基于神经网络与UKF结合的锂离子电池组SOC估算方法

Method for lithium-ion battery pack SOC estimation based on combination of neural network and UKF

锂离子电池组作为电动汽车的主要动力能源,对荷电状态的准确估计是电动汽车的关键技术之一。准确的SOC估计,对锂离子电池组的寿命维持及电动汽车的行车安全,具有十分重要的意义。基于此设计一种基于神经网络与无迹卡尔曼滤波器(UKF)相结合的SOC估算方法,既克服了UKF需要等效电池组电路模型的缺点,也能显著减小神经网络估算方法的最大误差。该实验数据来源于高级车辆仿真器(ADVISOR2002)基于实际工况的仿真结果,经实验数据证明,该方法具有有效性和实用性。

Lithium-ion battery pack is the main energy source of electric vehicles,and accurate estimation of lithium-ion battery state of charge(SOC)is one of the key technologies for electric vehicles. Accurate estimation of SOC has important significance for life maintain of lithium-ion battery pack and traffic safety of electric vehicles. Therefore,an SOC estimation method based on combination of neural network and unscented Kalman filter(UKF)is designed. The method not only overcomes the shortage that UKF needs circuit model of equivalent battery pack,but also significantly reduces the maximum error of the neural network estimation method. The experimental data is from the simulation results of advanced vehicle simulator ADVISOR2002 based on actual working condition. The experimental data show that the method is effective and practical.