考虑松弛和滞回的锂离子电池建模及SOC估计

针对锂离子电池在电流状态突然变化时产生的松弛现象和滞回现象,在分析了电池等效电路模型的基础上,引入线性滤波器和滞回模块,建立了电池的自校正模型。通过恒流脉冲实验和动态应力工况测试验证自校正模型在对电池电压特性跟随的可靠性,并在该模型的基础上使用有限差分扩展卡尔曼滤波FDEKF(finite difference extended Kalman filter)算法实现了电池的荷电状态SOC(state of charge)估计。实验分析表明,自校正模型能较好地体现电池的动态特性,并使SOC估计保持很好的精度。

Modeling of Lithium-ion Battery Considering Relaxation and Hysteresis and State of Charge Estimation

When the current state of a lithium-ion battery changes suddenly, relaxation and hysteresis will appear. In this paper, based on the analysis of an equivalent circuit model of battery, a linear filter and a hysteresis module were introduced, and a self-tuning model of battery was established. Then, constant-current pulse tests and dynamic stress test(DST) were conducted to verify the reliability of the self-tuning model in following the voltage characteristics of the battery. On the basis of the proposed model, the finite difference extended Kalman filter(FDEKF) algorithm was used to estimate the state of charge(SOC) of battery. Experimental results showed that the self-tuning model can better reflect the dynamic characteristics of the battery while keeping the SOC estimation with high accuracy.