城轨车用复合动力储能系统蓄电池SOC和SOH估计

Estimation of battery SOC and SOH for urban rail vehicle composite power energy storage system

以城市轨道交通复合储能系统蓄电池荷电状态(SOC)和健康状态(SOH)估算为目的,采用适合城市轨道交通车辆运行工况的二阶RC等效电路模型,并通过遗忘因子最小二乘算法(FFRLS)辨识其模型参数。基于二阶RC等效电路模型利用传统的自适应无迹卡尔曼滤波算法(AUKF)估计电池的荷电状态(SOC),由于列车复杂的运行环境,其电池受到的噪声是一个动态变化值,因此导致其估算结果误差较大,其最大误差达到5.5%。因此本文采用自适应无迹卡尔曼滤波算法实时估算蓄电池SOC,欧姆内阻及其容量,并根据欧姆内阻、容量与蓄电池SOH之间的函数关系,估算出电池的SOH。最后,通过设定的工况下对状态估计算法验证,经实验分析表明,相比UKF算法,AUKF算法能同时实时循环估算SOC和模型参数,根据观测值可以自动更新噪声,因而对于列车实际运行工况下其实用性更好,且精度较高,其最大误差为3.5%,均差为1.5%。

The purpose of this paper is to estimate the state of charge(SOC)and state of health(SOH)of the battery of the urban rail transit composite energy storage system.This paper adopted a second-order RC equivalent circuit model that meets the operating conditions of urban rail transit vehicles and identifies its model parameters by the forgetting factor least square algorithm.Based on the second-order RC equivalent circuit model,the traditional adaptive unscented Kalman filter algorithm was used to estimate the state of charge(SOC)of the battery.Due to the complex running environment of the train,the noise of the battery was a dynamic change value.The error of the estimation result is large,and the maximum error is 5.5%.Therefore,this paper used the adaptive unscented Kalman filter algorithm(AUKF)to estimate the state of charge(SOC),Ohmic internal resistance and capacity of the battery in real time.According to the relationship between the Ohmic internal resistance,capacity and battery SOH,the battery’s SOH was estimated.Finally,the experimental results show that compared with UKF algorithm,AUKF algorithm can estimate SOC and model parameters in real-time cycle at the same time.According to the observation value,it can update the noise automatically.Therefore,it has better practicability and higher accuracy for the actual operation of the train,with the maximum error of 3.5%and the average error of 1.5%.