Application of Adaptive Neuro-Fuzzy Inference Rule-based Controller in Hybrid Electric Vehicles

Abstract

Designing of hybrid architecture has greater importance in the development of electric vehicles to enhance the life cycle of the battery, to protect from nonlinearities and uncertainties of electrical energy storage systems. The objective of this paper is to design and apply the Adaptive Neuro-Fuzzy Inference rule-based controller with the semi-empirical strategy to protect from nonlinearities, uncertainties, and to improve efficiency in electric vehicles. In this paper, a fully active Li-Ion battery/Electric Double-Layer supercapacitor hybrid energy storage system used to decouple Li-Ion battery/Electric Double-Layer Supercapacitor from Direct Current bus and to generate Li-Ion battery current reference online semi-empirical rule-based energy management strategy used. The Control system is designed with the Adaptive Neuro-Fuzzy Interface rule-based controller to reduce non-linearity and different uncertainties of the energy storage system with two outputs battery current and DC bus voltage are chosen to measure control system design, which is tested under heavy and light load conditions. Results are validated using MATLAB/Simulink and the performance of the Adaptive Neuro-Fuzzy Interface rule-based controller is 16.96% and 9.81% greater than Robust Fractional Order Sliding Mode Controller under heavy and light load conditions.