分数阶扩展卡尔曼滤波算法的锂电池SOC估算

SOC estimation of lithium-ion battery by Extended Kalman Filtering algorithm based on fractional order battery model

由于锂离子电池的SOC(state of charge)不能直接被测得,目前只能通过电池外部输出特性对其进行估算。以磷酸铁锂电池为研究对象,考虑到电池各种复杂的非线性特征,分析了电池的电化学阻抗特性,采用恒相位元件(CPE)对传统的等效电路模型进行改进,建立了分数阶(fractional order)等效电路模型;联合遗传算法和混合脉冲动力试验对分数阶等效电路模型的参数进行离线识别;基于扩展卡尔曼滤波算法,建立了分数阶扩展卡尔曼滤波算法(fractional order extended Kalman Filter)的锂电池SOC估算模型;根据动态应力试验DST(dynamic stress test)工况设计制定了锂电池充放电方案,在环境温度25℃条件下,实时采集电池电流及电压数据,将采集所得数据输入到Matlab建立的模型中,对目标电池进行SOC估算。仿真结果表明:与二阶戴维南电路模型SOC仿真结果相比,基于FEKF算法的SOC估算结果具有更高的精度且波动性更小,误差均小于0.72%,均方根误差仅为0.24%。

Since the SOC(state of charge)of a lithium-ion battery cannot be directly measured,and can only be estimated by the external output characteristics of the battery.Taking lithium-ion phosphate lithium battery as the research object and considering the various complex nonlinear characteristics of the battery,the electrochemical impedance characteristics of the battery were analyzed.To improve the traditional equivalent circuit model,the fractional order equivalent circuit model was established using the constant phase element(CPE)combined Genetic Algorithm and hybrid impulse dynamic test to identify the parameters of the fractional equivalent circuit model offline;Based on the Extended Kalman Filter algorithm,the lithium battery SOC estimation model was built by a Fractional Order Extended Kalman Filter(FEKF)algorithm;According to the DST(dynamic stress test)conditions,charging and discharging plan for the lithium battery were designed,and the battery current and voltage data were collected in real time at an ambient temperature of 25℃,which was input into the model established by Matlab to estimate the SOC of the battery.Compared with the simulated SOC of the traditional second-order Thevenin circuit model,the SOC estimated based on the FEKF algorithm has higher accuracy and smaller volatility,and the error is less than 0.72%and the RMSE is only 0.24%.