Haiyun An, Qian Zhou, Yongyong Jia, Zhe Chen, Bingcheng Cen, Tong Zhu, Huiyun Li, Yifei Wang
With the extensive promotion of new energy vehicles, the number of electric vehicles (EVs) in China has increased rapidly. Electric vehicles are densely parked in garages, which means parking garages contain a large amount of idle energy storage resources. How to make this idle energy storage in garages participate in power system dispatch and evaluate the network loss and system carbon emissions considering electric vehicle energy storage has become an important research topic. The uncertainty around parking habits for electric vehicles causes it to be difficult to predict compared with the traditional energy storage system. Therefore, it is necessary to study its influence on the synergistic effect of loss reduction and carbon reduction as energy storage access. The benefits of new energy power generation output growth, energy waste reduction, and carbon emission reduction brought by loss reduction measures can be well reflected in the loss reduction index system of a power system in a low-carbon scenario. In this paper, a large amount of parking information in a certain area is collected, and the approximate parking habits of all vehicles in the simulated garage are obtained by the Monte Carlo method. Then, the load aggregation model is established, which is incorporated into the power system as an energy storage model. The synergy of loss reduction and carbon reduction is considered in this paper and comprehensively optimizes the strategy of integrating electric vehicles into the power system from the perspectives of electricity and carbon. In the scenarios of carbon flow calculation and network loss calculation, the YALMIP and CPLEX of MATLAB are applied, with various constraints input for simulation, so that the benefit evaluation method of carbon reduction and loss reduction under a coordinated transportation–electricity network is obtained.
{"title":"Benefit Evaluation of Carbon Reduction and Loss Reduction under a Coordinated Transportation–Electricity Network","authors":"Haiyun An, Qian Zhou, Yongyong Jia, Zhe Chen, Bingcheng Cen, Tong Zhu, Huiyun Li, Yifei Wang","doi":"10.3390/wevj15010024","DOIUrl":"https://doi.org/10.3390/wevj15010024","url":null,"abstract":"With the extensive promotion of new energy vehicles, the number of electric vehicles (EVs) in China has increased rapidly. Electric vehicles are densely parked in garages, which means parking garages contain a large amount of idle energy storage resources. How to make this idle energy storage in garages participate in power system dispatch and evaluate the network loss and system carbon emissions considering electric vehicle energy storage has become an important research topic. The uncertainty around parking habits for electric vehicles causes it to be difficult to predict compared with the traditional energy storage system. Therefore, it is necessary to study its influence on the synergistic effect of loss reduction and carbon reduction as energy storage access. The benefits of new energy power generation output growth, energy waste reduction, and carbon emission reduction brought by loss reduction measures can be well reflected in the loss reduction index system of a power system in a low-carbon scenario. In this paper, a large amount of parking information in a certain area is collected, and the approximate parking habits of all vehicles in the simulated garage are obtained by the Monte Carlo method. Then, the load aggregation model is established, which is incorporated into the power system as an energy storage model. The synergy of loss reduction and carbon reduction is considered in this paper and comprehensively optimizes the strategy of integrating electric vehicles into the power system from the perspectives of electricity and carbon. In the scenarios of carbon flow calculation and network loss calculation, the YALMIP and CPLEX of MATLAB are applied, with various constraints input for simulation, so that the benefit evaluation method of carbon reduction and loss reduction under a coordinated transportation–electricity network is obtained.","PeriodicalId":38979,"journal":{"name":"World Electric Vehicle Journal","volume":"8 29","pages":""},"PeriodicalIF":2.3,"publicationDate":"2024-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139440063","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}
The accurate identification of permanent magnet synchronous motor (PMSM) parameters is the foundation for high-performance driving in predictive control systems. The traditional PMSM multi-parameter identification method suffers from insufficient rank of the identification equation and is prone to getting stuck in local optimal solutions. This article combines the bacterial foraging optimization algorithm (BFOA) to establish a built-in PMSM predictive control parameter compensation model. Firstly, we analyzed the reasons why the distortion of PMSM motor parameters affects the actual speed and calculated the deviation of d-axis and q-axis currents caused by the distortion. Secondly, parameter compensation was applied to the prediction model, and BFOA was combined to optimize the compensation parameters. This algorithm does not use the traditional voltage equation as the fitness function but instead uses a brand-new set of four equations for parameter iteration optimization. The optimized compensation parameters can reduce current deviation and improve the robustness of the PMSM predictive control system. The proposed model can cover four kinds of motor distortion parameters, including stator resistance, D-axis inductance, Q-axis inductance, and permanent magnet flux linkage. Finally, the traditional PMSM predictive control model is compared with the predictive control model combined with BFOA. The simulation results show that the dynamic and static performance of the compensated system is improved when single or multiple parameters are distorted.
