{"title":"基于物理的电池SOC评估方法:最新进展和未来展望","authors":"Longxing Wu , Zhiqiang Lyu , Zebo Huang , Chao Zhang , Changyin Wei","doi":"10.1016/j.jechem.2023.09.045","DOIUrl":null,"url":null,"abstract":"<div><p>The reliable prediction of state of charge (SOC) is one of the vital functions of advanced battery management system (BMS), which has great significance towards safe operation of electric vehicles. By far, the empirical model-based and data-driven-based SOC estimation methods of lithium-ion batteries have been comprehensively discussed and reviewed in various literatures. However, few reviews involving SOC estimation focused on electrochemical mechanism, which gives physical explanations to SOC and becomes most attractive candidate for advanced BMS. For this reason, this paper comprehensively surveys on physics-based SOC algorithms applied in advanced BMS. First, the research progresses of physical SOC estimation methods for lithium-ion batteries are thoroughly discussed and corresponding evaluation criteria are carefully elaborated. Second, future perspectives of the current researches on physics-based battery SOC estimation are presented. The insights stated in this paper are expected to catalyze the development and application of the physics-based advanced BMS algorithms.</p></div>","PeriodicalId":14,"journal":{"name":"ACS Combinatorial Science","volume":null,"pages":null},"PeriodicalIF":3.7840,"publicationDate":"2023-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"Physics-based battery SOC estimation methods: Recent advances and future perspectives\",\"authors\":\"Longxing Wu , Zhiqiang Lyu , Zebo Huang , Chao Zhang , Changyin Wei\",\"doi\":\"10.1016/j.jechem.2023.09.045\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>The reliable prediction of state of charge (SOC) is one of the vital functions of advanced battery management system (BMS), which has great significance towards safe operation of electric vehicles. By far, the empirical model-based and data-driven-based SOC estimation methods of lithium-ion batteries have been comprehensively discussed and reviewed in various literatures. However, few reviews involving SOC estimation focused on electrochemical mechanism, which gives physical explanations to SOC and becomes most attractive candidate for advanced BMS. For this reason, this paper comprehensively surveys on physics-based SOC algorithms applied in advanced BMS. First, the research progresses of physical SOC estimation methods for lithium-ion batteries are thoroughly discussed and corresponding evaluation criteria are carefully elaborated. Second, future perspectives of the current researches on physics-based battery SOC estimation are presented. The insights stated in this paper are expected to catalyze the development and application of the physics-based advanced BMS algorithms.</p></div>\",\"PeriodicalId\":14,\"journal\":{\"name\":\"ACS Combinatorial Science\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":3.7840,\"publicationDate\":\"2023-10-13\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"ACS Combinatorial Science\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S209549562300565X\",\"RegionNum\":3,\"RegionCategory\":\"化学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"Chemistry\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"ACS Combinatorial Science","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S209549562300565X","RegionNum":3,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"Chemistry","Score":null,"Total":0}
Physics-based battery SOC estimation methods: Recent advances and future perspectives
The reliable prediction of state of charge (SOC) is one of the vital functions of advanced battery management system (BMS), which has great significance towards safe operation of electric vehicles. By far, the empirical model-based and data-driven-based SOC estimation methods of lithium-ion batteries have been comprehensively discussed and reviewed in various literatures. However, few reviews involving SOC estimation focused on electrochemical mechanism, which gives physical explanations to SOC and becomes most attractive candidate for advanced BMS. For this reason, this paper comprehensively surveys on physics-based SOC algorithms applied in advanced BMS. First, the research progresses of physical SOC estimation methods for lithium-ion batteries are thoroughly discussed and corresponding evaluation criteria are carefully elaborated. Second, future perspectives of the current researches on physics-based battery SOC estimation are presented. The insights stated in this paper are expected to catalyze the development and application of the physics-based advanced BMS algorithms.
期刊介绍:
The Journal of Combinatorial Chemistry has been relaunched as ACS Combinatorial Science under the leadership of new Editor-in-Chief M.G. Finn of The Scripps Research Institute. The journal features an expanded scope and will build upon the legacy of the Journal of Combinatorial Chemistry, a highly cited leader in the field.