为提高对动力电池的荷电状态(state of charge,SOC)估算精度、动力电池的健康状态(state of health,SOH)对锂电池性能的影响,提出一种扩展卡尔曼滤波(extended kalman filtering,EKF)联合估算算法.根据现有的实验数据,分析锂电池特性,构建二阶RC等效电路模型,并进行参数辨识,搭建MATLAB仿真平台联合EKF算法进行SOC估算,将仿真结果与真实数据进行对比,结果表明,EKF联合估算SOC比EKF估算SOC误差精度约高1.2％,且抗干扰能力更强.
Joint prediction of SOC and SOH of the Li-ion battery for a battery electric vehicles based on EKF algorithm
In order to improve the estimation accuracy of state of charge(SOC)and consider the influence of state of health(SOH)on the performance of lithium(Li)batteries,an extended kalman filter(EKF)joint estimation algorithm was proposed.According to the existing experimental data,the algorithm analyzes the characteristics of Li batteries,constructs a second-order RC equivalent circuit model,identifies parameters,and builds a MATLAB simulation platform and EKF algorithm for SOC estimation.After comparing the simulation results with the real data,the results show that the accuracy of the joint prediction SOC is about 1.2％higher than that of the EKF estimation of SOC,and the anti-interference ability is stronger.
EKF algorithmLi-ion batterystate of charge(SOC)state of health(SOH)prediction
万方数据
维普
