R. Swarnkar, Harikrishnan Ramachandran, S. Ali, Rani Jabbar
In recent years, artificial intelligence and machine learning have captured the attention of researchers and industrialists in order to estimate and predict the state of batteries. The quality of data must be good, and the source of data must be the same for different models’ performance comparisons. The lithium-ion battery is popularly used because of its high energy density and its compact size. Due to the non-linear and complex characteristics of lithium-ion batteries, electric vehicle users have to know about battery health conditions. Different types of state estimation methods are used, namely, electrochemical-based, equivalent circuit model (ECM) based, and data-driven approaches. This paper is a survey of electric vehicle history, different battery chemistries, battery management system (BMS) basics and key challenges and solutions in BMS, and in-depth discussions about other battery state of charge and state of health estimation methods. Research trend analysis, critical analysis of this work, limitations, and future directions of existing works are discussed. This paper also provides information on the open-access available datasets of different battery chemistry for a data-driven approach. This paper highlights the key challenges of state estimation techniques. Knowledge of accurate battery state of charge (SOC) provides critical information about remaining available energy. In comparison, battery state of health (SOH) indicates its current health condition, remaining lifetime, performance, and proper energy management of the electric vehicles.
{"title":"A Systematic Literature Review of State of Health and State of Charge Estimation Methods for Batteries Used in Electric Vehicle Applications","authors":"R. Swarnkar, Harikrishnan Ramachandran, S. Ali, Rani Jabbar","doi":"10.3390/wevj14090247","DOIUrl":"https://doi.org/10.3390/wevj14090247","url":null,"abstract":"In recent years, artificial intelligence and machine learning have captured the attention of researchers and industrialists in order to estimate and predict the state of batteries. The quality of data must be good, and the source of data must be the same for different models’ performance comparisons. The lithium-ion battery is popularly used because of its high energy density and its compact size. Due to the non-linear and complex characteristics of lithium-ion batteries, electric vehicle users have to know about battery health conditions. Different types of state estimation methods are used, namely, electrochemical-based, equivalent circuit model (ECM) based, and data-driven approaches. This paper is a survey of electric vehicle history, different battery chemistries, battery management system (BMS) basics and key challenges and solutions in BMS, and in-depth discussions about other battery state of charge and state of health estimation methods. Research trend analysis, critical analysis of this work, limitations, and future directions of existing works are discussed. This paper also provides information on the open-access available datasets of different battery chemistry for a data-driven approach. This paper highlights the key challenges of state estimation techniques. Knowledge of accurate battery state of charge (SOC) provides critical information about remaining available energy. In comparison, battery state of health (SOH) indicates its current health condition, remaining lifetime, performance, and proper energy management of the electric vehicles.","PeriodicalId":38979,"journal":{"name":"World Electric Vehicle Journal","volume":" ","pages":""},"PeriodicalIF":2.3,"publicationDate":"2023-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43128413","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}
Compared with batteries, ultracapacitors have higher specific power and longer cycle life. They can act as power buffers to absorb peak power during charging and discharging, playing a role in peak shaving and valley filling, thereby extending the cycle life of the battery. In this article, a replaceable battery electric coupe SUV equipped with a lithium iron phosphate (LiFePO4) power battery is taken as the research object, and a vehicle dynamics simulation model is established on the MATLAB/Simulink platform. Parameter matching and control optimization for a hybrid energy storage system (HESS) are conducted. Through a proven semiempirical cycle model of the LiFePO4 power battery, the operating cycle life model is derived and used to estimate the battery cycle life. World Light Vehicle Test Cycle (WLTC) simulation results show that the HESS with 308 ultracapacitors can extend the cycle life of the LiFePO4 power battery by 34.24%, thus significantly reducing the operation cost of the battery replacement station.