{"title":"Parameter Compensation for the Predictive Control System of a Permanent Magnet Synchronous Motor Based on Bacterial Foraging Optimization Algorithm","authors":"Jiali Yang, Yanxia Shen, Yongqiang Tan","doi":"10.3390/wevj15010023","DOIUrl":"https://doi.org/10.3390/wevj15010023","url":null,"abstract":"The accurate identification of permanent magnet synchronous motor (PMSM) parameters is the foundation for high-performance driving in predictive control systems. The traditional PMSM multi-parameter identification method suffers from insufficient rank of the identification equation and is prone to getting stuck in local optimal solutions. This article combines the bacterial foraging optimization algorithm (BFOA) to establish a built-in PMSM predictive control parameter compensation model. Firstly, we analyzed the reasons why the distortion of PMSM motor parameters affects the actual speed and calculated the deviation of d-axis and q-axis currents caused by the distortion. Secondly, parameter compensation was applied to the prediction model, and BFOA was combined to optimize the compensation parameters. This algorithm does not use the traditional voltage equation as the fitness function but instead uses a brand-new set of four equations for parameter iteration optimization. The optimized compensation parameters can reduce current deviation and improve the robustness of the PMSM predictive control system. The proposed model can cover four kinds of motor distortion parameters, including stator resistance, D-axis inductance, Q-axis inductance, and permanent magnet flux linkage. Finally, the traditional PMSM predictive control model is compared with the predictive control model combined with BFOA. The simulation results show that the dynamic and static performance of the compensated system is improved when single or multiple parameters are distorted.","PeriodicalId":38979,"journal":{"name":"World Electric Vehicle Journal","volume":"56 46","pages":""},"PeriodicalIF":2.3,"publicationDate":"2024-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139442049","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}
Enrico Pizzutilo, Thomas Acher, Benjamin Reuter, Christian Will, Simon Schäfer
Subcooled liquid hydrogen (sLH2) is an onboard storage, as well as a hydrogen refueling technology that is currently being developed by Daimler Truck and Linde to boost the mileage of heavy-duty trucks, while also improving performance and reducing the complexity of hydrogen refueling stations. In this article, the key technical aspects, advantages, challenges and future developments of sLH2 at vehicle and infrastructure levels will be explored and highlighted.
{"title":"Subcooled Liquid Hydrogen Technology for Heavy-Duty Trucks","authors":"Enrico Pizzutilo, Thomas Acher, Benjamin Reuter, Christian Will, Simon Schäfer","doi":"10.3390/wevj15010022","DOIUrl":"https://doi.org/10.3390/wevj15010022","url":null,"abstract":"Subcooled liquid hydrogen (sLH2) is an onboard storage, as well as a hydrogen refueling technology that is currently being developed by Daimler Truck and Linde to boost the mileage of heavy-duty trucks, while also improving performance and reducing the complexity of hydrogen refueling stations. In this article, the key technical aspects, advantages, challenges and future developments of sLH2 at vehicle and infrastructure levels will be explored and highlighted.","PeriodicalId":38979,"journal":{"name":"World Electric Vehicle Journal","volume":"27 1","pages":""},"PeriodicalIF":2.3,"publicationDate":"2024-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139445045","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}
In order to more accurately analyze the problem of time delay simulation and calculation in the time-sensitive network (TSN) of vehicular Ethernet, a TSN reservation class data delay analysis model improved based on the State–Action–Reward–State–Action (SARSA) reinforcement learning algorithm is proposed. Firstly, the TSN data queue forwarding delay model and reservation class data delay analysis intelligent body model are established, then the TSN traffic scheduling mechanism is improved by the SARSA reinforcement learning algorithm, and the improved TSN network reservation class data analysis model is established for the uncertainty of traffic scheduling in the network; finally, the fitting performance of the proposed method is verified by simulation and experimental validation. The results show that the deviation between the two is less than 5% under different BE loads, i.e., the established reservation class data delay analysis model is able to correctly fit the scheduling mechanism of the vehicle-mounted TSN network, which proves the reasonableness of the model simulation.
