Research on Estimation of Battery State of Electric Vehicle Battery Management System

Abstract

Estimating state parameters such as the state of charge (SOC) of power battery packs is a core function of BMS. Accurate estimation of battery SOC is conducive to the development of battery management systems. At the same time, the accurate estimation of the power battery SOC is an important basis for the driver to estimate the cruising range and arrange the travel plan. This paper studies a variety of SOC estimation methods, and provides basic reference information for the optimization and corresponding management of electric vehicle battery packs. Based on this, an integrated algorithm based on extended Kalman filter-Amp-time integration-open circuit voltage method (EKF-Ah-OCV) is proposed, which mainly uses the correction characteristics of Kalman filter algorithm to improve the Amp-time integration method and open circuit The accuracy of the voltage method not only overcomes the shortcomings of the inaccurate SOC initial value estimation of the ampere-time integration method, but also solves the problem of accumulated SOC estimation errors due to inaccurate long-term current measurement. This algorithm has good performance in the complex environment estimated by SOC, and can meet the requirements of power lithium batteries.