与电池相比，超级电容器具有更高的比功率和更长的循环寿命。它们可以作为电源缓冲器，在充放电过程中吸收峰值功率，起到调峰和填谷的作用，从而延长电池的循环寿命。本文以一款搭载磷酸铁锂(LiFePO4)动力电池的可更换电池电动轿跑车SUV为研究对象，在MATLAB/Simulink平台上建立了整车动力学仿真模型。对混合储能系统(HESS)进行参数匹配和控制优化。通过验证的LiFePO4动力电池半经验循环模型，推导出运行循环寿命模型，并用于电池循环寿命的估算。World Light Vehicle Test Cycle (WLTC)仿真结果表明，308个超级电容的HESS可使LiFePO4动力电池的循环寿命延长34.24%，从而显著降低电池更换站的运行成本。
{"title":"The Impact of Hybrid Energy Storage System on the Battery Cycle Life of Replaceable Battery Electric Vehicle","authors":"Wei Zhang, Jue Yang","doi":"10.3390/wevj14090248","DOIUrl":"https://doi.org/10.3390/wevj14090248","url":null,"abstract":"Compared with batteries, ultracapacitors have higher specific power and longer cycle life. They can act as power buffers to absorb peak power during charging and discharging, playing a role in peak shaving and valley filling, thereby extending the cycle life of the battery. In this article, a replaceable battery electric coupe SUV equipped with a lithium iron phosphate (LiFePO4) power battery is taken as the research object, and a vehicle dynamics simulation model is established on the MATLAB/Simulink platform. Parameter matching and control optimization for a hybrid energy storage system (HESS) are conducted. Through a proven semiempirical cycle model of the LiFePO4 power battery, the operating cycle life model is derived and used to estimate the battery cycle life. World Light Vehicle Test Cycle (WLTC) simulation results show that the HESS with 308 ultracapacitors can extend the cycle life of the LiFePO4 power battery by 34.24%, thus significantly reducing the operation cost of the battery replacement station.","PeriodicalId":38979,"journal":{"name":"World Electric Vehicle Journal","volume":" ","pages":""},"PeriodicalIF":2.3,"publicationDate":"2023-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47182269","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 autonomous driving systems, high-speed and real-time image processing, along with object recognition, are crucial technologies. This paper builds upon the research achievements in industrial item-sorting systems and proposes an object-recognition and sorting system for autonomous driving. In industrial sorting lines, goods-sorting robots often need to work at high speeds to efficiently sort large volumes of items. This poses a challenge to the robot’s real-time vision and sorting capabilities, making it both practical and economically viable to implement a real-time and low-cost sorting system in a real-world industrial sorting line. Existing sorting systems have limitations such as high cost, high computing resource consumption, and high power consumption. These issues mean that existing sorting systems are typically used only in large industrial plants. In this paper, we design a high-speed, low-cost, low-resource-consumption FPGA (Field-Programmable Gate Array)-based item-sorting system that achieves similar performance to current mainstream sorting systems but at a lower cost and consumption. The recognition component employs a morphological-recognition method, which segments the image using a frame difference algorithm and then extracts the color and shape features of the items. To handle sorting, a six-degrees-of-freedom robotic arm is introduced into the sorting segment. The improved cubic B-spline interpolation algorithm is employed to plan the motion trajectory and consequently control the robotic arm to execute the corresponding actions. Through a series of experiments, this system achieves an average recognition delay of 25.26 ms, ensures smooth operation of the gripping motion trajectory, minimizes resource consumption, and reduces implementation costs.
{"title":"An FPGA-Based Hardware Low-Cost, Low-Consumption Target-Recognition and Sorting System","authors":"Yulu Wang, Yi Han, Jun Chen, Zhou Wang, Yi Zhong","doi":"10.3390/wevj14090245","DOIUrl":"https://doi.org/10.3390/wevj14090245","url":null,"abstract":"In autonomous driving systems, high-speed and real-time image processing, along with object recognition, are crucial technologies. This paper builds upon the research achievements in industrial item-sorting systems and proposes an object-recognition and sorting system for autonomous driving. In industrial sorting lines, goods-sorting robots often need to work at high speeds to efficiently sort large volumes of items. This poses a challenge to the robot’s real-time vision and sorting capabilities, making it both practical and economically viable to implement a real-time and low-cost sorting system in a real-world industrial sorting line. Existing sorting systems have limitations such as high cost, high computing resource consumption, and high power consumption. These issues mean that existing sorting systems are typically used only in large industrial plants. In this paper, we design a high-speed, low-cost, low-resource-consumption FPGA (Field-Programmable Gate Array)-based item-sorting system that achieves similar performance to current mainstream sorting systems but at a lower cost and consumption. The recognition component employs a morphological-recognition method, which segments the image using a frame difference algorithm and then extracts the color and shape features of the items. To handle sorting, a six-degrees-of-freedom robotic arm is introduced into the sorting segment. The improved cubic B-spline interpolation algorithm is employed to plan the motion trajectory and consequently control the robotic arm to execute the corresponding actions. Through a series of experiments, this system achieves an average recognition delay of 25.26 ms, ensures smooth operation of the gripping motion trajectory, minimizes resource consumption, and reduces implementation costs.","PeriodicalId":38979,"journal":{"name":"World Electric Vehicle Journal","volume":" ","pages":""},"PeriodicalIF":2.3,"publicationDate":"2023-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45450982","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 