基于Kalman滤波的镍氢动力电池SOC估算方法

Ni-MH battery state-of-charge estimation based on Kalman filter

动力电池的荷电状态(SOC,State-of-Charge)是电动车能量控制的重要参数,针对镍氢动力电池,建立了一种新的状态空间模型.电池模型采用荷电状态和极化状态作为状态向量,考虑持续充放电时电荷累积效应对电池电压的影响,对模型的状态方程进行了优化,增加了电荷累积项,以提高模型在变电流充放电过程中的精度.根据Kalman最优滤波理论,设计了电池荷电状态Kalman滤波递推算法,估算方法考虑了电池电压、电流和电池温度,给出了递推计算公式.根据恒流充电、恒流放电、脉冲充/放电、变电流充/放电实验的实验数据,对模型进行了仿真分析.结果表明,采用Kalman滤波估算方法有利于提高动力电池的荷电状态估算精度,适合应用在混合动力电动车中.

The state-of-charge（SOC） is an important parameter for the electrical vehicle. A new Ni-MH battery nonlinear dynamic model in discrete-time state-space form was introduced. In the new model, SOC and state-of-polarization as states of the state vector were introduced to represent the dynamic behavior of the battery more accurately. Taking into account the charge accumulating effect that influence the terminal voltage, the model was modified by adding an accumulating item into the state equation. The SOC estimation method based on extended Kalman filter was studied. The battery voltage, current, internal resistance and temperature were used in the algorithm. The calculation equations were presented in detail. Charge and discharge experiments with constant current, pulse current and variable currents were implemented. The simulation with these experiment data shows that the method is benefit to improve the accuracy of SOC estimation. The method can be used in a battery management system and applied in hybrid electrical vehicles.