考虑电池特性的多模型Kalman滤波SOC估计

Multi-model adaptive Kalman filtering SOC estimation considering battery characteristics

为了准确捕获电池特性以及精确估计荷电状态(SOC),设计了一种多模型自适应Kalman滤波算法估计电池的SOC。该方法通过改进的混合脉冲功率特性(HPPC)试验获取电池的多种特性,建立了两个动态模型分别描述电池的温度特性和倍率特性,并运用基于条件概率的融合算法将电池内部不同的动态特性信息相结合,结果表明:在动态应力测试工况下平均估计误差小于1%,该多模型更好地解释了电池在复杂环境下的非线性特征,使SOC估算在整个充放电区间和较为复杂的使用条件下均保持较高的精度,提高了SOC估计的准确性和鲁棒性。

A multi-model adaptive Kalman filter algorithm was designed to capture battery characteristics and estimate state of charge（SOC） accurately. This method obtained a variety of characteristics of the battery through an improved hybrid pulse power characteristic（HPPC） test, and established two dynamic models to describe the temperature characteristics and the rate characteristics of the battery respectively, and used a fusion algorithm based on conditional probability to combine different dynamic characteristics information within the battery. The results show that the average estimation error is less than 1% under dynamic stress testing conditions. The multi-model filter estimation method integrates the different dynamic characteristics of the battery, which can better explain the nonlinear characteristics of the battery in complex environment, maintaining high accuracy over the entire charge-discharge interval and the more complex usage conditions, also improving the accuracy and robustness of the SOC estimation.