counter the problems of cracks and large area fractures in the welding points of quick-replacement battery boxes for electric vehicles (which may lead to the concentration of stress), in this study, a fatigue analysis of the welding points, based on a load spectrum, was used to predict welding points’ fatigue and improve the structural life of quick-replacement battery boxes. Firstly, a model of the quick-replacement battery box was established in SolidWorks software; secondly, the welding points’ fatigue was analyzed using the Optistruct module of HyperMesh software, and the topology of the quick-replacement battery box was optimized according to the results of the analysis; finally, for testing purposes and to achieve a lighter weight and an improved structural life, the fatigue of the welding points of the optimized battery box was analyzed. The results of the analysis showed that the force of the quick-replacement battery box was primarily concentrated at the connection between the middle bottom plate and the partition. Additionally, retaining the number of welding points at the hanging ear was shown to be beneficial for maintaining stiffness during electric vehicle operation; however, the number of welding points at the partition connection could be appropriately reduced. Before optimization, the maximum fatigue damage values of the welding points were 2.763 × 10−6, 3.833 × 10−6, and 6.728 × 10−6, respectively, satisfying the criteria of fatigue damage to the welding points. After optimization, the fatigue damage values of the welding points in the quick-replacement battery box were significantly reduced to 4.431 × 10−8, 4.562 × 10−8, and 8.885 × 10−8, respectively, compared with their pre-optimized levels. Consequently, the stress concentration was alleviated effectively, thereby meeting the conditions for fatigue damage. These results have important theoretical and engineering significance for the design and optimization of quick-replacement battery boxes for electric vehicles.
{"title":"A Welding Fatigue Analysis of a Quick-Replacement Battery Box for Electric Vehicles","authors":"Jianying Li, Jienan Zhou, Junjie Chen","doi":"10.3390/wevj14090246","DOIUrl":"https://doi.org/10.3390/wevj14090246","url":null,"abstract":"In order to counter the problems of cracks and large area fractures in the welding points of quick-replacement battery boxes for electric vehicles (which may lead to the concentration of stress), in this study, a fatigue analysis of the welding points, based on a load spectrum, was used to predict welding points’ fatigue and improve the structural life of quick-replacement battery boxes. Firstly, a model of the quick-replacement battery box was established in SolidWorks software; secondly, the welding points’ fatigue was analyzed using the Optistruct module of HyperMesh software, and the topology of the quick-replacement battery box was optimized according to the results of the analysis; finally, for testing purposes and to achieve a lighter weight and an improved structural life, the fatigue of the welding points of the optimized battery box was analyzed. The results of the analysis showed that the force of the quick-replacement battery box was primarily concentrated at the connection between the middle bottom plate and the partition. Additionally, retaining the number of welding points at the hanging ear was shown to be beneficial for maintaining stiffness during electric vehicle operation; however, the number of welding points at the partition connection could be appropriately reduced. Before optimization, the maximum fatigue damage values of the welding points were 2.763 × 10−6, 3.833 × 10−6, and 6.728 × 10−6, respectively, satisfying the criteria of fatigue damage to the welding points. After optimization, the fatigue damage values of the welding points in the quick-replacement battery box were significantly reduced to 4.431 × 10−8, 4.562 × 10−8, and 8.885 × 10−8, respectively, compared with their pre-optimized levels. Consequently, the stress concentration was alleviated effectively, thereby meeting the conditions for fatigue damage. These results have important theoretical and engineering significance for the design and optimization of quick-replacement battery boxes for electric vehicles.","PeriodicalId":38979,"journal":{"name":"World Electric Vehicle Journal","volume":" ","pages":""},"PeriodicalIF":2.3,"publicationDate":"2023-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48555025","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}
Pinpin Qin, Fumao Wu, Shenglin Bin, Xing Li, Fuming Ya
To address traffic congestion in urban expressways during the transition from wide to narrow sections, this study proposed a car-following strategy based on deep reinforcement learning. Firstly, a car-following strategy was developed based on a twin-delayed deep deterministic policy gradient (TD3) algorithm, and a multi-objective constrained reward function was designed by comprehensively considering safety, traffic efficiency, and ride comfort. Secondly, 214 car-following periods and 13 platoon-following periods were selected from the natural driving database for the strategies training and testing. Finally, the effectiveness of the proposed strategy was verified through simulation experiments of car-following and platoon-following. The results showed that compared to human-driven vehicles (HDV), the TD3 and deep deterministic policy gradient (DDPG)-based strategies enhanced traffic efficiency by over 29% and ride comfort by more than 60%. Furthermore, compared to DDPG, the relative errors between the following distance and desired safety distance using TD3 could be reduced by 1.28% and 1.37% in simulation experiments of car-following and platoon-following, respectively. This study provides a new approach to alleviate traffic congestion for wide-to-narrow road sections in urban expressways.
