Research On Joint Online Estimation Method Of SOC And SOH Of Power Lithium-ion Batteries

At present,the world is paying attention to the energy crisis and environmental pollution.As a relatively mature and advanced battery,lithium-ion batteries are widely used in all walks of life due to their advantages of pollution-free,light weight,and large power storage.Especially in the field of new energy vehicles,lithium-ion batteries can be said to be in short supply.As one of the main core components of pure electric vehicles,the battery management system(BMS)provides a guarantee for the safe driving of electric vehicles through effective control and management of the entire vehicle battery pack.However,the battery management technology is far from mature.How to realize the online estimation of the battery’s state of charge(SOC)and state of health(SOH)and improve the accuracy of the estimation during the driving of electric vehicles has always been a major difficulty in BMS research.This paper takes 18650 lithium battery as the research object.Based on the establishment of battery physical model and online identification of its parameters,a joint online estimation of battery SOC and SOH is realized by using dual Kalman filtering(DEKF).First of all,this article introduces the main internal structure,composition,working principle and main performance indicators of lithium-ion batteries in detail.Combined with the above properties,the corresponding experimental research is carried out on the basic characteristics of lithium-ion batteries such as open circuit voltage and terminal voltage.On this basis,the physical characteristics of the external impulse response of the battery are fully considered,and the secondorder RC equivalent circuit battery design model is adopted.By implementing the offline pulse discharge test,the parameter values of each circuit in the model can be initialized and identified.In order to fully adapt to the time-varying characteristics of the model parameters,an improved recursive least squares method is used to realize on-line identification of model parameters,which improves the efficiency of parameter identification.In addition,on the basis of analyzing the classic SOC estimation method,this paper establishes the state equation and observation equation under DEKF based on the model parameters identified online,and realizes the online estimation of the SOC.By simulating federal urban driving schedule(FUDS)and comparing the algorithm prediction data with the experimental truth data,the effectiveness and high accuracy of the method are verified.Finally,in terms of SOH estimation,this paper extracts the parameters of the above model that have strong correlation with SOH,uses the battery capacity as the estimation parameter vector,and substitutes it into the DEKF algorithm to realize the online estimation of SOH and solve the traditional The problem of low efficiency of SOH offline estimation method.Through experimental comparison,it is proved that this method can effectively predict SOH with high calculation accuracy.