Optimal sources rating of electric vehicle based on generic battery storage system model

Abstract

Batteries are the foundation stone of the hybrid-electric vehicle, where the powertrain is made of a battery and an energy source. An accurate battery model is a necessary tool for a successful sizing procedure. This paper presents a new generic battery model for the sizing process; it utilizes different methods of battery mocking up into one model. Firstly, a database is created based on battery tests performed under various conditions. Then, the model receives the real-time power demand and signals of the environment status; then, the algorithm characterizes the parameter changes. The algorithm interpolates and extrapolates the data to find the predicted operating point. The findings are fed into a battery-equivalent circuit where some basic parameters are revealed. Then, analytical equations are employed to supply all battery parameters. The outcomes show that the proposed generic model can better predict the battery parameters through all operating regions under varying conditions. The suggested algorithm could be designed for all types of batteries by reentering the data for any specific battery. A case study was made on the lithium ferro-phosphate (LiFePo4) battery. The experimental results demonstrate that the proposed model is more accurate than the others; thus, the sizing results are more optimal.