An Extensive Comparison of State of Charge Estimation of Lithium Ion Battery — Towards Predictive Intelligent Battery Management System for Electric Vehicles

Abstract

The electric vehicle is evolving as a futuristic technology vehicle to focus on the frequent serious energy and environment concerns. The Battery Management System (BMS) is the primary segment which has an essential job in controlling and securing the electric vehicle. The important functions of BMS include estimating battery state of charge (SoC) using various algorithms and propose features towards developing predictive intelligent BMS. Estimating SoC accurately is relevant for designing a predictive intelligent BMS. The accurate SoC estimation of a Li-ion battery is tough and involved task due to its excessive complexity, time-variant, and non-linear characteristics. This work intends to estimate and compare the SoC of thermal dependent Lithium Ion cell using different methods viz. Coulomb Counting (CC), Extended Kalman Filter (EKF) and Artificial Neural Network (ANN) algorithms.