Intelligent Energy Management System Evaluation of Hybrid Electric Vehicle Based on Recurrent Wavelet Neural Network and PSO Algorithm

Abstract

An energy management system (EMS) for hydrogen fuel cell hybrid electric vehicles (FCHEV) based on artificial intelligent (AI) technique is presented in this paper. In order to achieve a fast dynamic response and maintain high efficiency of energy storage resources, the fuzzy logic controller (FLC) and artificial neural networks (ANNs) are utilized for purpose of intelligently managing the system's power flow. Moreover, the feed-forward wavelet neural network linked with proportional-integral (PI) controller named (WNN-PI) and the recurrent wavelet neural network linked with PI controller named (RWNN-PI) are tuned by particle swarm optimization (PSO) algorithm, both are aimed at the operating of the resources at high efficiency with respect to their mechanism performance, meeting the load power demands efficiently, and reducing hydrogen usage. Finally, a comparison of the simulation outcomes is presented to choose the best of the proposed AI controllers where the results showed optimum power flow between power sources and load power of FCHEV then as consequence, the BAT and UC are run in a safe manner and extend their lifetime, also, the average efficiency of the FC stack has been increased, and the amount of usage of hydrogen fuel is reduced. The simulation of AI EMS has been carried out by MATLAB/Simulink R2022a, with various vehicle driving cycles by the advanced vehicle simulator (ADVISOR). © 2023,International Journal of Intelligent Engineering and Systems.All Rights Reserved.