Fuzzy Logic-Based Novel Hybrid Fuel Framework for Modern Vehicles

The transport sector has proven to be the largest contributor to global $CO_{2}$ emissions. To reduce $CO_{2}$ emissions and improve mileage, the existing research has proposed different fuel models for vehicles such as Plug-in Hybrid Electric Vehicles (PHEVs), Electric Vehicles (EVs), solar and hydrogen Vehicles. However, these vehicles suffer from a range of issues and solutions are required to increase range, and improve charging. In this context, we propose A Novel Hybrid Fuel Framework for Modern Vehicles, to reduce $CO_{2}$ emissions and increase vehicle mileage, by managing energy resources efficiently through the application of Fuzzy Logic. It considers three different energy sources i.e., gasoline, solar and electric power, to charge a vehicle, and suggest 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 overcome 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 Kilometers 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% lower 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.