Pub Date : 2024-03-01DOI: 10.3390/batteries10030084
Chao Shi, Hewu Wang, Hengjie Shen, Juan Wang, Cheng Li, Yalun Li, Wenqiang Xu, Minghai Li
Layered ternary materials with high nickel content are regarded as the most promising cathode materials for high-energy-density lithium-ion batteries, owing to their advantages of high capacity, low cost, and relatively good safety. However, as the nickel content increases in ternary layered materials, their thermal stability noticeably decreases. It is of paramount importance to explore the characteristics of thermal runaway for lithium-ion batteries. In this study, two high-nickel LiNi0.9Co0.05Mn0.05O2 batteries were laterally heated to thermal runaway in a sealed chamber filled with nitrogen to investigate the thermal characteristics and gas compositions. The temperature of the battery tabs was measured, revealing that both batteries were in a critical state of thermal runaway near 120 degrees Celsius. A quantitative analysis method was employed during the eruption process, dividing it into three stages: ultra-fast, fast, and slow; the corresponding durations for the two batteries were 3, 2, 27 s and 3, 3, 26 s. By comparing the changes in chamber pressure, it was observed that both batteries exhibited a similar continuous venting duration of 32 s. However, the pressure fluctuation ranges of the two samples were 99.5 and 68.2 kPa·m·s−1. Compared to the other sample, the 211 Ah sample exhibited larger chamber pressure fluctuations and reached higher peak pressures, indicating a higher risk of explosion. In the experimental phenomenon captured by a high-speed camera, it took only 1 s for the sample to transition from the opening of the safety valve to filling the experimental chamber with smoke. The battery with higher energy density exhibited more intense eruption during thermal runaway, resulting in more severe mass loss. The mass loss of the two samples is 73% and 64.87%. The electrolyte also reacted more completely, resulting in a reduced number of measured exhaust components. The main components of gaseous ejections are CO, CO2, H2, C2H4, and CH4. For the 211 Ah battery, the vented gases were mainly composed of CO (41.3%), CO2 (24.8%), H2 (21%), C2H4 (7.4%) and CH4 (3.9%), while those for the other 256 Ah battery were mainly CO (30.6%), CO2 (28.5%), H2 (21.7%), C2H4 (12.4%) and CH4 (5.8%). Comparatively, the higher-capacity battery produced more gases. The gas volumes, converted to standard conditions (0 °C, 101 kPa) and normalized, resulted in 1.985 L/Ah and 2.182 L/Ah, respectively. The results provide valuable guidance for the protection of large-capacity, high-energy-density battery systems. The quantitative analysis of the eruption process has provided assistance to fire alarm systems and firefighting strategies.
{"title":"Thermal Runaway Characteristics and Gas Analysis of LiNi0.9Co0.05Mn0.05O2 Batteries","authors":"Chao Shi, Hewu Wang, Hengjie Shen, Juan Wang, Cheng Li, Yalun Li, Wenqiang Xu, Minghai Li","doi":"10.3390/batteries10030084","DOIUrl":"https://doi.org/10.3390/batteries10030084","url":null,"abstract":"Layered ternary materials with high nickel content are regarded as the most promising cathode materials for high-energy-density lithium-ion batteries, owing to their advantages of high capacity, low cost, and relatively good safety. However, as the nickel content increases in ternary layered materials, their thermal stability noticeably decreases. It is of paramount importance to explore the characteristics of thermal runaway for lithium-ion batteries. In this study, two high-nickel LiNi0.9Co0.05Mn0.05O2 batteries were laterally heated to thermal runaway in a sealed chamber filled with nitrogen to investigate the thermal characteristics and gas compositions. The temperature of the battery tabs was measured, revealing that both batteries were in a critical state of thermal runaway near 120 degrees Celsius. A quantitative analysis method was employed during the eruption process, dividing it into three stages: ultra-fast, fast, and slow; the corresponding durations for the two batteries were 3, 2, 27 s and 3, 3, 26 s. By comparing the changes in chamber pressure, it was observed that both batteries exhibited a similar continuous venting duration of 32 s. However, the pressure fluctuation ranges of the two samples were 99.5 and 68.2 kPa·m·s−1. Compared to the other