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A study of SOC estimation algorithm for energy storage Lithium battery pack based on information fusion technology

机译:基于信息融合技术的储能锂电池组SOC估计算法研究

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According to the practical engineering problems of battery energy storage system (BESS), the precision and robust of state of charge(SOC) estimation is becoming increasingly important. The battery pack capacity, operation condition, cycle times, environment temperature, charge and discharge rate has an important relationship, this will affect the accurate of SOC estimation. An SOC estimation algorithm based on information fusion framework and a multi-pattern switch strategy based on the information fusion technology are proposed for Lithium-ion battery. The different operation areas are created on the OCV-SOC curve, i.e. flat voltage area, identification-correction area and over charge/discharge area. In order to utilizing the estimation model according to the different operation areas, the algorithm switches to the optimized estimation model to acquire better performance. Then eliminates the model error which introduced by working state. Finally, by utilizing the actual operation data, experiments and numerical table were conducted, show that this SOC estimation algorithm based on information fusion technology has better robust performance of the practical application of lithium battery energy storage system.
机译:根据电池储能系统(BESS)的实际工程问题,充电状态(SOC)估计的精确度和鲁棒性变得越来越重要。电池组的容量,工作条件,循环时间,环境温度,充放电速率具有重要的关系,这将影响SOC估算的准确性。提出了一种基于信息融合框架的SOC估计算法和一种基于信息融合技术的多模式切换策略。在OCV-SOC曲线上创建不同的操作区域,即平坦电压区域,识别校正区域和过充/放电区域。为了根据不同的操作区域利用估计模型,该算法切换到优化的估计模型以获得更好的性能。从而消除了工作状态引起的模型误差。最后,利用实际的运行数据,进行了实验和数值表分析,表明该基于信息融合技术的SOC估计算法具有较好的鲁棒性能,可用于锂电池储能系统的实际应用。

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