Lithium-ion Battery SOC Estimation Based On Dynamic Gain Joint-EKF Algorithm

Electric vehicles have the advantages of low noise,zero emissions,etc.It is one of the important ways to save energy and reduce emissions in the transportation sector.As a core component of electric vehicles,the reasonable degree for the power battery used has direct influence on the performance of electric vehicles.At present,the battery management system(BMS)research mainly includes: establish a system model with accurate description of the battery characteristics,accurately estimating the state of charge(SOC)of the battery,and reasonably predicting the battery health status.The battery SOC estimation is one of the key technologies of the BMS.Accurately estimate the battery SOC,can effectively prevent the battery over-charging and over-discharging,and extend the battery life,but also to provide an important basis for controlling the electric vehicles.This paper selects Lithium-ion battery as the research carrier,on the purpose of improving accuracy of the battery SOC estimation,and extending the research around the battery SOC estimation algorithm.The mainly research works are as following aspects:Firstly,to thoroughly understand the development of electric vehicle battery and BMS and the current research status of battery SOC estimation algorithms,analyze the basic characteristics of lithium-ion batteries and the factors that affect the SOC estimation of lithium-ion batteries,and study the current commonly used battery models.The battery mixed noise model based on the traditional Thevenin equivalent model was used to reduce the adverse effect of drift current on battery SOC estimation,and a parameter identification method was given to verify the accuracy of the model.Secondly,the existing SOC estimation algorithm for lithium-ion batteries was analyzed.After study the basic theory of Joint-EKF algorithm,in order to overcome the problem of insufficient battery SOC estimation due to observed voltage lag in Joint-EKF algorithm under special conditions where the current changes drastically,based on the Joint-EKF algorithm,the gain coefficient is adjusted online to enhance the tracking effect of the algorithm,in order to improve the accuracy of the SOC estimation of the lithium-ion battery,and to verify the improved Joint-EKF algorithm,Experimental results show that the Joint-EKF algorithm adjusted by the online gain factor is more accurate in estimating battery SOC.Finally,around realizing the Lithium-ion battery SOC estimation builds the BMS platform.Design the hardware and software components of the BMS,which is related to the battery SOC estimation,and tests the designed system function through the experimental verification.Experimental results show that,the designed system platform can meet the requirements of the related data collection and estimation accuracy for the battery SOC estimation,and has some practical value.Given the online gain coefficient adjustment Joint-EKF algorithm has advantage in the terms of estimating the Lithium-ion battery SOC,the algorithm has the practical significance to meet the requirements of the battery SOC estimation precision.