sample, the 211 Ah sample exhibited larger chamber pressure fluctuations and reached higher peak pressures, indicating a higher risk of explosion. In the experimental phenomenon captured by a high-speed camera, it took only 1 s for the sample to transition from the opening of the safety valve to filling the experimental chamber with smoke. The battery with higher energy density exhibited more intense eruption during thermal runaway, resulting in more severe mass loss. The mass loss of the two samples is 73% and 64.87%. The electrolyte also reacted more completely, resulting in a reduced number of measured exhaust components. The main components of gaseous ejections are CO, CO2, H2, C2H4, and CH4. For the 211 Ah battery, the vented gases were mainly composed of CO (41.3%), CO2 (24.8%), H2 (21%), C2H4 (7.4%) and CH4 (3.9%), while those for the other 256 Ah battery were mainly CO (30.6%), CO2 (28.5%), H2 (21.7%), C2H4 (12.4%) and CH4 (5.8%). Comparatively, the higher-capacity battery produced more gases. The gas volumes, converted to standard conditions (0 °C, 101 kPa) and normalized, resulted in 1.985 L/Ah and 2.182 L/Ah, respectively. The results provide valuable guidance for the protection of large-capacity, high-energy-density battery systems. The quantitative analysis of the eruption process has provided assistance to fire alarm systems and firefighting strategies.","PeriodicalId":502356,"journal":{"name":"Batteries","volume":"56 14","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140085908","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}
Pub Date : 2024-03-01DOI: 10.3390/batteries10030082
Jiegang Wang, Kerui Li, Chi Zhang, Zhenpo Wang, Yangjie Zhou, Peng Liu
Inconsistency is a key factor triggering safety problems in battery packs. The inconsistency evaluation of retired batteries is of great significance to ensure the safe and stable operation of batteries during subsequent gradual use. This paper summaries the commonly used diagnostic methods for battery inconsistency assessment. The local outlier factor (LOF) algorithm and the improved Shannon entropy (ImEn) algorithm are selected for validation based on the individual voltage data from real-world vehicles. Then, a comprehensive inconsistency evaluation strategy for retired batteries with many levels and indicators is established based on the three parameters of LOF, ImEn, and cell voltage range. Finally, the evaluation strategy is validated using two real-world vehicle samples of retired batteries. The results show that the proposed method can achieve the inconsistency evaluation of retired batteries quickly and effectively.
{"title":"Research on Inconsistency Evaluation of Retired Battery Systems in Real-World Vehicles","authors":"Jiegang Wang, Kerui Li, Chi Zhang, Zhenpo Wang, Yangjie Zhou, Peng Liu","doi":"10.3390/batteries10030082","DOIUrl":"https://doi.org/10.3390/batteries10030082","url":null,"abstract":"Inconsistency is a key factor triggering safety problems in battery packs. The inconsistency evaluation of retired batteries is of great significance to ensure the safe and stable operation of batteries during subsequent gradual use. This paper summaries the commonly used diagnostic methods for battery inconsistency assessment. The local outlier factor (LOF) algorithm and the improved Shannon entropy (ImEn) algorithm are selected for validation based on the individual voltage data from real-world vehicles. Then, a comprehensive inconsistency evaluation strategy for retired batteries with many levels and indicators is established based on the three parameters of LOF, ImEn, and cell voltage range. Finally, the evaluation strategy is validated using two real-world vehicle samples of retired batteries. The results show that the proposed method can achieve the inconsistency evaluation of retired batteries quickly and effectively.","PeriodicalId":502356,"journal":{"name":"Batteries","volume":"20 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140082966","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}
Pub Date : 2024-02-15DOI: 10.3390/batteries10020061
M. Dinh, Thi-Tinh Le, Minwon Park
