Study On SOC Estimation Strategy Of Lithium-ion Battery Based On LM-IEKF Algorithm

Popularization of electric vehicles is becoming one of the ways for energy conservation and emission reduction. As a core energy supply unit of electric vehicles, the reasonable degree for the power battery used has direct influence on the performance of electric vehicles. The battery state-of-charge(SOC) estimation is one of the key technologies of the battery management system(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, depths to understanding the development of electric vehicles power batteries and the current research status for the battery SOC estimation algorithm. Analysis of the basic characteristics and the SOC definition and SOC estimation factors of the Lithium-ion batteries. Compares advantages and disadvantages of the several commonly methods for the battery SOC estimation, and adopts the iterated extended kalman filter(IEKF) algorithm as the basic algorithm to estimate the battery SOC.Secondly, based on the relationship between the selected SOC estimation algorithm and the battery model, fully considers the impact of temperature to the battery characteristics, and establishes the improved second order RC battery model. Then, identifies the parameters of the established model, and verifies the accuracy of the model by Matlab/Simulink.Thirdly, researches the basic theory of the extended kalman filter(EKF) algorithm and IEKF algorithm, and based on the problems of EKF algorithm and IEKF algorithm estimates the battery SOC, adopts the Levenberg-Marquardt(LM) algorithm to optimize the iterated process of IEKF algorithm —— LM-IEKF algorithm to estimate the battery SOC. Detail elaborates the realized process for the adopted algorithm estimating the Lithium-ion battery SOC, and through the simulation validating the convergence of the LM-IEKF algorithm. The battery SOC estimation results of the adopted algorithm with the result of IEKF algorithm and EKF algorithm are compared. Compares results show that LM-IEKF algorithm has advantage in battery SOC estimation accuracy.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 LM-IEKF 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.