基于多尺度并行卡尔曼滤波算法的电池状态参数估算

Estimation of battery state parameters based on a multi-scale double extended Kalman filter algorithm

准确估算并合理利用电池的荷电状态(state of charge,SOC)与健康状态(state of health,SOH)可以延长电池的使用寿命。为了实现准确的SOC-SOH在线估计,在扩展卡尔曼滤波的基础上,采用多尺度并行扩展卡尔曼滤波估计算法(multi-scale double extended Kalman filter,MDEKF)提高估计精度。在建立电池2阶RC等效电路模型上,利用最小二乘法对模型参数进行辨识,设计并行结构的滤波器进行电池SOC估计和参数修正,并以电池组容量值作为表征量对SOH进行估算。仿真实验结果表明,SOC估计误差由1.43%降低到1.10%,SOH估计结果稳定在0.5%以内,验证了算法的快速收敛性和实时性。

Accurate estimation of state of charge(SOC)and state of health(SOH)of the battery and rational utilization can extend its life.Multi-scale double extended Kalman filter(MDEKF)algorithm was adopted based on extended Kalman filter algorithm to improve the accuracy of SOC-SOH online estimation.Firstly,2-RC equivalent circuit model was established.Secondly,the least square method was used to identify the model parameters.Then,a filter with double structures was designed to estimate the battery SOC and modify the parameters,and battery SOH was estimated with battery pack capacity as representation.The simulation results verify the fast convergence and real-time performance of the algorithm.The SOC estimation error decreases from 1.43%to 1.10%,and the SOH estimation result is stable within 0.5%.