{"title":"High-Accuracy, High-Efficiency, and Comfortable Car-Following Strategy Based on TD3 for Wide-to-Narrow Road Sections","authors":"Pinpin Qin, Fumao Wu, Shenglin Bin, Xing Li, Fuming Ya","doi":"10.3390/wevj14090244","DOIUrl":"https://doi.org/10.3390/wevj14090244","url":null,"abstract":"To address traffic congestion in urban expressways during the transition from wide to narrow sections, this study proposed a car-following strategy based on deep reinforcement learning. Firstly, a car-following strategy was developed based on a twin-delayed deep deterministic policy gradient (TD3) algorithm, and a multi-objective constrained reward function was designed by comprehensively considering safety, traffic efficiency, and ride comfort. Secondly, 214 car-following periods and 13 platoon-following periods were selected from the natural driving database for the strategies training and testing. Finally, the effectiveness of the proposed strategy was verified through simulation experiments of car-following and platoon-following. The results showed that compared to human-driven vehicles (HDV), the TD3 and deep deterministic policy gradient (DDPG)-based strategies enhanced traffic efficiency by over 29% and ride comfort by more than 60%. Furthermore, compared to DDPG, the relative errors between the following distance and desired safety distance using TD3 could be reduced by 1.28% and 1.37% in simulation experiments of car-following and platoon-following, respectively. This study provides a new approach to alleviate traffic congestion for wide-to-narrow road sections in urban expressways.","PeriodicalId":38979,"journal":{"name":"World Electric Vehicle Journal","volume":" ","pages":""},"PeriodicalIF":2.3,"publicationDate":"2023-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49288813","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}
Multiple object tracking (MOT), as a core technology for environment perception in autonomous driving, has attracted attention from researchers. Combing the advantages of batch global optimization, we present a novel online MOT framework for autonomous driving, consisting of feature extraction and data association on a temporal window. In the feature extraction stage, we design a three-channel appearance feature extraction network based on metric learning by using ResNet50 as the backbone network and the triplet loss function and employ a Kalman Filter with a constant acceleration motion model to optimize and predict the object bounding box information, so as to obtain reliable and discriminative object representation features. For data association, to reduce the ID switches, the min-cost flow of global association is introduced within the temporal window composed of consecutive multi-frame images. The trajectories within the temporal window are divided into two categories, active trajectories and inactive trajectories, and the appearance, motion affinities between each category of trajectories, and detections are calculated, respectively. Based on this, a sparse affinity network is constructed, and the data association is achieved using the min-cost flow problem of the network. Qualitative experimental results on KITTI MOT public benchmark dataset and real-world campus scenario sequences validate the effectiveness and robustness of our method. Compared with the homogeneous, vision-based MOT methods, quantitative experimental results demonstrate that our method has competitive advantages in terms of higher order tracking accuracy, association accuracy, and ID switches.
