VCM模型下的IBAS-EKF锂电池荷电状态估计

IBAS-EKF Estimation of Lithium Battery State of Charge under VCM

为解决锂电池荷电状态(SoC)难以精确估计的问题,提出了极化电压修正模型(VCM)和改进天牛须优化扩展Kalman滤波算法(IBAS-EKF)共同实现电池SoC的精确估计。在建立3阶RC电池模型和参数辨识的基础上,使用Elman循环神经网络对模型极化电压实现在线修正和优化,形成VCM模型;采用改进天牛须搜索算法优化扩展Kalman滤波算法的系统噪声协方差矩阵和量测噪声协方差矩阵,形成IBAS-EKF锂电池SoC估计算法。在测试平台上进行城市道路循环工况试验,结果表明:基于VCM模型的IBAS-EKF锂电池SoC估计算法的各项误差指标均低于传统的SoC估计算法,估计误差在0.6%以内,效果满足实际工程要求。

To solve the problem that lithium battery state of charge(SoC)is difficult to be estimated,put forward the polarization voltage correction model(VCM)and improved beetle antennae search optimized extended Kalman filtering algorithm(IBAS-EKF)can realize battery SoC accurate estimates.Based on the third-order RC battery model and parameter identification,Elman recurrent neural network is used to realize on-line correction and optimization of model polarization voltage to form VCM model.The system noise covariance matrix and measurement noise covariance matrix of the improved beetle antennae search optimized extended Kalman filtering algorithm is used to form the IBAS-EKF lithium battery SoC estimation algorithm.Working urban dynamometer driving schedule test on the test platform,the results show that each error index of IBAS-EKF lithium battery SoC estimation algorithm based on VCM model is lower than the traditional SoC estimation algorithm,and the estimation error is within 0.6%,which meets the requirements of practical engineering.