基于改进EKF算法的锂离子电池SOC估算方法

针对锂离子电池在变电流放电过程中荷电状态SOC(state of charge)估算精度的问题,提出了一种基于改进扩展卡尔曼滤波EKF(extended Kalman filter)算法的新估算方法。首先,通过放电实验和混合脉冲功率特性HPPC(hybrid pulsepower characteristic)实验,分析计算了等效电路模型参数;然后,利用该方法获得了该模型参数与放电倍率和SOC之间的关系,提出了一种估算SOC时在线修正开路电压和欧姆内阻的新原理和方法;最后,通过变电流放电的SOC估算结果,验证了该改进算法的可行性与有效性,从而解决了锂离子电池在复杂工况下估算精度不足的问题。

SOC Estimation Method for Lithium Ion Battery Based on Improved EKF Algorithm

A novel estimation method based on improved extended Kalman filter(EKF) algorithm was proposed, which aimed at the low accuracy problem when estimating the lithium ion battery'' state of charge(SOC) in the variable-current discharging process. First, though the discharging and hybrid pulse power characteristic(HPPC) experiments, the parameters of equivalent circuit model were analyzed and calculated. Then, the relationship among model parameters, discharge rate and SOC was obtained, and a novel principle and a novel method for estimating SOC were proposed, which were based on online correcting the open circuit voltage and ohmic resistance. Finally, the feasibility and effectiveness of the improved method were verified by the SOC estimation results in the variable-current discharging process. In this way, the proposed method can solve the low estimation accuracy problem of lithium ion battery under complex working conditions.