SOC estimation of lithium battery based on multi-innovation unscented Kalman filter algorithm

Abstract

Precise of the state is an imperative necessity for ensuring the dependable operation of lithium-ion batteries. The state of charge (SOC) of lithium-ion batteries is arduous to determine precisely. Therefore, a novel method was proposed, which incorporates the Unscented Kalman Filter (UKF) algorithm in combination with the theory of multi-innovation. This method exhibits an enhanced estimation precision of the UKF algorithm by means of re-utilizing prior information. The process encompasses a charge-discharge experiment, and identification of offline parameters for obtaining the RC equivalent-circuit model parameter. The model was verified to be accurate and correct through simulation using Matlab/Simulink. The UKF and MIUKF methods are utilized for estimating the actual operational state of a single lithium battery. According to experimental results, MIUKF offers a higher degree of accuracy and effectiveness in estimating the SOC than the UKF algorithm, with a smaller margin of SOC estimation error.