A Neuro Fuzzy Based Energy Management Strategy for a Series Hybrid 2-Wheeler: *Note: Sub-titles are not captured in Xplore and should not be used

Abstract

Hybrid Vehicles can bridge the gap between Internal Combustion Engines (ICE) and Electric Vehicles (EV) to find a feasible solution for sustainable & affordable mobility. Series Hybrid Vehicles are range-extending vehicles where the engine is operated when the batteries are depleted giving an increased range to the user. To achieve such a transition, an Energy Management Strategy (EMS) for an Electric Hybrid Vehicle must have the objective of optimal utilization of Energy, reduced fuel consumption, fewer emissions. Rule based Strategies have the advantage of real time implementation but are limited in operating scenarios. This reduced scope can be expanded by using a Neuro-Fuzzy Network which is essentially a neural network combined with fuzzy logic. This paper details a Simulation of an EMS using Neuro-Fuzzy Rule Based Strategies on MATLAB-Simulink for a moderate-fidelity Series Hybrid electric two-wheeler model on MATLAB-Simulink. The objective of the EMS is to cover the distance traveled most efficiently. The results of the simulation show a significant improvement in energy consumption and fuel economy over the conventional ICE model. The Energy Consumption is also reduced by 133.5 Wh when compared with the Electric Model.