基于 SHEKF-GPM 融合的锂电池 SOC 估算

荷电状态（SOC）是电池控制策略和管理系统的重要参数。针对积分法和电压法估算锂电池 SOC 时不能减少误差累积现象，提出一种基于平方根高阶扩展卡尔曼滤波（SHEKF）与灰色预测模型（GPM） 融合的算法，用于估算锂电池 SOC。该方法结合遗忘因子递推最小二乘法（FFRLS）和二阶 RC 等效电路模 型实时在线辨识和修改锂电池模型参数，结合 SHEKF-GPM 融合模型进行锂电池 SOC 状态方程的线性部分 和非线性部分估算。通过仿真分析，得到 SHEKF-GPM 融合算法估算 SOC 时的误差低于 0.3%，协方差误 差为 0% 左右，不会产生误差累积。仿真结果表明，该方法能减少误差累积，提高电池管理系统估算锂电池 SOC 时的实用性、有效性和估算精度。

State of Charge Estimation of Lithium Battery Based on SHEKF-GPM Fusion

State of charge (SOC) is an essential parameter of the battery control strategy and management system. In view of the fact that the error accumulation can not be reduced by using the integration method and the voltage method in SOC estimation of lithium battery, an algorithm has thus been proposed based on the fusion of square-root high-degree extended Kalman filter (SHEKF) and grep prediction model (GPM) to estimate SOC of lithium battery. The proposed method combines the forgetting factor recursive least square (FFRLS) method with the second-order RC equivalent circuit model to identify and modify the parameters of the lithium battery model in real time. Combined with SHEKF-GPM fusion model, an estimation had been made of the linear and nonlinear part of SOC state equation of lithium battery. The simulation results show that the error of SHEKF-GPM fusion algorithm in SOC estimation is less than 0.3%, with the covariance error being about 0%. The simulation results show that the method can reduce the error accumulation and improve the practicability, effectiveness and accuracy of battery management system in SOC estimation of lithium batteries.