由MIEKPF-EKPF算法协同估计锂离子电池SOC与SOH

Collaborative estimation of SOC and SOH of Li-ion battery by MIEKPF-EKPF algorithm

针对锂离子电池荷电状态(SOC)和健康状态(SOH)难以直接测量的问题,提出基于多新息的扩展卡尔曼粒子滤波(MIEKPF)与扩展卡尔曼粒子滤波(EKPF)协同估计SOC和SOH。采用EKPF算法在线辨识参数,并估计SOH,将阻容等辨识结果作为输入,弥补估计SOC时应该考虑电池老化影响产生的误差,实现SOH对SOC的修正,提高模型精度。在新欧洲驾驶周期(NEDC)工况下,进行充放电实验,EKPF算法估计SOH的结果符合实际情况。MIEKPF-EKPF算法最终SOC估计的平均误差为0.48%、最大误差为1.97%、均方根误差为0.58%,仿真结果验证了所提方法的可行性和准确性。

Aiming at the problems that the state of charge(SOC)and state of health(SOH)of Li-ion battery were difficult to measure directly,the extended Kalman particle filter based on multiple innovations(MIEKPF)and the extended Kalman particle filter(EKPF)were proposed to estimate SOC and SOH in cooperation.EKPF algorithm was used to identify parameters online and estimate SOH,the identification results such as resistance and capacity were used as the input to compensate for the error that should be considered when estimating SOC due to the influence of battery aging,to achieve the correction of SOH to SOC and improve the accuracy of the model.The charge-discharge experiment was taken under the new European driving cycle(NEDC)operating conditions,the SOH estimation result of the EKPF algorithm was consistent with the actual condition.The average error of the final SOC estimated by MIEKPF-EKPF algorithm was 0.48%,the maximum error was 1.97%,the root mean square error was 0.58%.The simulation results verified the feasibility and accuracy of the proposed method.