SOC estimation of lithium-ion batteries under multiple temperatures conditions based on MIARUKF algorithm
Accurate state of charge(SOC)estimation is the key to ensure the safe and stable operation of power batteries.However,in practical applications,the environment factors such as temperature change and noise interference make the accurate estimation of SOC difficult.In order to solve this problem,this paper proposes a joint estimation method of multi-timescales of the SOC of lithium ion batteries in wide temperature range based on the multi-new interest adaptive robust untrace Kalman filter(MIARUKF)algorithm,which integrates multi-new interest theory,adaptive filtering and robust algorithm based on the UKF algorithm.The proposed algorithm uses the multi-interest vector to correct the state estimates and timely update the noise covariance,so as to improve the estimation accuracy of SOC and improve the robustness of the algorithm by introducing the H filtering algorithm.Meanwhile,in order to reduce the computational burden of BMS,the UKF algorithm was used to estimate the model parameters online on the macroscopic time scale,and the MIARUKF algorithm was used to estimate the battery SOC on the microscopic time scale.Finally,the estimation results of battery SOC were compared and analyzed under different initial SOC initial values and temperature conditions,and the maximum and average absolute errors of the proposed method were 1.05%and 0.42%,respectively,indicating the high accuracy and good robustness.
lithium-ion batterystate of charge(SOC)multiple temperaturesMIARUKF
万方数据
维普