{"title":"Time-Sensitive Network Simulation for In-Vehicle Ethernet Using SARSA Algorithm","authors":"Chen Huang, Yiqi Wang, Yuxin Zhang","doi":"10.3390/wevj15010021","DOIUrl":"https://doi.org/10.3390/wevj15010021","url":null,"abstract":"In order to more accurately analyze the problem of time delay simulation and calculation in the time-sensitive network (TSN) of vehicular Ethernet, a TSN reservation class data delay analysis model improved based on the State–Action–Reward–State–Action (SARSA) reinforcement learning algorithm is proposed. Firstly, the TSN data queue forwarding delay model and reservation class data delay analysis intelligent body model are established, then the TSN traffic scheduling mechanism is improved by the SARSA reinforcement learning algorithm, and the improved TSN network reservation class data analysis model is established for the uncertainty of traffic scheduling in the network; finally, the fitting performance of the proposed method is verified by simulation and experimental validation. The results show that the deviation between the two is less than 5% under different BE loads, i.e., the established reservation class data delay analysis model is able to correctly fit the scheduling mechanism of the vehicle-mounted TSN network, which proves the reasonableness of the model simulation.","PeriodicalId":38979,"journal":{"name":"World Electric Vehicle Journal","volume":"16 24","pages":""},"PeriodicalIF":2.3,"publicationDate":"2024-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139445444","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}
The pursuit of autonomous driving relies on developing perception systems capable of making accurate, robust, and rapid decisions to interpret the driving environment effectively. Object detection is crucial for understanding the environment at these systems’ core. While 2D object detection and classification have advanced significantly with the advent of deep learning (DL) in computer vision (CV) applications, they fall short in providing essential depth information, a key element in comprehending driving environments. Consequently, 3D object detection becomes a cornerstone for autonomous driving and robotics, offering precise estimations of object locations and enhancing environmental comprehension. The CV community’s growing interest in 3D object detection is fueled by the evolution of DL models, including Convolutional Neural Networks (CNNs) and Transformer networks. Despite these advancements, challenges such as varying object scales, limited 3D sensor data, and occlusions persist in 3D object detection. To address these challenges, researchers are exploring multimodal techniques that combine information from multiple sensors, such as cameras, radar, and LiDAR, to enhance the performance of perception systems. This survey provides an exhaustive review of multimodal fusion-based 3D object detection methods, focusing on CNN and Transformer-based models. It underscores the necessity of equipping fully autonomous vehicles with diverse sensors to ensure robust and reliable operation. The survey explores the advantages and drawbacks of cameras, LiDAR, and radar sensors. Additionally, it summarizes autonomy datasets and examines the latest advancements in multimodal fusion-based methods. The survey concludes by highlighting the ongoing challenges, open issues, and potential directions for future research.
