Research On The State Of Health Estimation Of Power Batteries Based On Adaptive Unscented Kalman Filter

With the increasingly serious environmental pollution and energy crisis,lithium-ion batteries have become the main power source of new energy vehicles at present due to the advantages of high energy density,high power density,low self-discharge rate,stable voltage and wide range of working temperature range.In order to ensure the safety performance of electric vehicles and improve the driving range,it is important to optimize the energy management of electric vehicles,extend the cycle life of power batteries,and reduce the cost of power batteries.The state of charge(SOC)and state of health(SOH)of batteries are the core of battery management system(BMS),and how to estimate online and improve the estimation accuracy are still hot and difficult problems in current research.In view of the shortcomings of existing research,this article conducts a deeper study on the SOC and SOH estimation of batteries,and the main research content is as follows:Firstly,the working principle and performance index of lithium-ion batteries are introduced,and the aging mechanism of lithium-ion batteries is analyzed.On this basis,the influencing factors of battery SOH were further studied.Secondly,the existing commonly used battery models are compared and analyzed,and the second-order RC equivalent circuit model is selected as the research object after considering the calculation and accuracy.Based on the hybrid pulse power characteristic(HPPC)test,the parameters of the battery model are identified offline,and since the offline identification results change greatly with the state of battery and cannot be obtained in real time,a recursive least squares method with forgetting factor(FFRLS)is proposed to identify the model parameters online.Thirdly,based on the second-order RC equivalent circuit model,the Adaptive Extended Kalman Filter(AEKF)algorithm and the Adaptive Traceless Kalman Filter(AUKF)algorithm are used to estimate the battery SOC under simulated Federal Urban Driving Schedule(FUDS)based on the battery parameters obtained from online identification,and the errors of the two algorithms are compared.The experimental results show that the AUKF algorithm is more accurate and stable than the AEKF algorithm because it does not require linearisation of the state equations and does not suffer from truncation errors.Finally,on the basis of accurate identification of SOC,the AUKF algorithm is selected to estimate the ohmic internal resistance in real time,and the relationship between the battery ohmic internal resistance and SOH is used to realize the real-time estimation of SOH.Experiments show that the estimation error of battery SOH estimated by the AUKF algorithm is small,the estimation accuracy is high,and the real-time online estimation of SOH can be realized.