{"title":"利用扩展卡尔曼滤波确定电池管理系统的荷电状态","authors":"Namrata Padalale, M. Sindhu","doi":"10.1109/TENSYMP55890.2023.10223676","DOIUrl":null,"url":null,"abstract":"The lithium-ion battery is an integral part of electric vehicles. Electric vehicles (EVs) heavily rely on battery technology, with lithium-ion batteries being the most popular for their superior performance in the automotive industry. Accurate SoC determination is vital for maximizing the utilization of EVs and optimizing energy storage in renewable systems. By using the EKF to estimate SoC, BMS can ensure efficient charging and discharging, thereby improving the overall energy management and reducing carbon emissions. This paper proposes an enhanced extended kalman filter based SoC estimation on first-order-RC equivalent circuit model (ECM) and validated with an accuracy of 99%. MATLAB/Simulink platform has been used and the results of the enhanced extended Kalman filters are verified using dSPACE 1104.","PeriodicalId":314726,"journal":{"name":"2023 IEEE Region 10 Symposium (TENSYMP)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2023-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"State of Charge (SoC) Determination Through Extended Kalman Filter in Battery Management Systems\",\"authors\":\"Namrata Padalale, M. Sindhu\",\"doi\":\"10.1109/TENSYMP55890.2023.10223676\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The lithium-ion battery is an integral part of electric vehicles. Electric vehicles (EVs) heavily rely on battery technology, with lithium-ion batteries being the most popular for their superior performance in the automotive industry. Accurate SoC determination is vital for maximizing the utilization of EVs and optimizing energy storage in renewable systems. By using the EKF to estimate SoC, BMS can ensure efficient charging and discharging, thereby improving the overall energy management and reducing carbon emissions. This paper proposes an enhanced extended kalman filter based SoC estimation on first-order-RC equivalent circuit model (ECM) and validated with an accuracy of 99%. MATLAB/Simulink platform has been used and the results of the enhanced extended Kalman filters are verified using dSPACE 1104.\",\"PeriodicalId\":314726,\"journal\":{\"name\":\"2023 IEEE Region 10 Symposium (TENSYMP)\",\"volume\":\"1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2023-09-06\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2023 IEEE Region 10 Symposium (TENSYMP)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/TENSYMP55890.2023.10223676\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2023 IEEE Region 10 Symposium (TENSYMP)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/TENSYMP55890.2023.10223676","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
State of Charge (SoC) Determination Through Extended Kalman Filter in Battery Management Systems
The lithium-ion battery is an integral part of electric vehicles. Electric vehicles (EVs) heavily rely on battery technology, with lithium-ion batteries being the most popular for their superior performance in the automotive industry. Accurate SoC determination is vital for maximizing the utilization of EVs and optimizing energy storage in renewable systems. By using the EKF to estimate SoC, BMS can ensure efficient charging and discharging, thereby improving the overall energy management and reducing carbon emissions. This paper proposes an enhanced extended kalman filter based SoC estimation on first-order-RC equivalent circuit model (ECM) and validated with an accuracy of 99%. MATLAB/Simulink platform has been used and the results of the enhanced extended Kalman filters are verified using dSPACE 1104.
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