{"title":"Emerging Trends in Autonomous Vehicle Perception: Multimodal Fusion for 3D Object Detection","authors":"S. Y. Alaba, Ali C. Gurbuz, John E. Ball","doi":"10.3390/wevj15010020","DOIUrl":"https://doi.org/10.3390/wevj15010020","url":null,"abstract":"The pursuit of autonomous driving relies on developing perception systems capable of making accurate, robust, and rapid decisions to interpret the driving environment effectively. Object detection is crucial for understanding the environment at these systems’ core. While 2D object detection and classification have advanced significantly with the advent of deep learning (DL) in computer vision (CV) applications, they fall short in providing essential depth information, a key element in comprehending driving environments. Consequently, 3D object detection becomes a cornerstone for autonomous driving and robotics, offering precise estimations of object locations and enhancing environmental comprehension. The CV community’s growing interest in 3D object detection is fueled by the evolution of DL models, including Convolutional Neural Networks (CNNs) and Transformer networks. Despite these advancements, challenges such as varying object scales, limited 3D sensor data, and occlusions persist in 3D object detection. To address these challenges, researchers are exploring multimodal techniques that combine information from multiple sensors, such as cameras, radar, and LiDAR, to enhance the performance of perception systems. This survey provides an exhaustive review of multimodal fusion-based 3D object detection methods, focusing on CNN and Transformer-based models. It underscores the necessity of equipping fully autonomous vehicles with diverse sensors to ensure robust and reliable operation. The survey explores the advantages and drawbacks of cameras, LiDAR, and radar sensors. Additionally, it summarizes autonomy datasets and examines the latest advancements in multimodal fusion-based methods. The survey concludes by highlighting the ongoing challenges, open issues, and potential directions for future research.","PeriodicalId":38979,"journal":{"name":"World Electric Vehicle Journal","volume":"28 7","pages":""},"PeriodicalIF":2.3,"publicationDate":"2024-01-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139448322","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}
In the rapidly evolving electric vehicle industry, the reliability of electronic systems is critical to ensuring vehicle safety and performance. Printed circuit boards (PCBs), serving as a cornerstone in these systems, necessitate efficient and accurate surface defect detection. Traditional PCB surface defect detection methods, like basic image processing and manual inspection, are inefficient and error-prone, especially for complex, minute, or irregular defects. Addressing this issue, this study introduces a technology based on the YOLOv5 network structure. By integrating the Convolutional Block Attention Module (CBAM), the model’s capability in recognizing intricate and small defects is enhanced. Further, partial convolution (PConv) replaces traditional convolution for more effective spatial feature extraction and reduced redundant computation. In the network’s final stage, multi-scale defect detection is implemented. Additionally, the normalized Wasserstein distance (NWD) loss function is introduced, considering relationships between different categories, thereby effectively solving class imbalance and multi-scale defect detection issues. Training and validation on a public PCB dataset showed the model’s superior detection accuracy and reduced false detection rate compared to traditional methods. Real-time monitoring results confirm the model’s ability to accurately detect various types and sizes of PCB surface defects, satisfying the real-time detection needs of electric vehicle production lines and providing crucial technical support for electric vehicle reliability.
{"title":"Advancements in Electric Vehicle PCB Inspection: Application of Multi-Scale CBAM, Partial Convolution, and NWD Loss in YOLOv5","authors":"Hanlin Xu, Li Wang, Feng Chen","doi":"10.3390/wevj15010015","DOIUrl":"https://doi.org/10.3390/wevj15010015","url":null,"abstract":"In the rapidly evolving electric vehicle industry, the reliability of electronic systems is critical to ensuring vehicle safety and performance. Printed circuit boards (PCBs), serving as a cornerstone in these systems, necessitate efficient and accurate surface defect detection. Traditional PCB surface defect detection methods, like basic image processing and manual inspection, are inefficient and error-prone, especially for complex, minute, or irregular defects. Addressing this issue, this study introduces a technology based on the YOLOv5 network structure. By integrating the Convolutional Block Attention Module (CBAM), the model’s capability in recognizing intricate and small defects is enhanced. Further, partial convolution (PConv) replaces traditional convolution for more effective spatial feature extraction and reduced redundant computation. In the network’s final stage, multi-scale defect detection is implemented. Additionally, the normalized Wasserstein distance (NWD) loss function is introduced, considering relationships between different categories, thereby effectively solving class imbalance and multi-scale defect detection issues. Training and validation on a public PCB dataset showed the model’s superior detection accuracy and reduced false detection rate compared to traditional methods. Real-time monitoring results confirm the model’s ability to accurately detect various types and sizes of PCB surface defects, satisfying the real-time detection needs of electric vehicle production lines and providing crucial technical support for electric vehicle reliability.","PeriodicalId":38979,"journal":{"name":"World Electric Vehicle Journal","volume":"21 2","pages":""},"PeriodicalIF":2.3,"publicationDate":"2024-01-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139451606","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}
