State Of Charge Estimation Of Lithium Iron Phosphate Battery Based On Second-Order RC Model

In response to the worsening situation of scarcity of oil resources and environment,the pure electric vehicle industry has been developing rapidly worldwide.As an important part of pure electric vehicle,the research of pure electric vehicle power lithium-ion battery packs is supported by many companies.The State of Charge（SOC）of the battery is an important research direction of power lithium-ion battery packs,and it is a direct indicator of the remaining power of the battery.Accurate estimation of the SOC of the battery can not only prevent the battery charge and discharge,and can enhance the overall performance of the battery,which is of great significance in battery research.The main work of this paper is as follows:（1）By comparing the performance of lithium-iron phosphate battery（LiFePO₄）with other types of batteries,LiFePO₄ battery is selected as the research object in this paper,and its charge and discharge reactions and main characteristics are studied.Considering the difficulty of direct measurement of SOC,the commonly used methods of SOC estimation are compared and analyzed,and the model based filtering method is used to estimate the SOC.（2）The advantages and disadvantages of the common equivalent circuit model of LiFePO₄battery is compared and analyzed.The second-order RC equivalent circuit model is selected as the equivalent circuit model of LiFePO₄ battery,and the discrete state space equation of the model is given.（3）Considering the influence of model parameters on the estimation accuracy of SOC,a recursive least square identification algorithm with adaptive forgetting factor is used to identify model parameters.（4）The method of estimating SOC based on Extended Kalman Filter（EKF）and Extended H Infinity Filter（EHIF）is studied.Considering the participation of the historical data of the EKF algorithm,a weighted multi-innovation EKF algorithm with improved discrete sliding mode observer is proposed to reduce the jitter and noise interference problems of the system by using the theory of multi-innovation and particle filtering,combined with the improved sliding mode observer.Considering that EKF algorithm regards random noise as Gaussian white noise and ignores the randomness of noise,and EHIF algorithm does not consider the influence of historical data when updating the status,a weighted multi-innovation EHIF algorithm is proposed to solve the problems of randomness of noise and insufficient participation of historical data.Finally,intermittent discharge experiment and DST experiment are conducted to verify the effectiveness of the proposed algorithm to improve the accuracy of SOC estimation.