Innovative Methods for State of the Charge Estimation for EV Battery Management Systems

Abstract

The aim of this chapter is to explain the different methods for state of charge (SOC) estimation of lithium-ion batteries and assist the readers in implementing a method in real time in a battery management system (BMS) of electric vehicle (EV). The different methods include extended Kalman filter, adaptive extended Kalman filter, central difference Kalman filter, sliding mode observer and backpropagation neural network. A possible inference from this chapter would be the implementation of the selected SOC estimation algorithm and observation of its performance in real time on an EV. The SOC estimation methods can be developed in MATLAB/Simulink environment or can be developed directly in C-language for implementation in BMS. Another possible inference that can be drawn from this chapter will be the analysis of individual step in each of the explained methods and analysis of the effect of the changing parameter in the equations. The researchers, students and engineers working in the domain of developing state estimation algorithms for EV batteries will find this explanation very relevant to their research and development works.