China has pledged to peak its CO2 emissions by 2030 and achieve carbon neutrality by 2060. To meet these goals, China needs to accelerate the electrification of passenger vehicles. However, the rapid development of electric vehicles may impact the supply of critical raw materials, which may hinder the low-carbon transition. Therefore, the impact of vehicle electrification on CO2 emissions and the corresponding bottlenecks in the supply of critical raw materials should be systematically considered. In this study, we developed the China Automotive Fleet CO2 Model (CAFCM) to simulate a mixed-technology passenger vehicle fleet evolution. We further assessed the impact of energy and CO2 emissions and evaluated the demand for critical battery materials. We designed three scenarios with different powertrain type penetration rates to depict the potential uncertainty. The results showed that (1) the CO2 emissions of passenger vehicles in both the operation stage and the fuel cycle can peak before 2030; (2) achieving the dual carbon goals will lead to a rapid increase in the demand for critical raw materials for batteries and lead to potential supply risks, especially for cobalt, with the cumulative demand for cobalt for new energy passenger vehicles in China being 5.7 to 7.3 times larger than China’s total cobalt reserves; and (3) the potential amount of critical material recycled from retired power batteries will rapidly increase but will not be able to substantially alleviate the demand for critical materials before 2035. China’s new energy vehicle promotion policies and key resource supply risks must be systematically coordinated under the dual carbon goals.
{"title":"Prospects of Passenger Vehicles in China to Meet Dual Carbon Goals and Bottleneck of Critical Materials from a Fleet Evolution Perspective","authors":"Rujie Yu, Longze Cong, Yaoming Li, Chunjia Ran, Dongchang Zhao, Ping Li","doi":"10.3390/wevj15010014","DOIUrl":"https://doi.org/10.3390/wevj15010014","url":null,"abstract":"China has pledged to peak its CO2 emissions by 2030 and achieve carbon neutrality by 2060. To meet these goals, China needs to accelerate the electrification of passenger vehicles. However, the rapid development of electric vehicles may impact the supply of critical raw materials, which may hinder the low-carbon transition. Therefore, the impact of vehicle electrification on CO2 emissions and the corresponding bottlenecks in the supply of critical raw materials should be systematically considered. In this study, we developed the China Automotive Fleet CO2 Model (CAFCM) to simulate a mixed-technology passenger vehicle fleet evolution. We further assessed the impact of energy and CO2 emissions and evaluated the demand for critical battery materials. We designed three scenarios with different powertrain type penetration rates to depict the potential uncertainty. The results showed that (1) the CO2 emissions of passenger vehicles in both the operation stage and the fuel cycle can peak before 2030; (2) achieving the dual carbon goals will lead to a rapid increase in the demand for critical raw materials for batteries and lead to potential supply risks, especially for cobalt, with the cumulative demand for cobalt for new energy passenger vehicles in China being 5.7 to 7.3 times larger than China’s total cobalt reserves; and (3) the potential amount of critical material recycled from retired power batteries will rapidly increase but will not be able to substantially alleviate the demand for critical materials before 2035. China’s new energy vehicle promotion policies and key resource supply risks must be systematically coordinated under the dual carbon goals.","PeriodicalId":38979,"journal":{"name":"World Electric Vehicle Journal","volume":"134 13","pages":""},"PeriodicalIF":2.3,"publicationDate":"2024-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139453246","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}
An autonomous car must know where it is with high precision in order to maneuver safely and reliably in both urban and highway environments. Thus, in this paper, a reliable and relatively precise position estimation (localization) technique for autonomous vehicles is proposed and implemented. In dealing with the obtained sensory data or given knowledge about the vehicle’s surroundings, the proposed method takes a probabilistic approach. In this approach, the involved probability densities are expressed by keeping a collection of samples selected at random from them (Monte Carlo simulation). Consequently, this Monte Carlo sampling allows the resultant position estimates to be represented with any arbitrary distribution, not only a Gaussian one. The selected technique to implement this Monte-Carlo-based localization is Bayesian filtering with particle-based density representations (i.e., particle filters). The employed particle filter receives the surrounding object ranges from a carefully tuned Unscented Kalman Filter (UKF) that is used to fuse radar and lidar sensory readings. The sensory readings are used to detect pole-like static objects in the egocar’s surroundings and compare them to the ones that exist in a supplied detailed reference map that contains pole-like landmarks that are produced offline and extracted from a 3D lidar scan. Comprehensive simulation tests were conducted to evaluate the outcome of the proposed technique in both lateral and longitudinal localization. The results show that the proposed technique outperforms the other techniques in terms of smaller lateral and longitudinal mean position errors.