{"title":"Online Multiple Object Tracking Using Min-Cost Flow on Temporal Window for Autonomous Driving","authors":"Hongjian Wei, Yingping Huang, Qian Zhang, Zhiyang Guo","doi":"10.3390/wevj14090243","DOIUrl":"https://doi.org/10.3390/wevj14090243","url":null,"abstract":"Multiple object tracking (MOT), as a core technology for environment perception in autonomous driving, has attracted attention from researchers. Combing the advantages of batch global optimization, we present a novel online MOT framework for autonomous driving, consisting of feature extraction and data association on a temporal window. In the feature extraction stage, we design a three-channel appearance feature extraction network based on metric learning by using ResNet50 as the backbone network and the triplet loss function and employ a Kalman Filter with a constant acceleration motion model to optimize and predict the object bounding box information, so as to obtain reliable and discriminative object representation features. For data association, to reduce the ID switches, the min-cost flow of global association is introduced within the temporal window composed of consecutive multi-frame images. The trajectories within the temporal window are divided into two categories, active trajectories and inactive trajectories, and the appearance, motion affinities between each category of trajectories, and detections are calculated, respectively. Based on this, a sparse affinity network is constructed, and the data association is achieved using the min-cost flow problem of the network. Qualitative experimental results on KITTI MOT public benchmark dataset and real-world campus scenario sequences validate the effectiveness and robustness of our method. Compared with the homogeneous, vision-based MOT methods, quantitative experimental results demonstrate that our method has competitive advantages in terms of higher order tracking accuracy, association accuracy, and ID switches.","PeriodicalId":38979,"journal":{"name":"World Electric Vehicle Journal","volume":" ","pages":""},"PeriodicalIF":2.3,"publicationDate":"2023-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48634244","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}
To solve the problem of smooth switching between the car-following model and lane-changing model, the Intelligent Driver Model (IDM) for a single lane was used to study the driver’s behavior switching mechanism of normally following, generating intentions to change lanes, creating space and speed gains, and performing lane change. In the case of sufficient lane-changing space and speed gains, the ego vehicle’s intention to change lanes was considered to solve the switching boundary between car-following behavior and lane-changing behavior, which is also the IDM failure point. In the event that there are no lane-changing gains, the IDM was optimized by incorporating the constraint components of the target lane vehicles in conjunction with the actual motion state of the ego vehicle, and the Stepless Switching Intelligent Driver Model (SSIDM) was constructed. Drivers’ natural driving information was collected, and scenario mining was performed on structured roads. On the basis of the collected data, an elliptic equation was used to fit the behavior switching boundary, and the two component balance coefficients of the front and rear vehicles on the target lane were identified. According to the test set verification results, the Mean Square Error (MSE) of the SSIDM is 2.172, which is 57.98% less than that of the conventional single-lane IDM. The SSIDM can accomplish stepless switching comparable to the driver’s behavior between the car-following behavior and the lane-changing behavior, with greater precision than IDM. This research can provide theoretical support for the construction of the point-to-point driving model and the development of L2+ autonomous driving functions. It can provide assistance for the landing and application of full-behavior and full-scene autonomous driving.
{"title":"Research on the SSIDM Modeling Mechanism for Equivalent Driver’s Behavior","authors":"Rui Fang","doi":"10.3390/wevj14090242","DOIUrl":"https://doi.org/10.3390/wevj14090242","url":null,"abstract":"To solve the problem of smooth switching between the car-following model and lane-changing model, the Intelligent Driver Model (IDM) for a single lane was used to study the driver’s behavior switching mechanism of normally following, generating intentions to change lanes, creating space and speed gains, and performing lane change. In the case of sufficient lane-changing space and speed gains, the ego vehicle’s intention to change lanes was considered to solve the switching boundary between car-following behavior and lane-changing behavior, which is also the IDM failure point. In the event that there are no lane-changing gains, the IDM was optimized by incorporating the constraint components of the target lane vehicles in conjunction with the actual motion state of the ego vehicle, and the Stepless Switching Intelligent Driver Model (SSIDM) was constructed. Drivers’ natural driving information was collected, and scenario mining was performed on structured roads. On the basis of the collected data, an elliptic equation was used to fit the behavior switching boundary, and the two component balance coefficients of the front and rear vehicles on the target lane were identified. According to the test set verification results, the Mean Square Error (MSE) of the SSIDM is 2.172, which is 57.98% less than that of the conventional single-lane IDM. The SSIDM can accomplish stepless switching comparable to the driver’s behavior between the car-following behavior and the lane-changing behavior, with greater precision than IDM. This research can provide theoretical support for the construction of the point-to-point driving model and the development of L2+ autonomous driving functions. It can provide assistance for the landing and application of full-behavior and full-scene autonomous driving.","PeriodicalId":38979,"journal":{"name":"World Electric Vehicle Journal","volume":" ","pages":""},"PeriodicalIF":2.3,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43334545","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}
Soheil Saeedipour, A. Gharehghani, Jabraeil Ahbabi Saray, A. M. Andwari, Maciej Mikulski
The charging and discharging process of batteries generates a significant amount of heat, which can adversely affect their lifespan and safety. This study aims to enhance the performance of a lithium-ion battery (LIB) pack with a high discharge rate (5C) by proposing a combined battery thermal management system (BTMS) consisting of improved phase change materials (paraffin/aluminum composite) and forced-air convection. Battery thermal performance is simulated using computational fluid dynamics (CFD) to study the effects of heat transfer and flow parameters. To evaluate the impact of essential parameters on the thermal performance of the battery module, temperature uniformity and maximum temperature in the cells are evaluated. For the proposed cooling system, an ambient temperature of 24.5 °C and the application of a 3 mm thick paraffin/aluminum composite showed the best cooling effect. In addition, a 2 m/s inlet velocity with 25 mm cell spacing provided the best cooling performance, thus reducing the maximum temperature. The paraffin can effectively manage thermal parameters maintaining battery temperature stability and uniformity. Simulation results demonstrated that the proposed cooling system combined with forced-air convection, paraffin, and metal foam effectively reduced the maximum temperature and temperature difference in the battery by 308 K and 2.0 K, respectively.
