Joint Estimation Of Lithium Battery SOC And SOH Based On MIAUKF Algorithm

The state of charge(SOC)estimation and the state of health(SOH)estimation of the lithium battery are the two most important functions in the battery management system,they can provide a reference for the allocation of the battery energy and the timely replacement of the battery.To increase the precision of the battery SOC and SOH estimation results,18650 lithium battery is used as the target of study,a united estimation method on the basis of RC equivalent circuit model(ECM)is raised,and the joint estimation method is verified under different test conditions.The major study contents of this paper are shown as blow:First,this paper compares some commonly used battery models,and finally chooses the second-order ECM through the comprehensive consideration of the model and the precision of the model.The OCV-SOC relationship curve was established by various fitting methods,and the ECM parameters of the battery were identified through the adaptive genetic algorithm(AGA).Using constant current discharge test data pulse discharge test data in the MATLAB simulation software to verify the parameters identification results and the precision of the ECM.Second,aiming at the problem of larger SOC estimation error caused by fixed noise covariance and single innovation vector,this paper puts forward a Multi Innovation Adaptive Unscented Kalman Filter(MIAUKF)algorithm to estimate the SOC of lithium battery on the basis of the UKF algorithm by adding adaptive filtering algorithm and multiinnovation identification theory.The comparison of UKF,AUKF and MIAUKF algorithms in MATLAB software using constant current discharge experimental data and DST cycle experimental data shows that MIAUKF algorithm improves the estimation precision of battery SOC.Third,aiming at the problem of the on-line estimation of battery SOH,a Variable Forgetting Factor Recursive Least Squares(VFFRLS)algorithm is proposed to estimate the current utmost usable volume of the battery,and the SOH of the battery is characterized by the ratio of the current utmost usable volume to the rated volume of the battery.Aiming at the problem that the current utmost usable volume of battery affects the precision of SOC estimation,a joint estimation method based on MIAUKF+VFFRLS is raised to achieve the alternate update of the two parameters of battery SOC estimation and current maximum available capacity.The joint estimation method was verified by constant current discharge experimental data and DST condition experimental data in MATLAB software,comparing the SOC estimation value of battery based on joint estimation method with that based on MIAUKF algorithm,the results show that the SOC estimation error based on joint estimation method is kept in the range of 1.24%,which is more accurate than that based on MIAUKF algorithm,at the same time,it can realize the real-time estimation of battery SOH.