基于ABP-EKF算法的锂电池SOC估计

Lithium Battery SOC Estimation Based on ABP-EKF Algorithm

电池荷电状态(state of charge,SOC)的准确估计是电动汽车合理实施电池管理的前提条件和重要依据。针对目前电动汽车对动力电池SOC估计精度的不断提高这一问题,利用联合估计法对锂电池SOC进行研究。基于Thevenin电池模型与修正的安时积分算法,推导出了锂电池的输出方程以及状态空间模型,通过采集实验过程中的相关数据并应用递推最小二乘法对电池模型参数作出辨识。分析了扩展卡尔曼滤波(EKF)算法以及自适应BP神经网络算法的原理,联合两种算法并在此基础上提出了自适应BP-EKF算法(ABP-EKF)。运用所提出的算法对锂离子电池SOC进行联合估计,最后通过对比ABP-EKF与EKF两种算法估计锂电池SOC的数据,研究结果表明:所提出ABP-EKF算法相比于EKF算法在均值误差项与均方根误差项分别减少了3.9%和3.79%。

Accurate estimation of battery SOC is a prerequisite and important basis for the rational implementation of battery management in electric vehicles.Aiming at the problem that the estimation accuracy of power battery SOC for electric vehicles is improving continuously,the joint estimation method was used to study the SOC of lithium battery.Based on the Thevenin battery model and the modified ampere-hour integration algorithm,the output equation and state space model of the lithium battery were derived.The parameters of the battery model were identified by collecting relevant data during the experiment and applying the recursive least square method.The principles of the extended Kalman filter(EKF)algorithm and the adaptive BP neural network algorithm were analyzed,and two algorithms were combined.On this basis,the self-adaptive BP-EKF algorithm(ABP-EKF)was proposed.The proposed algorithm was used to jointly estimate the lithium-ion battery SOC.Finally,the data of the lithium battery SOC was estimated by comparing the two algorithms of ABP-EKF and EKF.The research results show that compared with the EKF algorithm,the proposed ABP-EKF algorithm reduces the mean error term and root mean square error term by 3.9%and 3.79%,respectively.