In this paper, a high-efficiency and low-cost active cell-to-cell balancing circuit for the reuse of electric vehicle (EV) batteries is proposed. In the proposed method, a battery string is divided into two legs to transfer the charge from each cell in one leg to that in the other and a bidirectional CLLC resonant converter is used to transfer energy between the selected cells. Thanks to the proposed structure, the number of bidirectional switches and gate drivers can be reduced by half compared to the conventional direct cell-to-cell topologies, thereby achieving lower cost for the system. The CLLC converter is used to transfer the charge, and it is designed to work at resonant frequencies to achieve zero-voltage zero-current switching (ZVZCS) for all the switches and diodes. Consequently, the system’s efficiency can be enhanced, and hence, the fuel economy of the system can also be improved significantly. To verify the performance of the proposed active cell-balancing system, a prototype is implemented for balancing the three EV battery modules that contain twelve lithium-ion batteries from xEV. The maximum efficiency achieved for the charge transfer is 89.4%, and the balancing efficiency is 96.3%.
{"title":"A Low-Cost and High-Efficiency Active Cell-Balancing Circuit for the Reuse of EV Batteries","authors":"M. Dinh, Thi-Tinh Le, Minwon Park","doi":"10.3390/batteries10020061","DOIUrl":"https://doi.org/10.3390/batteries10020061","url":null,"abstract":"In this paper, a high-efficiency and low-cost active cell-to-cell balancing circuit for the reuse of electric vehicle (EV) batteries is proposed. In the proposed method, a battery string is divided into two legs to transfer the charge from each cell in one leg to that in the other and a bidirectional CLLC resonant converter is used to transfer energy between the selected cells. Thanks to the proposed structure, the number of bidirectional switches and gate drivers can be reduced by half compared to the conventional direct cell-to-cell topologies, thereby achieving lower cost for the system. The CLLC converter is used to transfer the charge, and it is designed to work at resonant frequencies to achieve zero-voltage zero-current switching (ZVZCS) for all the switches and diodes. Consequently, the system’s efficiency can be enhanced, and hence, the fuel economy of the system can also be improved significantly. To verify the performance of the proposed active cell-balancing system, a prototype is implemented for balancing the three EV battery modules that contain twelve lithium-ion batteries from xEV. The maximum efficiency achieved for the charge transfer is 89.4%, and the balancing efficiency is 96.3%.","PeriodicalId":502356,"journal":{"name":"Batteries","volume":"32 14","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139775962","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}
Pub Date : 2024-02-15DOI: 10.3390/batteries10020060
David Geerts, R. Medina, W. V. van Sark, Steven Wilkins
Reducing greenhouse emissions can be done via the electrification of the transport industry. However, there are challenges related to the electrification such as the lifetime of vehicle batteries as well as limitations on the charging possibilities. To cope with some of these challenges, a charge scheduling method for fleets of electric vehicles is presented. Such a method assigns the charging moments (i.e., schedules) of fleets that have more vehicles than chargers. While doing the assignation, the method also maximizes the total Remaining Useful Life (RUL) of all the vehicle batteries. The method consists of two optimization algorithms. The first optimization algorithm determines charging profiles (i.e., charging current vs time) for individual vehicles. The second algorithm finds the charging schedule (i.e. the order in which vehicles are connected to a charger) that maximizes the RUL in the batteries of the entire fleet. To reduce the computational effort of predicting the battery RUL, the method uses a Machine Learning (ML) model. Such a model predicts the RUL of an individual battery while taking into account common stress factors and fabrication-related differences per battery. Simulation results show that charging a single vehicle as late as possible maximizes the RUL of that single vehicle, due to the lower battery degradation. Simulations also show that the ML model accurately predicts the RUL, while taking into account fabrication-related variability in the battery. Additionally, it was shown that this method schedules the charging moments of a fleet, leading to an increased total RUL of all the batteries in the vehicle fleet.
