State of Charge Estimation of Lithium-ion Batteries for Electric Vehicle.

Abstract

A battery management system for an electric automobile has traditionally been built around battery power detection. To accurately gauge the battery’s state of charge, extended Kalman filtering techniques are utilised (SOC). First-order Thevenin modelling is one modelling approach for battery equivalent circuits. The model simulation in Matlab Simulink and the completion of the design and methodology verification. The structure of the entire experiment as well as the algorithm’s flowchart are both included in the design of the experimental technique. The Extended Kalman Filtering method and the Ampere-Hour Integral methodology have been compared. The experimental simulation shows that the Extended Kalman Filtering method can predict the Li-ion battery’s SOC accurately with a maximum error of about 2%, satisfying the precision demands of battery management systems.