基于大数据的电池特征参数提取和SOC估算方法  

Study on battery feature parameter extraction and SOC estimation method based on big data

作　　者：卢宇轩 李晟 林仕立 张先勇 LU Yuxuan;LI Sheng;LIN Shili;ZHANG Xianyong(Guangdong Polytechnic Normal University,Guangzhou,Guangdong Province,510665,China;Shuifa Singyes Energy(Zhuhai)Co.,Ltd.,Zhuhai,Guangdong Province,519085,China)

机构地区：[1]广东技术师范大学,广东广州510665 [2]水发兴业能源(珠海)有限公司,广东珠海519085

出　　处：《电池工业》2023年第6期295-300,315,共7页Chinese Battery Industry

基　　金：广东省自然科学基金项目(2020A1515010721);广东技术师范大学人才专项(2022SDKYA032)。

摘　　要：运维系统是保障电池储能电站安全、高效运行的重要支撑工具,电池荷电状态(state of charge,SOC)的精确估算是运维工具依赖的关键技术之一。针对储能电站应用领域的电池特性参数和电池SOC获取问题,使用基于大数据的电池特征参数提取方法,利用电池历史数据提取SOC、电压、电流等参数的对应关系,可对“开路电压法+安时积分法”估算方法的初始荷电状态SOC_(0)和实际可用容量Q_(a)进行修正。该方法可以有效提高运维工具对储能电站等应用领域中电池SOC的实时估算精度。Operation and maintenance(O&M)system is an important support tool to ensure the safe and efficient operation of energy storage plants,and the accurate estimation of the state of charge(SOC)of batteries is one of the key technologies that O&M tools rely on.For the problem of obtaining battery characteristic parameters and SOC of batteries in the application field of energy storage plants,we use the method of extracting battery characteristic parameters based on big data to extract the correspondence of SOC,voltage,current and other parameters by using the historical data of batteries,and then we can corrected the initial state of charge(SOC_(0))and actual usable capacity(Q_(a))by using the estimation method of“open-circuit voltage method+ampere hour method”.Q_(a) and SOC_(0) can be corrected.This method can effectively improve the accuracy of real-time estimation of battery SOC by operation and maintenance tools for energy storage plants and other applications.

关 键 词：荷电状态 历史数据 特征参数 

分 类 号：TM912[电气工程—电力电子与电力传动]