{"title":"Proposing a Hybrid Thermal Management System Based on Phase Change Material/Metal Foam for Lithium-Ion Batteries","authors":"Soheil Saeedipour, A. Gharehghani, Jabraeil Ahbabi Saray, A. M. Andwari, Maciej Mikulski","doi":"10.3390/wevj14090240","DOIUrl":"https://doi.org/10.3390/wevj14090240","url":null,"abstract":"The charging and discharging process of batteries generates a significant amount of heat, which can adversely affect their lifespan and safety. This study aims to enhance the performance of a lithium-ion battery (LIB) pack with a high discharge rate (5C) by proposing a combined battery thermal management system (BTMS) consisting of improved phase change materials (paraffin/aluminum composite) and forced-air convection. Battery thermal performance is simulated using computational fluid dynamics (CFD) to study the effects of heat transfer and flow parameters. To evaluate the impact of essential parameters on the thermal performance of the battery module, temperature uniformity and maximum temperature in the cells are evaluated. For the proposed cooling system, an ambient temperature of 24.5 °C and the application of a 3 mm thick paraffin/aluminum composite showed the best cooling effect. In addition, a 2 m/s inlet velocity with 25 mm cell spacing provided the best cooling performance, thus reducing the maximum temperature. The paraffin can effectively manage thermal parameters maintaining battery temperature stability and uniformity. Simulation results demonstrated that the proposed cooling system combined with forced-air convection, paraffin, and metal foam effectively reduced the maximum temperature and temperature difference in the battery by 308 K and 2.0 K, respectively.","PeriodicalId":38979,"journal":{"name":"World Electric Vehicle Journal","volume":" ","pages":""},"PeriodicalIF":2.3,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47997995","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}
During wireless charging of the traction battery of electrically powered vehicles, the active area between the ground and vehicle assemblies must be monitored for inductive power transfer. If metallic foreign objects enter this area, they interact with the magnetic field and can heat up strongly, and thus become a potential source of hazard. To detect such foreign objects, measurements based on passive inductive sensors have already been carried out in advance. However, a large number of factors influence the detectability of metallic foreign objects, such as the characteristics of the magnetic field of the ground assembly coil, the size, shape, position, orientation, and material composition of the foreign objects, or the design of the sensor coils. The related practical testing effort can be reduced if the characteristics of the charging system and the foreign object detection system can be simulated. Therefore, simulation models were developed within the scope of this work and validated with the help of practical measurements. These models were used in the next step to analyze new test arrangements that had not yet been investigated by measurement. In the simulations described here, precision in the range of 1 mV could be achieved. Cumulatively, many influencing factors can be easily investigated, and results can be generated in a largely automated manner and typically in a wider variety than with practical measurements.
