{"title":"用于电荷状态估计的锂离子电池状态相关准线性参数变化模型","authors":"Yaoke Sun , Xiaoyong Zeng , Xiangyang Xia , Laien Chen","doi":"10.1016/j.jpowsour.2024.234879","DOIUrl":null,"url":null,"abstract":"<div><p>Accurate estimation of state of charge (SOC) forms the foundation of battery management systems. Although commonly used for SOC estimation, equivalent circuit models (ECMs) inadequately capture battery nonlinear dynamics and rely on SOC-open circuit voltage curves. To overcome these limitations, this paper introduces state-dependent mechanisms into ECMs, proposing a state-dependent quasi-linear parameter-varying model (SD-QLPVM). This model incorporates a quasi-linear model derived from ECMs. Crucially, it eschews traditional approaches of parameter determination through offline experiments or online adaptive methods, which are limited by their linear nature. Conversely, the parameters of the quasi-linear model are treated as time-varying and state-dependent functional parameters, calculated using radial basis function neural networks (RBF-NNs). Subsequently, state variables, such as terminal voltage, current, SOC, and temperature, are used to characterize the operation point of LIBs. By considering state variables as the inputs to the RBF-NNs, the proposed parameter determination approach enables the quasi-linear model to dynamically adjust its parameters in response to evolving battery operation points, representing battery dynamics accurately and responsively. Finally, an online SOC estimation method is developed based on the SD-QLPVM and a particle filter. The effectiveness of the proposed model and SOC estimation method is verified across various drive cycles and temperature conditions.</p></div>","PeriodicalId":377,"journal":{"name":"Journal of Power Sources","volume":null,"pages":null},"PeriodicalIF":8.1000,"publicationDate":"2024-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"A state-dependent quasi-linear parameter-varying model of lithium-ion batteries for state of charge estimation\",\"authors\":\"Yaoke Sun , Xiaoyong Zeng , Xiangyang Xia , Laien Chen\",\"doi\":\"10.1016/j.jpowsour.2024.234879\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>Accurate estimation of state of charge (SOC) forms the foundation of battery management systems. Although commonly used for SOC estimation, equivalent circuit models (ECMs) inadequately capture battery nonlinear dynamics and rely on SOC-open circuit voltage curves. To overcome these limitations, this paper introduces state-dependent mechanisms into ECMs, proposing a state-dependent quasi-linear parameter-varying model (SD-QLPVM). This model incorporates a quasi-linear model derived from ECMs. Crucially, it eschews traditional approaches of parameter determination through offline experiments or online adaptive methods, which are limited by their linear nature. Conversely, the parameters of the quasi-linear model are treated as time-varying and state-dependent functional parameters, calculated using radial basis function neural networks (RBF-NNs). Subsequently, state variables, such as terminal voltage, current, SOC, and temperature, are used to characterize the operation point of LIBs. By considering state variables as the inputs to the RBF-NNs, the proposed parameter determination approach enables the quasi-linear model to dynamically adjust its parameters in response to evolving battery operation points, representing battery dynamics accurately and responsively. Finally, an online SOC estimation method is developed based on the SD-QLPVM and a particle filter. The effectiveness of the proposed model and SOC estimation method is verified across various drive cycles and temperature conditions.</p></div>\",\"PeriodicalId\":377,\"journal\":{\"name\":\"Journal of Power Sources\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":8.1000,\"publicationDate\":\"2024-06-28\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Power Sources\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0378775324008310\",\"RegionNum\":2,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"CHEMISTRY, PHYSICAL\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Power Sources","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0378775324008310","RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"CHEMISTRY, PHYSICAL","Score":null,"Total":0}
A state-dependent quasi-linear parameter-varying model of lithium-ion batteries for state of charge estimation
Accurate estimation of state of charge (SOC) forms the foundation of battery management systems. Although commonly used for SOC estimation, equivalent circuit models (ECMs) inadequately capture battery nonlinear dynamics and rely on SOC-open circuit voltage curves. To overcome these limitations, this paper introduces state-dependent mechanisms into ECMs, proposing a state-dependent quasi-linear parameter-varying model (SD-QLPVM). This model incorporates a quasi-linear model derived from ECMs. Crucially, it eschews traditional approaches of parameter determination through offline experiments or online adaptive methods, which are limited by their linear nature. Conversely, the parameters of the quasi-linear model are treated as time-varying and state-dependent functional parameters, calculated using radial basis function neural networks (RBF-NNs). Subsequently, state variables, such as terminal voltage, current, SOC, and temperature, are used to characterize the operation point of LIBs. By considering state variables as the inputs to the RBF-NNs, the proposed parameter determination approach enables the quasi-linear model to dynamically adjust its parameters in response to evolving battery operation points, representing battery dynamics accurately and responsively. Finally, an online SOC estimation method is developed based on the SD-QLPVM and a particle filter. The effectiveness of the proposed model and SOC estimation method is verified across various drive cycles and temperature conditions.
期刊介绍:
The Journal of Power Sources is a publication catering to researchers and technologists interested in various aspects of the science, technology, and applications of electrochemical power sources. It covers original research and reviews on primary and secondary batteries, fuel cells, supercapacitors, and photo-electrochemical cells.
Topics considered include the research, development and applications of nanomaterials and novel componentry for these devices. Examples of applications of these electrochemical power sources include:
• Portable electronics
• Electric and Hybrid Electric Vehicles
• Uninterruptible Power Supply (UPS) systems
• Storage of renewable energy
• Satellites and deep space probes
• Boats and ships, drones and aircrafts
• Wearable energy storage systems