基于分数阶模型的锂离子电池SOC与SOH协同估计

Collaborative estimation of SOC and SOH of Li-ion battery based on fractional order model

锂离子电池荷电状态(SOC)和健康状态(SOH)的精确估计对电动汽车稳定运行十分重要。以精确估计电池SOC和SOH为目标,提出了一种基于分数阶模型的协同估计算法。建立基于二阶RC电路模型的分数阶电池模型,采用自适应遗传算法(AGA)辨识模型参数,利用分数阶扩展卡尔曼滤波(FOEKF)算法估计SOC,并结合自适应无迹卡尔曼滤波(AUKF)算法估计SOH,迭代更新内阻与SOC进而实现SOC与SOH精确的协同估计。在城市道路循环工况(UDDS)下使用Matlab工具验证和对比了算法精度,平均误差均控制在2%以内。结果表明,该协同估计算法能够精确估计电池SOC和SOH,为电池状态估计提供了一种方法。

The accurate estimation of the state of charge(SOC)and state of health(SOH)of Li-ion batteries is important for the stable operation of electric vehicles.A collaborative estimation algorithm based on fractional order model was proposed to estimate accurately SOC and SOH.A fractional order model based on the second-order RC circuit model was established,and the model parameters were identified by adaptive genetic algorithm(AGA).The SOC was estimated by the fractional order extended Kalman filter(FOEKF)algorithm,the SOH was estimated by adaptive unscented Kalman filtering(AUKF)algorithm,and the internal resistance and SOC were iteratively updated to achieve accurate estimation of SOC and SOH.The accuracy of the algorithm was verified by using Matlab tool under the urban dynamometer driving schedule(UDDS).The error is less than 2%.The results show that the collaborative estimation algorithm can accurately estimate battery SOC and SOH,providing a method for battery state estimation.