基于推广卡尔曼滤波算法的SOC估算策略

A strategy of estimating state of charge based on extended Kalman filter

为了估算锂离子动力电池的荷电状态,以Thevenin模型为基础,建立了数学关系简单,易于工程实现的状态空间模型。在此基础上,对模型进行了线性化处理,采用推广卡尔曼滤波算法实现了对电池荷电状态的估算。仿真结果表明,该模型能较好地体现电池的动静态特性,推广卡尔曼滤波算法在估算过程中能保持很好的精度,并对初始值的误差有很强的修正作用,对噪声有很强的抑制作用。

A strategy of estimating state of charge based on extended Kalman filter was presented. A state space model of a lithium ion battery derived from Thevenin model was proposed, which had the advantage of simplicity and could be easily implemented. The model was linearized in estimating state of charge, and extended Kalman filter was applied in the estimation. Matlab simulation and experiments were carried out. Comparison indicates that the model＇s performance accords well with that of lithium ion battery. The extended Kalman filter keeps an excellent precision in full ranae of the SOC, and performs well when initial error or disturbance happens.