Research On SOC And SOH Estimation Methods For Power Lithium-ion Battery

The State of Charge(SOC)and State of Health(SOH)of the power lithium-ion batteries are two important parameters for BMS system monitoring,the real-time and accurate estimation of SOC and SOH are of great significance for the batteries safety,rationality,efficiency use and the development of BMS technology.The traditional SOC and SOH estimation methods of power batteries exist these problems of low estimation accuracy and filter divergence,the theory focus on lithium-ion battery and based on the accurate battery model,the estimation algorithm of SOC and SOH are studied.The specific contents of this dissertation are as follows:Firstly,the research background and significance of the issue are introduced,according to the research contents of the issue,the research status of battery model,SOC estimation method and SOH estimation method at home and abroad are reviewed anslysis.Secondly,the working principle and performance characterization parameters of lithium-ion battery are introduced and analyzed,and aim is to establish the battery equivalent circuit model.Based on the test system platform of the high performance,the related characteristic experiments of lithium-ion battery are carried out.Based on the analysis of the experimental results,the thevenin equivalent circuit model of the battery is established and the corresponding simulation model is built on Simulink platform.Thirdly,based on the thevenin equivalent circuit model,the state space equation of the system is built and the on-line identification of battery model parameters is realized by using the Bias Compensation Recursive Least-Squares(BCRL S)method.The simulation model based on BCRLS algorithm is built on simulink platform,and the model accuracy was verified under different current conditions.Fourthly,on the basis of the battery simulation model based on BCRLS algorithm,the Extended Kalman Filter(EKF)and improved Adaptive Extend Kalman Filter(AEKF)are used to realize the estimation of battery SOC,and the SOC estimation model is built to carry out simulation verification.The simulation results under Pulse,HPPC and UDDS conditions show that the estimation accuracy of improved Adaptive Extend Kalman Filter(AEKF)for the battery SOC is higher than the Extended Kalman Filter(EKF)algorithm.The maximum SOC estimation error is less than 5% and the estimation accuracy is high.Finally,the EKF algorithm is used to realize the on-line estimation of battery SOH from the perspective of internal resistance,and the feasibility of the algorithm is verified by the actual for data.Considering the influence of on-line SOC value on SOH estimation,a dual Kalman filter algorithm is designed to realize the on-line joint estimation of battery SOC and SOH,and the effectiveness and accuracy of the dual Kalman filter algorithm are verified by simulation experiments.The simulation results show that the maximum estimation error of battery SOH is less than 3% under UDDS condition and the accuracy meets the design requirements of BMS system.