锂电池SOC和SOH的自适应联合在线估算方法

AN ADAPTIVE JOINT ONLINE ESTIMATION METHOD OF LITHIUM BATTERY SOC AND SOH

为了准确和快速地估算电动汽车运行过程中汽车电池的荷电状态(State of Charge,SOC)和健康状态(State of Health,SOH),提出一种基于遗忘因子最小二乘和可变时间尺度扩展卡尔曼滤波器的自适应联合估算算法。为了提高算法的效率和准确度,引入自适应遗忘因子递归最小二乘(Adaptive Forgetting Factor Recursive Least Square,AFFRLS)方法来识别电池模型中的参数,并采用可变时间尺度扩展卡尔曼滤波器(Variable Time Scale Extended Kalman Filter,VEKF)来指示SOC和SOH,以满足对电池动态状况进行在线快速估算的需求。应用动态应力测试(Dynamic Stress Test,DST)数据库验证了该方法的有效性,实验结果表明,该联合估算方法可以获取准确的电池模型,并实现在线状态估算。

To estimate the battery state of charge(SOC) and state of health(SOH) in an accurate and fast manner during the operation of a vehicle,a novel adaptive joint estimation algorithm based on forgetting factor least squares and extended Kalman filter with variable time scale is proposed.To improve the efficiency and accuracy of the algorithm,an adaptive forgetting factor recursive least square(AFFRLS) method was introduced to identify the parameters in the battery model.To fulfill the demand of rapid online estimation for the dynamic situation of the battery,a variable time scale extended Kalman filter(VEKF) was used to indicate the SOC and SOH.The dynamic stress test(DST) conditions database was applied to verify the effectiveness and efficiency of the proposed method.The results demonstrate that the proposed method can acquire an accurate model of the battery and provide a real-time state estimation.