{"title":"Charge Scheduling of Electric Vehicle Fleets: Maximizing Battery Remaining Useful Life Using Machine Learning Models","authors":"David Geerts, R. Medina, W. V. van Sark, Steven Wilkins","doi":"10.3390/batteries10020060","DOIUrl":"https://doi.org/10.3390/batteries10020060","url":null,"abstract":"Reducing greenhouse emissions can be done via the electrification of the transport industry. However, there are challenges related to the electrification such as the lifetime of vehicle batteries as well as limitations on the charging possibilities. To cope with some of these challenges, a charge scheduling method for fleets of electric vehicles is presented. Such a method assigns the charging moments (i.e., schedules) of fleets that have more vehicles than chargers. While doing the assignation, the method also maximizes the total Remaining Useful Life (RUL) of all the vehicle batteries. The method consists of two optimization algorithms. The first optimization algorithm determines charging profiles (i.e., charging current vs time) for individual vehicles. The second algorithm finds the charging schedule (i.e. the order in which vehicles are connected to a charger) that maximizes the RUL in the batteries of the entire fleet. To reduce the computational effort of predicting the battery RUL, the method uses a Machine Learning (ML) model. Such a model predicts the RUL of an individual battery while taking into account common stress factors and fabrication-related differences per battery. Simulation results show that charging a single vehicle as late as possible maximizes the RUL of that single vehicle, due to the lower battery degradation. Simulations also show that the ML model accurately predicts the RUL, while taking into account fabrication-related variability in the battery. Additionally, it was shown that this method schedules the charging moments of a fleet, leading to an increased total RUL of all the batteries in the vehicle fleet.","PeriodicalId":502356,"journal":{"name":"Batteries","volume":"16 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139776385","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}
Pub Date : 2024-02-15DOI: 10.3390/batteries10020061
M. Dinh, Thi-Tinh Le, Minwon Park
In this paper, a high-efficiency and low-cost active cell-to-cell balancing circuit for the reuse of electric vehicle (EV) batteries is proposed. In the proposed method, a battery string is divided into two legs to transfer the charge from each cell in one leg to that in the other and a bidirectional CLLC resonant converter is used to transfer energy between the selected cells. Thanks to the proposed structure, the number of bidirectional switches and gate drivers can be reduced by half compared to the conventional direct cell-to-cell topologies, thereby achieving lower cost for the system. The CLLC converter is used to transfer the charge, and it is designed to work at resonant frequencies to achieve zero-voltage zero-current switching (ZVZCS) for all the switches and diodes. Consequently, the system’s efficiency can be enhanced, and hence, the fuel economy of the system can also be improved significantly. To verify the performance of the proposed active cell-balancing system, a prototype is implemented for balancing the three EV battery modules that contain twelve lithium-ion batteries from xEV. The maximum efficiency achieved for the charge transfer is 89.4%, and the balancing efficiency is 96.3%.