{"title":"Utilizing Probabilistic Maps and Unscented-Kalman-Filtering-Based Sensor Fusion for Real-Time Monte Carlo Localization","authors":"Wael A. Farag, Julien Moussa H. Barakat","doi":"10.3390/wevj15010005","DOIUrl":"https://doi.org/10.3390/wevj15010005","url":null,"abstract":"An autonomous car must know where it is with high precision in order to maneuver safely and reliably in both urban and highway environments. Thus, in this paper, a reliable and relatively precise position estimation (localization) technique for autonomous vehicles is proposed and implemented. In dealing with the obtained sensory data or given knowledge about the vehicle’s surroundings, the proposed method takes a probabilistic approach. In this approach, the involved probability densities are expressed by keeping a collection of samples selected at random from them (Monte Carlo simulation). Consequently, this Monte Carlo sampling allows the resultant position estimates to be represented with any arbitrary distribution, not only a Gaussian one. The selected technique to implement this Monte-Carlo-based localization is Bayesian filtering with particle-based density representations (i.e., particle filters). The employed particle filter receives the surrounding object ranges from a carefully tuned Unscented Kalman Filter (UKF) that is used to fuse radar and lidar sensory readings. The sensory readings are used to detect pole-like static objects in the egocar’s surroundings and compare them to the ones that exist in a supplied detailed reference map that contains pole-like landmarks that are produced offline and extracted from a 3D lidar scan. Comprehensive simulation tests were conducted to evaluate the outcome of the proposed technique in both lateral and longitudinal localization. The results show that the proposed technique outperforms the other techniques in terms of smaller lateral and longitudinal mean position errors.","PeriodicalId":38979,"journal":{"name":"World Electric Vehicle Journal","volume":"10 12","pages":""},"PeriodicalIF":2.3,"publicationDate":"2023-12-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138951844","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}
In many high-speed electrical machines, centrifugal forces within the rotor can be first-order constraints on electromagnetic optimization. This can be particularly acute in interior permanent magnet (IPM) machines in which magnets are usually retained entirely by the rotor core with no additional mechanical containment. This study investigates the nature of the trade-off between mechanical and electromagnetic requirements within the context of an eight-pole, 100 kW IPM machine with a base speed of 4000 rpm and an extended speed range up to 12,000 rpm. A series of mechanical and electromagnetic models are used to estimate the level of shaft interference, mechanical stress in critical regions of the rotor and the impact of various features and dimensions within the machine on electromagnetic torque. A systematic exploration of the design space is undertaken for rotor diameters from 120 mm to 180 mm, with optimal designs in terms of torque per unit length established at each diameter while meeting the constraints imposed on mechanical stress. The final preferred design has a rotor of 165 mm and an axial length of 103 mm long with a fractional slot winding in a 30-slot stator. The overall machine has an active mass of 42.3 kg, which corresponds to ~2.4 kW/kg. This paper describes the optimization study in detail and draws on the results to explore the nature of the design trade-offs in such rotors and the impact of core properties.
