Fuzzy Logic-Based Novel Hybrid Fuel Framework for Modern Vehicles

Abstract

The transport sector has proven to be the largest contributor of CO 2 in the Greenhouse Gas (GHG) emissions. To reduce CO 2 emissions and improve mileage, the existing research has proposed different models for vehicles such as Plug-in Hybrid Electric Vehicle (PHEVs), Electric Vehicle (EVs), solar and hydrogen Vehicles. However, the existing models clearly lack suitable solutions and there is a need to improve the existing vehicle models for lesser CO 2 emissions, longer range, and quicker charging. In this context, we propose A Novel Hybrid Framework for Modern Vehicles, to reduce CO 2 emissions and increase vehicle mileage, by managing energy resources efficiently using Fuzzy Logic. It considers three different energy sources i.e., gasoline, solar and electric power, to charge a vehicle, and a modification in the architecture of EVs is made for the availability of all these energy resources. We use Visual Studio to implement fuzzy logic based algorithm designed to simulate the proposed system and added a small gasoline engine to the existing architecture of EVs to provide energy resources that solve charging issues during long-range travel. We use the Statistical Package for Social Sciences (SPSS) tool to evaluate the performance of the proposed framework for CO 2 emissions and fuel efficiency. The proposed framework achieves the best mileage of 57.6 Kilometer per liter (Km/l) with a 660 Cubic Centimeter (CC) gasoline engine which is 111.11% more efficient than existing frameworks. Moreover, CO 2 emissions through our proposed framework are 41.52 Grams per Kilometer (G/Km) which are 53% lesser than current frameworks. The proposed framework also improves the charging duration of batteries i.e., a 10 Kilowatt-Hour (KwH) battery can be charged in 1 hour and 15 minutes.