{"title":"Simulation of Foreign Object Detection Using Passive Inductive Sensors in a Wireless Charging System for Electric Vehicles","authors":"U. Hentschel, M. Helwig, A. Winkler, Niels Modler","doi":"10.3390/wevj14090241","DOIUrl":"https://doi.org/10.3390/wevj14090241","url":null,"abstract":"During wireless charging of the traction battery of electrically powered vehicles, the active area between the ground and vehicle assemblies must be monitored for inductive power transfer. If metallic foreign objects enter this area, they interact with the magnetic field and can heat up strongly, and thus become a potential source of hazard. To detect such foreign objects, measurements based on passive inductive sensors have already been carried out in advance. However, a large number of factors influence the detectability of metallic foreign objects, such as the characteristics of the magnetic field of the ground assembly coil, the size, shape, position, orientation, and material composition of the foreign objects, or the design of the sensor coils. The related practical testing effort can be reduced if the characteristics of the charging system and the foreign object detection system can be simulated. Therefore, simulation models were developed within the scope of this work and validated with the help of practical measurements. These models were used in the next step to analyze new test arrangements that had not yet been investigated by measurement. In the simulations described here, precision in the range of 1 mV could be achieved. Cumulatively, many influencing factors can be easily investigated, and results can be generated in a largely automated manner and typically in a wider variety than with practical measurements.","PeriodicalId":38979,"journal":{"name":"World Electric Vehicle Journal","volume":" ","pages":""},"PeriodicalIF":2.3,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45089579","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}
Chengcheng Liu, Hongming Zhang, Shaoheng Wang, Shiwei Zhang, Youhua H. Wang
Permanent magnet motors have become an important component of industrial production, transportation, and aerospace due to their advantages of high torque density, high power density, high reliability, low losses, and high efficiency. Permanent magnet claw pole motor (PMCPM) is a special type of transverse flux motor which has a higher torque density compared to traditional permanent magnet motors. Due to the absence of winding ends, its axial space utilization is high, and the usage of windings is greatly reduced, reducing the cost and weight of the motor. PMCPM has the advantages of small space, a light weight, a high torque density, a high efficiency, and simple production, which have potential for use in the field of electric vehicles. The double-stator structure design can improve the torque density, efficiency, and radial space utilization of PMCPM, which helps to expand their applications in the field of electric vehicles. This article designs two PMCPM with concentrated winding while different rotor structures (PMCPM1 and PMCPM2) and a three-dimensional finite element method is employed to compare and analyze the performance of PMCPM1 and PMCPM2 and the traditional PMCPM (TPMCPM). Multiphysics analysis is carried out for PMCPM1 and PMCPM2. The stress of the inner and outer stators during interference assembly are analyzed. In this paper, a hybrid material core design is proposed, in which the stator yoke is rolled by silicon steel material and the stator claw pole is pressed by the SMC die method. The multiphysics simulation performance of the PMCPM1 and PMCPM2 with hybrid cores is analyzed.
{"title":"Design, Analysis, and Comparison of Permanent Magnet Claw Pole Motor with Concentrated Winding and Double Stator","authors":"Chengcheng Liu, Hongming Zhang, Shaoheng Wang, Shiwei Zhang, Youhua H. Wang","doi":"10.3390/wevj14090237","DOIUrl":"https://doi.org/10.3390/wevj14090237","url":null,"abstract":"Permanent magnet motors have become an important component of industrial production, transportation, and aerospace due to their advantages of high torque density, high power density, high reliability, low losses, and high efficiency. Permanent magnet claw pole motor (PMCPM) is a special type of transverse flux motor which has a higher torque density compared to traditional permanent magnet motors. Due to the absence of winding ends, its axial space utilization is high, and the usage of windings is greatly reduced, reducing the cost and weight of the motor. PMCPM has the advantages of small space, a light weight, a high torque density, a high efficiency, and simple production, which have potential for use in the field of electric vehicles. The double-stator structure design can improve the torque density, efficiency, and radial space utilization of PMCPM, which helps to expand their applications in the field of electric vehicles. This article designs two PMCPM with concentrated winding while different rotor structures (PMCPM1 and PMCPM2) and a three-dimensional finite element method is employed to compare and analyze the performance of PMCPM1 and PMCPM2 and the traditional PMCPM (TPMCPM). Multiphysics analysis is carried out for PMCPM1 and PMCPM2. The stress of the inner and outer stators during interference assembly are analyzed. In this paper, a hybrid material core design is proposed, in which the stator yoke is rolled by silicon steel material and the stator claw pole is pressed by the SMC die method. The multiphysics simulation performance of the PMCPM1 and PMCPM2 with hybrid cores is analyzed.","PeriodicalId":38979,"journal":{"name":"World Electric Vehicle Journal","volume":"40 ","pages":""},"PeriodicalIF":2.3,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41275559","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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