{"title":"Combined Electromagnetic and Mechanical Design Optimization of Interior Permanent Magnet Rotors for Electric Vehicle Drivetrains","authors":"Guanhua Zhang, G. W. Jewell","doi":"10.3390/wevj15010004","DOIUrl":"https://doi.org/10.3390/wevj15010004","url":null,"abstract":"In many high-speed electrical machines, centrifugal forces within the rotor can be first-order constraints on electromagnetic optimization. This can be particularly acute in interior permanent magnet (IPM) machines in which magnets are usually retained entirely by the rotor core with no additional mechanical containment. This study investigates the nature of the trade-off between mechanical and electromagnetic requirements within the context of an eight-pole, 100 kW IPM machine with a base speed of 4000 rpm and an extended speed range up to 12,000 rpm. A series of mechanical and electromagnetic models are used to estimate the level of shaft interference, mechanical stress in critical regions of the rotor and the impact of various features and dimensions within the machine on electromagnetic torque. A systematic exploration of the design space is undertaken for rotor diameters from 120 mm to 180 mm, with optimal designs in terms of torque per unit length established at each diameter while meeting the constraints imposed on mechanical stress. The final preferred design has a rotor of 165 mm and an axial length of 103 mm long with a fractional slot winding in a 30-slot stator. The overall machine has an active mass of 42.3 kg, which corresponds to ~2.4 kW/kg. This paper describes the optimization study in detail and draws on the results to explore the nature of the design trade-offs in such rotors and the impact of core properties.","PeriodicalId":38979,"journal":{"name":"World Electric Vehicle Journal","volume":"3 1","pages":""},"PeriodicalIF":2.3,"publicationDate":"2023-12-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138952185","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}
Xuerui Hou, Meiling Su, Chenhui Liu, Ying Li, Qinglu Ma
With the great increase of electric vehicles (EVs) in the past decade, EV-involved traffic accidents have also been increasing quickly in many countries, bringing many new traffic safety challenges. Norway has the largest EV penetration rate in the world. Using the EV accident data from Norway in 2020 and 2021, this study aims to investigate the features of EV safety comprehensively. Firstly, a descriptive analysis is conducted. It has been found that rear-end collisions are the major collision type of EVs, and EVs are very likely to collide with pedestrians/cyclists. In addition, in terms of roadway type, EV accidents mainly occur on medium- and low-speed roads; in terms of environment, they mainly occur in good visibility conditions and dry road surface conditions. Then, a regression analysis is conducted to identify the key factors affecting the accident size, which is the number of traffic units involved in an accident and taken as the accident severity surrogate here. Since EV accidents are divided into four categories in order of accident size, the ordered logit model is adopted. It divides a multi-categorical dependent variable into multiple binary data points in order and calculates the probability of the dependent variable falling into each category with the logit model, respectively. The estimation results indicate that time of day, speed limit, and presence of medians have statistically significant impacts on the EV accident size. Finally, some countermeasures to prevent EV accidents are proposed based on the research results.
{"title":"Examination of the Factors Influencing the Electric Vehicle Accident Size in Norway (2020–2021)","authors":"Xuerui Hou, Meiling Su, Chenhui Liu, Ying Li, Qinglu Ma","doi":"10.3390/wevj15010003","DOIUrl":"https://doi.org/10.3390/wevj15010003","url":null,"abstract":"With the great increase of electric vehicles (EVs) in the past decade, EV-involved traffic accidents have also been increasing quickly in many countries, bringing many new traffic safety challenges. Norway has the largest EV penetration rate in the world. Using the EV accident data from Norway in 2020 and 2021, this study aims to investigate the features of EV safety comprehensively. Firstly, a descriptive analysis is conducted. It has been found that rear-end collisions are the major collision type of EVs, and EVs are very likely to collide with pedestrians/cyclists. In addition, in terms of roadway type, EV accidents mainly occur on medium- and low-speed roads; in terms of environment, they mainly occur in good visibility conditions and dry road surface conditions. Then, a regression analysis is conducted to identify the key factors affecting the accident size, which is the number of traffic units involved in an accident and taken as the accident severity surrogate here. Since EV accidents are divided into four categories in order of accident size, the ordered logit model is adopted. It divides a multi-categorical dependent variable into multiple binary data points in order and calculates the probability of the dependent variable falling into each category with the logit model, respectively. The estimation results indicate that time of day, speed limit, and presence of medians have statistically significant impacts on the EV accident size. Finally, some countermeasures to prevent EV accidents are proposed based on the research results.","PeriodicalId":38979,"journal":{"name":"World Electric Vehicle Journal","volume":"72 10","pages":""},"PeriodicalIF":2.3,"publicationDate":"2023-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138956670","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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