Intelligent Energy Management System for Hybrid Electric Vehicle Based on Optimization Wavelet Neural Network by PSO Algorithm

Abstract

An intelligent controller is proposed in this work for plug-in hydrogen Fuel Cell Hybrid Electric Vehicle (FCHEV) that integrated Fuel Cell (FC), Battery (BAT), and Ultracapacitor (UC) to reach a high dynamic response and keep high efficiency of energy storage resources. That controller manages the power flow of the system in an intelligent tracking to be optimal for FCHEV. Where the Fuzzy Logic Controller (FLC) and the Artificial Neural Networks (ANNs) are employed to meet the Energy Management System (EMS) requirements thereby efficient management has been developed for ensuring that three power sources are running at high efficiency with their mechanism performance and meets efficiently the load power demands and uses less hydrogen consumption. Moreover, the control strategy of the Wavelet Neural Network that is linked with the PI controller, named WNN-PI, and the parameters of WNN-PI are tuned by using the Particle Swarm Optimization (PSO) algorithm is also used. Considering the battery and ultra-capacitor state-of-charge (SOC) with power conditioning unit converters that control the FC and BAT output and provide the desired voltage of the DC bus as well as keep the voltage stable at 300 V. Analysis and evaluations of the system have been done by MATLAB/Simulink environment while various vehicle driving cycles have been applied by using ADVISOR Simulator. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.