Energy Management System For Hybrid Electrical Vehicles Using Soft Computing Algorithms

Abstract

newline newlineVast growth in global population, commercialization, and modernization leads to huge increase in use of vehicles. The traditional Internal Combustion Engine (ICE) operated vehicles use fossil fuel as power source resulting in pollution due to hydrocarbon emission such as Carbon Dioxide (CO2), Carbon Monoxide (CO), Nitrogen (N2), etc. Recently, Hybrid Electrical Vehicles (HEVs) have become more popular because of its pollution free nature, lower cost and lesser maintenance as an alternative for the continuously declining and costly fossil fuel operated vehicles. The HEVs with hybrid sources have shown greater efficiency in the critical conditions for providing the power to the HEVs in absence of HEV. However, the Energy Management System (EMS) is very challenging for HEVs with multiple sources such as Fuel cell (FC), Battery, and Ultra-capacitor (UC) because of dynamic changes in driving conditions, vehicle dynamics and external environment parameters. newlineThis research work focuses on the automatic control of hybrid energy sources (HES) in HEVs using EMS based on soft computing techniques and optimization techniques. It investigated Grey wolf optimization (GWO) and fuzzy logic based EMS for the selection of the power source for the HEV motor. The proposed work is divided into three phases, including modelling and simulation of HEVs with hybrid power sources, EMS based on fuzzy logic, and EMS for HEVs based on GWO. newlineUnder extremely dynamic circumstances, the Fuzzy logic-based EMS strategy demonstrates effective control of the power sources to meet the load demand of HEVs. The Mamdani fuzzy model is used to create the fuzzy logic EMS, which runs on battery and fuel cells instead of an internal combustion engine. The fuzzy logic-based EMS competently and inevitably controls the newline