基于双Kalman滤波的并联锂电池组循环寿命估计

Cycle Life Estimation Method for Parallel Lithium Battery Pack Based on Double Kalman Filtering Algorithm

摘要: 以磷酸铁锂电池为研究对象，建立锂电池组并联电路等效模型，分析电路模型参数特征，在Kalman滤波算法应用研究的基础上，进行基于双Kalmam滤波（D-KF）的健康状况（SOH）估计方法和循环寿命预测方法研究.新方法利用抗差无迹卡尔曼滤波（R-UKF）估计单体电池模型参数和荷电状态（SOC），利用扩展卡尔曼滤波（EKF）估计电池组SOH和循环寿命.实验结果显示，新方法具有较高的估计精度和较好的鲁棒性，对于提高电池组的能量储存能力、利用率和循环寿命有着重要的应用价值.

Abstract: The lithium-iron phosphatebattery is studied in this paper. The equivalent circuit model is established and the characteristics of model parameters are analysed. The double Kalman filtering algorithm is presented to estimate the cycle life of battery pack, which is defined as State of Health (SOH). The efforts of our study are, firstly the parameters and state of charge(SOC) are estimated based on robust Unscented Kalman filter (R-UKF). Secondly, the SOH value is estimated using Extended Kalman filter (EKF) according to real-time value of the model parameters. The experimental results indicate that our new algorithm can obtain the estimation results more accurate. Besides, our studies have important application value for improving the energy storage capacity, utilization ratio and cycle life of the integrated battery.