{"title":"A Low-Cost and High-Efficiency Active Cell-Balancing Circuit for the Reuse of EV Batteries","authors":"M. Dinh, Thi-Tinh Le, Minwon Park","doi":"10.3390/batteries10020061","DOIUrl":"https://doi.org/10.3390/batteries10020061","url":null,"abstract":"In this paper, a high-efficiency and low-cost active cell-to-cell balancing circuit for the reuse of electric vehicle (EV) batteries is proposed. In the proposed method, a battery string is divided into two legs to transfer the charge from each cell in one leg to that in the other and a bidirectional CLLC resonant converter is used to transfer energy between the selected cells. Thanks to the proposed structure, the number of bidirectional switches and gate drivers can be reduced by half compared to the conventional direct cell-to-cell topologies, thereby achieving lower cost for the system. The CLLC converter is used to transfer the charge, and it is designed to work at resonant frequencies to achieve zero-voltage zero-current switching (ZVZCS) for all the switches and diodes. Consequently, the system’s efficiency can be enhanced, and hence, the fuel economy of the system can also be improved significantly. To verify the performance of the proposed active cell-balancing system, a prototype is implemented for balancing the three EV battery modules that contain twelve lithium-ion batteries from xEV. The maximum efficiency achieved for the charge transfer is 89.4%, and the balancing efficiency is 96.3%.","PeriodicalId":502356,"journal":{"name":"Batteries","volume":"470 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139835577","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}
Pub Date : 2024-02-15DOI: 10.3390/batteries10020059
Jaehyun Bae, D. Hyun, Jaeyoung Han
With an increase in global warming, battery electric vehicles (BEVs), which are environmentally friendly, have been rapidly commercialized to replace conventional vehicles with internal combustion engines. Unlike traditional internal combustion engine vehicles, the powertrain system of BEVs operates with high efficiency, resulting in lower heat generation. This poses a challenge for cabin heating under low-temperature conditions. Conversely, under high-temperature conditions, the operating temperature of a high-voltage battery (HVB) is lower than the ambient air temperature, which makes cooling through ambient air challenging. To overcome these challenges, in this study, we proposed an integrated thermal management system (ITMS) based on a heat pump system capable of stable thermal management under diverse climatic conditions. Furthermore, to assess the ability of the proposed ITMS to perform thermal management under various climatic conditions, we integrated a detailed powertrain system model incorporating BEV specifications and the proposed ITMS model based on the heat pump system. The ITMS model was evaluated under high-load-driving conditions, specifically the HWFET scenario, demonstrating its capability to perform stable thermal management not only under high-temperature conditions, such as at 36 °C, but also under low-temperature conditions, such as at −10 °C, through the designated thermal management modes.
随着全球变暖的加剧，环保型电池电动汽车（BEV）已迅速商业化，以取代传统的内燃机汽车。与传统内燃机汽车不同，BEV 的动力总成系统运行效率高，发热量低。这给低温条件下的车厢加热带来了挑战。相反，在高温条件下，高压电池（HVB）的工作温度低于环境空气温度，这给通过环境空气降温带来了挑战。为了克服这些挑战，我们在本研究中提出了一种基于热泵系统的集成热管理系统（ITMS），该系统能够在不同气候条件下实现稳定的热管理。此外，为了评估所提出的 ITMS 在各种气候条件下执行热管理的能力，我们整合了一个包含 BEV 规格的详细动力总成系统模型和所提出的基于热泵系统的 ITMS 模型。在高负荷驱动条件下，特别是在 HWFET 情景下，对 ITMS 模型进行了评估，结果表明该模型不仅能够在 36 °C 等高温条件下执行稳定的热管理，而且还能在 -10 °C 等低温条件下通过指定的热管理模式执行稳定的热管理。
{"title":"Adaptive Integrated Thermal Management System for a Stable Driving Environment in Battery Electric Vehicles","authors":"Jaehyun Bae, D. Hyun, Jaeyoung Han","doi":"10.3390/batteries10020059","DOIUrl":"https://doi.org/10.3390/batteries10020059","url":null,"abstract":"With an increase in global warming, battery electric vehicles (BEVs), which are environmentally friendly, have been rapidly commercialized to replace conventional vehicles with internal combustion engines. Unlike traditional internal combustion engine vehicles, the powertrain system of BEVs operates with high efficiency, resulting in lower heat generation. This poses a challenge for cabin heating under low-temperature conditions. Conversely, under high-temperature conditions, the operating temperature of a high-voltage battery (HVB) is lower