基于均值模型的电动叉车电池组荷电状态估计

SOC Estimation of Electric Forklift Power Battery Pack Based on Mean Value Model

针对电动叉车的动力电池组荷电状态(SOC)的实时估计,考虑到电池存在的差异性,采用电池选型外部滤波过程,建立了二阶RC等效电路模型与电池组均值模型,并提出了FFRLS_EKF联合算法。运用带遗忘因子的最小二乘法(FFRLS)进行模型参数辨识,结合扩展卡尔曼滤波算法实现SOC估计。通过试验测试与MATLAB计算验证,结果表明开路电压(OCV)模型估计值与实际值的误差均值约为0.02V,动力电池组SOC值与各单体电池均值的误差小于2%。该方法应用于电池组,可实现电池组内各单体电池的最大可用容量和荷电状态一致性估计。

Aiming at the real-time estimation of the state of charge(SOC)about the power battery pack of the electric forklift,considering the difference of the battery,the second-order RC equivalent circuit model and the battery mean value model were established by using the external filter process of battery selection,and the FFRLS_EKF joint algorithm was proposed.The least square method with forgetting factor(FFRLS)was used to identify the model parameters,and the extended Kalman filter algorithm was used to realize the SOC estimation.Through experimental tests and MATLAB calculation verification,the results showed that the average error between the estimated value and the actual value of the open circuit voltage(OCV)model was about 0.02 V,and the error between the SOC value of the power battery pack and the average value of each single cell was less than 2%.The method was applied to a battery pack,and could realize the estimation of the maximum usable capacity and state-of-charge consistency of individual cells in the battery pack.