Open AccessAn Efficient Energy Management Scheme for a Hybrid FC-SC-Battery Electric Vehicle using Model Predictive Control and Multi-Objective Particle Swarm Optimization
Author(s) -
Adel A. A. El-Gammal
Publication year - 2019
Publication title -
international journal of recent technology and engineering
Language(s) - English
Resource type - Journals
ISSN - 2277-3878
DOI - 10.35940/ijrte.d8316.118419
Subject(s) - particle swarm optimization , energy management , state of charge , battery (electricity) , model predictive control , automotive engineering , driving cycle , electric vehicle , matlab , engineering , computer science , power (physics) , control theory (sociology) , energy (signal processing) , control (management) , algorithm , statistics , physics , mathematics , quantum mechanics , artificial intelligence , operating system
This paper presents a new Design for an efficient Energy Management System (EMS) for a Plug-in Hybrid Electric Vehicle (PHEV) which is supplied by Fuel Cell/Supercapacitor/Battery sources to attain maximum energy savings and minimize the amount of the fossil fuel utilized by the transportation sector. Model Predictive Control (MPC) combined with MultiObjective Particle Swarm Optimization (MOPSO) control strategy was proposed to manage the battery and the SC State of Charge (SOC) and decide the optimal power distribution between the Fuel Cell (FC), the Battery and the Super Capacitor (SC). The main target of the proposed EMS calculation technique is to achieve maximum energy efficiency, minimum battery current variation, minimum variation of the state of charge of the SC and minimum cumulative hydrogen consumption during the driving cycle. The proposed MOPSO based EMS methodology has been checked by the High Speed (US06) Driving Cycle utilizing the MATLAB/Simulink. Five diverse driving patterns are utilized to assess the speculation capacity of the developed technique. Simulation results and the realtime experiment demonstrate that the proposed MOPSO-Model Predictive Control based EMS technique can accomplish better energy effectiveness compared with Fuzzy Logic Rule-based EMS and GA-based EMS.