than the ambient air temperature, which makes cooling through ambient air challenging. To overcome these challenges, in this study, we proposed an integrated thermal management system (ITMS) based on a heat pump system capable of stable thermal management under diverse climatic conditions. Furthermore, to assess the ability of the proposed ITMS to perform thermal management under various climatic conditions, we integrated a detailed powertrain system model incorporating BEV specifications and the proposed ITMS model based on the heat pump system. The ITMS model was evaluated under high-load-driving conditions, specifically the HWFET scenario, demonstrating its capability to perform stable thermal management not only under high-temperature conditions, such as at 36 °C, but also under low-temperature conditions, such as at −10 °C, through the designated thermal management modes.","PeriodicalId":502356,"journal":{"name":"Batteries","volume":"300 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139834510","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}
Pub Date : 2024-02-15DOI: 10.3390/batteries10020062
Yi Cui, Xueling Shen, Hang Zhang, Yanping Yin, Zhanglong Yu, Dong Shi, Yanyan Fang, Ran Xu
Since 2014, the electric vehicle industry in China has flourished and has been accompanied by rapid growth in the power battery industry led by lithium-ion battery (LIB) development. Due to a variety of factors, LIBs have been widely used, but user abuse and battery quality issues have led to explosion accidents that have caused loss of life and property. Current strategies to address battery safety concerns mainly involve enhancing the intrinsic safety of batteries and strengthening safety controls with approaches such as early warning systems to alert users before thermal runaway and ensure user safety. In this paper, we discuss the current research status and trends in two areas, intrinsic battery safety risk control and early warning methods, with the goal of promoting the development of safe LIB solutions in new energy applications.
{"title":"Intrinsic Safety Risk Control and Early Warning Methods for Lithium-Ion Power Batteries","authors":"Yi Cui, Xueling Shen, Hang Zhang, Yanping Yin, Zhanglong Yu, Dong Shi, Yanyan Fang, Ran Xu","doi":"10.3390/batteries10020062","DOIUrl":"https://doi.org/10.3390/batteries10020062","url":null,"abstract":"Since 2014, the electric vehicle industry in China has flourished and has been accompanied by rapid growth in the power battery industry led by lithium-ion battery (LIB) development. Due to a variety of factors, LIBs have been widely used, but user abuse and battery quality issues have led to explosion accidents that have caused loss of life and property. Current strategies to address battery safety concerns mainly involve enhancing the intrinsic safety of batteries and strengthening safety controls with approaches such as early warning systems to alert users before thermal runaway and ensure user safety. In this paper, we discuss the current research status and trends in two areas, intrinsic battery safety risk control and early warning methods, with the goal of promoting the development of safe LIB solutions in new energy applications.","PeriodicalId":502356,"journal":{"name":"Batteries","volume":"283 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139835711","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}
Pub Date : 2024-02-15DOI: 10.3390/batteries10020062
Yi Cui, Xueling Shen, Hang Zhang, Yanping Yin, Zhanglong Yu, Dong Shi, Yanyan Fang, Ran Xu
Since 2014, the electric vehicle industry in China has flourished and has been accompanied by rapid growth in the power battery industry led by lithium-ion battery (LIB) development. Due to a variety of factors, LIBs have been widely used, but user abuse and battery quality issues have led to explosion accidents that have caused loss of life and property. Current strategies to address battery safety concerns mainly involve enhancing the intrinsic safety of batteries and strengthening safety controls with approaches such as early warning systems to alert users before thermal runaway and ensure user safety. In this paper, we discuss the current research status and trends in two areas, intrinsic battery safety risk control and early warning methods, with the goal of promoting the development of safe LIB solutions in new energy applications.
