Lithium Battery SOC Estimation Based on Extended Kalman Filtering Algorithm

Abstract

Battery power detection has always been the core of the battery management system of electric vehicles, and the fast and accurate estimation of charged state can guarantee the safe operation of electric vehicles. This article is based on the electric car with iron phosphate lithium-ion battery PNGV model to complete the estimate of the lithium ion battery SOC, using Matlab Simulink module and simulation of the model. And it proves that PNGV model had higher precision. This system uses the extended kalman filter algorithm to estimate the charge state (SOC) of lithium battery and give the concrete implementation steps. Experimental simulation show that using the extended kalman filtering algorithm can rapidly estimate the charged state of lithium-ion batteries, the maximum error is about 4%, the average error is around 2%, can satisfy the accuracy of the battery management system needs.