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SOC estimation of LiPB batteries using Extended Kalman Filter based on high accuracy electrical model

机译:基于高精度电模型的扩展卡尔曼滤波器SOC估计LIPB电池

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This paper proposes a SOC (State-of-charge) estimator of batteries using the Extended Kalman Filter (EKF). EKF can work properly only with accurate model. Therefore, this paper includes high accuracy electrical battery model for EKF state. The modeling is focused on high-capacity LiPB batteries. The battery model is extracted from single cell of LiPB 40Ah, 3.7V. The dynamic behavior of single cell battery is modeled using a bulk capacitor, two series RC networks and a series resistance. The voltage in bulk capacitor represents OCV (Open Circuit Voltage) related to SOC value. EKF is employed to estimate the OCV, and then OCV is converted to SOC value. EKF is tested with experiments data obtained by MACCOR battery tester. EKF is improved with single varying parameter of bulk capacitor which follows the SOC value. The test of estimator algorithm is done for full region of SOC. The test results show the error of estimation can reach max 5%SOC at some points.
机译:本文提出了使用扩展卡尔曼滤波器(EKF)的电池的SoC(充电状态)估算器。 EKF只能使用精确的模型正常工作。 因此,本文包括用于EKF状态的高精度电池模型。 建模集中在高容量的Lipb电池上。 电池模型从Lipb 40Ah的单个单元中提取,3.7V。 单电池电池的动态行为是使用散装电容,两个系列RC网络和串联电阻进行建模的。 散装电容中的电压表示与SOC值相关的OCV(开路电压)。 EKF用于估计OCV,然后OCV转换为SOC值。 通过Maccor电池测试仪获得的实验数据测试EKF。 通过散装电容器的单个变化参数改进了EKF,其沿SOC值跟随SOC值。 估计器算法的测试是为SOC的完整区域完成的。 测试结果显示估计误差可以在某些点到达最多5%SOC。

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