical working conditions; to reduce the sudden change in the steering wheel torque caused by AFS additional angle interventions; to improve the poor steering characteristics caused by DDAS, introducing additional yaw torque; to greatly enhance the collaborative control effect; and to meet the requirements for vehicle handling stability, portability, and safety.
{"title":"Research on Collaborative Control of Differential Drive Assisted Steering and Active Front Steering for Distributed Drive Electric Vehicles","authors":"Zhigang Zhou, Xinqing Ding, Zhichong Shi","doi":"10.3390/wevj14100292","DOIUrl":"https://doi.org/10.3390/wevj14100292","url":null,"abstract":"A collaborative control strategy for distributed drive electric vehicles (DDEVs) focusing on differential drive assisted steering (DDAS) and active front steering (AFS) is proposed to address the issues of sudden torque changes, reduced steering characteristics, and weak collaborative control capabilities caused by the coupling of the AFS and DDAS systems in DDEVs. This paper establishes a coupled dynamic model of the AFS and DDAS systems and, on this basis, designs AFS controllers for yaw velocity feedback control and DDAS controllers for steering wheel torque control, respectively. Additionally, it analyzes the interference factors of the two control systems and develops a collaborative control strategy for DDAS and AFS; this control strategy establishes a corner motor correction module, steering wheel torque correction module, and assistance correction module. Co-simulation is carried out on Matlab/Simulink and the Carsim platform to verify the correctness of the model under typical working conditions; to reduce the sudden change in the steering wheel torque caused by AFS additional angle interventions; to improve the poor steering characteristics caused by DDAS, introducing additional yaw torque; to greatly enhance the collaborative control effect; and to meet the requirements for vehicle handling stability, portability, and safety.","PeriodicalId":38979,"journal":{"name":"World Electric Vehicle Journal","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135918509","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Yuan Fang, Mingzhang Chen, Weida Liang, Zijian Zhou, Xunchen Liu
Research on manufacturing components for electric vehicles plays a vital role in their development. Furthermore, significant advancements in additive manufacturing processes have revolutionized the production of various parts. By establishing a system that enables the recovery, processing, and reuse of metal powders essential for additive manufacturing, we can achieve sustainable production of electric vehicles. This approach holds immense importance in terms of reducing manufacturing costs, expanding the market, and safeguarding the environment. In this study, we developed an additive manufacturing system for recycled metal powders, encompassing powder variety, properties, processing, manufacturing, component properties, and applications. This system was used to create a knowledge graph providing a convenient resource for researchers to understand the entire procedure from recycling to application. To improve the graph’s accuracy, we employed ChatGPT and BERT training. We also demonstrated the knowledge graph’s utility by processing recycled 316 L stainless steel powders and assessing their quality through image processing. This experiment serves as a practical example of recycling and analyzing powders using the established knowledge graph.
{"title":"Knowledge Graph Learning for Vehicle Additive Manufacturing of Recycled Metal Powder","authors":"Yuan Fang, Mingzhang Chen, Weida Liang, Zijian Zhou, Xunchen Liu","doi":"10.3390/wevj14100289","DOIUrl":"https://doi.org/10.3390/wevj14100289","url":null,"abstract":"Research on manufacturing components for electric vehicles plays a vital role in their development. Furthermore, significant advancements in additive manufacturing processes have revolutionized the production of various parts. By establishing a system that enables the recovery, processing, and reuse of metal powders essential for additive manufacturing, we can achieve sustainable production of electric vehicles. This approach holds immense importance in terms of reducing manufacturing costs, expanding the market, and safeguarding the environment. In this study, we developed an additive manufacturing system for recycled metal powders, encompassing powder variety, properties, processing, manufacturing, component properties, and applications. This system was used to create a knowledge graph providing a convenient resource for researchers to understand the entire procedure from recycling to application. To improve the graph’s accuracy, we employed ChatGPT and BERT training. We also demonstrated the knowledge graph’s utility by processing recycled 316 L stainless steel powders and assessing their quality through image processing. This experiment serves as a practical example of recycling and analyzing powders using the established knowledge graph.","PeriodicalId":38979,"journal":{"name":"World Electric Vehicle Journal","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136013015","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Sustainability emphasises the crucial need to incorporate environmentally conscious practises across the entire supply chain management process in the modern age. A great emphasis is placed on minimising environmental consequences, eliminating waste, conserving energy, and sourcing materials responsibly in the production, distribution, and disposal of electric vehicles. Electric vehicle manufacturers must prioritise sustainability to ensure that their products contribute significantly to a brighter future while also meeting the ethical and environmental demands of consumers as well as regulatory bodies. Green supply chain management (GSCM) incorporates environmentally friendly practises to reduce environmental effects. This study incorporates fuzzy TOPSIS for analysing and rating GSCM practises, assisting decision-makers in prioritising sustainability in the supply chains of electric vehicle manufacturers. We develop a multi-criteria decision-making framework to evaluate GSCM criteria while accounting for inherent uncertainty. Fuzzy TOPSIS handles linguistic problems as well as ambiguity while providing a precise GSCM representation. Real-world case studies from various sectors demonstrate the applicability and benefits of our approach to finding improvement areas and expediting GSCM assessments. This research presents a systematic, quantitative way for evaluating GSCM practises, allowing supply chain alignment with sustainability goals. This promotes environmentally sustainable practises and increases the sustainability of supply chains for electric car manufacturing.
{"title":"Cultivating Sustainable Supply Chain Practises in Electric Vehicle Manufacturing: A MCDM Approach to Assessing GSCM Performance","authors":"Torky Althaqafi","doi":"10.3390/wevj14100290","DOIUrl":"https://doi.org/10.3390/wevj14100290","url":null,"abstract":"Sustainability emphasises the crucial need to incorporate environmentally conscious practises across the entire supply chain management process in the modern age. A great emphasis is placed on minimising environmental consequences, eliminating waste, conserving energy, and sourcing materials responsibly in the production, distribution, and disposal of electric vehicles. Electric vehicle manufacturers must prioritise sustainability to ensure that their products contribute significantly to a brighter future while also meeting the ethical and environmental demands of consumers as well as regulatory bodies. Green supply chain management (GSCM) incorporates environmentally friendly practises to reduce environmental effects. This study incorporates fuzzy TOPSIS for analysing and rating GSCM practises, assisting decision-makers in prioritising sustainability in the supply chains of electric vehicle manufacturers. We develop a multi-criteria decision-making framework to evaluate GSCM criteria while accounting for inherent uncertainty. Fuzzy TOPSIS handles linguistic problems as well as ambiguity while providing a precise GSCM representation. Real-world case studies from various sectors demonstrate the applicability and benefits of our approach to finding improvement areas and expediting GSCM assessments. This research presents a systematic, quantitative way for evaluating GSCM practises, allowing supply chain alignment with sustainability goals. This promotes environmentally sustainable practises and increases the sustainability of supply chains for electric car manufacturing.","PeriodicalId":38979,"journal":{"name":"World Electric Vehicle Journal","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136013483","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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