{"title":"Intrinsic Safety Risk Control and Early Warning Methods for Lithium-Ion Power Batteries","authors":"Yi Cui, Xueling Shen, Hang Zhang, Yanping Yin, Zhanglong Yu, Dong Shi, Yanyan Fang, Ran Xu","doi":"10.3390/batteries10020062","DOIUrl":"https://doi.org/10.3390/batteries10020062","url":null,"abstract":"Since 2014, the electric vehicle industry in China has flourished and has been accompanied by rapid growth in the power battery industry led by lithium-ion battery (LIB) development. Due to a variety of factors, LIBs have been widely used, but user abuse and battery quality issues have led to explosion accidents that have caused loss of life and property. Current strategies to address battery safety concerns mainly involve enhancing the intrinsic safety of batteries and strengthening safety controls with approaches such as early warning systems to alert users before thermal runaway and ensure user safety. In this paper, we discuss the current research status and trends in two areas, intrinsic battery safety risk control and early warning methods, with the goal of promoting the development of safe LIB solutions in new energy applications.","PeriodicalId":502356,"journal":{"name":"Batteries","volume":"23 8","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139776244","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}
Pub Date : 2024-02-15DOI: 10.3390/batteries10020060
David Geerts, R. Medina, W. V. van Sark, Steven Wilkins
Reducing greenhouse emissions can be done via the electrification of the transport industry. However, there are challenges related to the electrification such as the lifetime of vehicle batteries as well as limitations on the charging possibilities. To cope with some of these challenges, a charge scheduling method for fleets of electric vehicles is presented. Such a method assigns the charging moments (i.e., schedules) of fleets that have more vehicles than chargers. While doing the assignation, the method also maximizes the total Remaining Useful Life (RUL) of all the vehicle batteries. The method consists of two optimization algorithms. The first optimization algorithm determines charging profiles (i.e., charging current vs time) for individual vehicles. The second algorithm finds the charging schedule (i.e. the order in which vehicles are connected to a charger) that maximizes the RUL in the batteries of the entire fleet. To reduce the computational effort of predicting the battery RUL, the method uses a Machine Learning (ML) model. Such a model predicts the RUL of an individual battery while taking into account common stress factors and fabrication-related differences per battery. Simulation results show that charging a single vehicle as late as possible maximizes the RUL of that single vehicle, due to the lower battery degradation. Simulations also show that the ML model accurately predicts the RUL, while taking into account fabrication-related variability in the battery. Additionally, it was shown that this method schedules the charging moments of a fleet, leading to an increased total RUL of all the batteries in the vehicle fleet.
{"title":"Charge Scheduling of Electric Vehicle Fleets: Maximizing Battery Remaining Useful Life Using Machine Learning Models","authors":"David Geerts, R. Medina, W. V. van Sark, Steven Wilkins","doi":"10.3390/batteries10020060","DOIUrl":"https://doi.org/10.3390/batteries10020060","url":null,"abstract":"Reducing greenhouse emissions can be done via the electrification of the transport industry. However, there are challenges related to the electrification such as the lifetime of vehicle batteries as well as limitations on the charging possibilities. To cope with some of these challenges, a charge scheduling method for fleets of electric vehicles is presented. Such a method assigns the charging moments (i.e., schedules) of fleets that have more vehicles than chargers. While doing the assignation, the method also maximizes the total Remaining Useful Life (RUL) of all the vehicle batteries. The method consists of two optimization algorithms. The first optimization algorithm determines charging profiles (i.e., charging current vs time) for individual vehicles. The second algorithm finds the charging schedule (i.e. the order in which vehicles are connected to a charger) that maximizes the RUL in the batteries of the entire fleet. To reduce the computational effort of predicting the battery RUL, the method uses a Machine Learning (ML) model. Such a model predicts the RUL of an individual battery while taking into account common stress factors and fabrication-related differences per battery. Simulation results show that charging a single vehicle as late as possible maximizes the RUL of that single vehicle, due to the lower battery degradation. Simulations also show that the ML model accurately predicts the RUL, while taking into account fabrication-related variability in the battery. Additionally, it was shown that this method schedules the charging moments of a fleet, leading to an increased total RUL of all the batteries in the vehicle fleet.","PeriodicalId":502356,"journal":{"name":"Batteries","volume":"176 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139836049","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}
Pub Date : 2024-02-15DOI: 10.3390/batteries10020059
Jaehyun Bae, D. Hyun, Jaeyoung Han
With an increase in global warming, battery electric vehicles (BEVs), which are environmentally friendly, have been rapidly commercialized to replace conventional vehicles with internal combustion engines. Unlike traditional internal combustion engine vehicles, the powertrain system of BEVs operates with high efficiency, resulting in lower heat generation. This poses a challenge for cabin heating under low-temperature conditions. Conversely, under high-temperature conditions, the operating temperature of a high-voltage battery (HVB) is lower than the ambient air temperature, which makes cooling through ambient air challenging. To overcome these challenges, in this study, we proposed an integrated thermal management system (ITMS) based on a heat pump system capable of stable thermal management under diverse climatic conditions. Furthermore, to assess the ability of the proposed ITMS to perform thermal management under various climatic conditions, we integrated a detailed powertrain system model incorporating BEV specifications and the proposed ITMS model based on the heat pump system. The ITMS model was evaluated under high-load-driving conditions, specifically the HWFET scenario, demonstrating its capability to perform stable thermal management not only under high-temperature conditions, such as at 36 °C, but also under low-temperature conditions, such as at −10 °C, through the designated thermal management modes.
随着全球变暖的加剧，环保型电池电动汽车（BEV）已迅速商业化，以取代传统的内燃机汽车。与传统内燃机汽车不同，BEV 的动力总成系统运行效率高，发热量低。这给低温条件下的车厢加热带来了挑战。相反，在高温条件下，高压电池（HVB）的工作温度低于环境空气温度，这给通过环境空气降温带来了挑战。为了克服这些挑战，我们在本研究中提出了一种基于热泵系统的集成热管理系统（ITMS），该系统能够在不同气候条件下实现稳定的热管理。此外，为了评估所提出的 ITMS 在各种气候条件下执行热管理的能力，我们整合了一个包含 BEV 规格的详细动力总成系统模型和所提出的基于热泵系统的 ITMS 模型。在高负荷驱动条件下，特别是在 HWFET 情景下，对 ITMS 模型进行了评估，结果表明该模型不仅能够在 36 °C 等高温条件下执行稳定的热管理，而且还能在 -10 °C 等低温条件下通过指定的热管理模式执行稳定的热管理。
{"title":"Adaptive Integrated Thermal Management System for a Stable Driving Environment in Battery Electric Vehicles","authors":"Jaehyun Bae, D. Hyun, Jaeyoung Han","doi":"10.3390/batteries10020059","DOIUrl":"https://doi.org/10.3390/batteries10020059","url":null,"abstract":"With an increase in global warming, battery electric vehicles (BEVs), which are environmentally friendly, have been rapidly commercialized to replace conventional vehicles with internal combustion engines. Unlike traditional internal combustion engine vehicles, the powertrain system of BEVs operates with high efficiency, resulting in lower heat generation. This poses a challenge for cabin heating under low-temperature conditions. Conversely, under high-temperature conditions, the operating temperature of a high-voltage battery (HVB) is lower than the ambient air temperature, which makes cooling through ambient air challenging. To overcome these challenges, in this study, we proposed an integrated thermal management system (ITMS) based on a heat pump system capable of stable thermal management under diverse climatic conditions. Furthermore, to assess the ability of the proposed ITMS to perform thermal management under various climatic conditions, we integrated a detailed powertrain system model incorporating BEV specifications and the proposed ITMS model based on the heat pump system. The ITMS model was evaluated under high-load-driving conditions, specifically the HWFET scenario, demonstrating its capability to perform stable thermal management not only under high-temperature conditions, such as at 36 °C, but also under low-temperature conditions, such as at −10 °C, through the designated thermal management modes.","PeriodicalId":502356,"journal":{"name":"Batteries","volume":"67 13","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139775227","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：