Development of Progressive Fuzzy Logic and ANFIS Control for Energy Management of Plug-In Hybrid Electric Vehicle

In this project presents a Hybrid electric vehicles (HEVs) offer a promising alternative to conventional fuel-powered vehicles, with efficient and intelligent energy management being a key factor in their widespread global adoption. Recent advancements in intelligent control techniques, coupled with the growing need for smarter energy systems, have accelerated the development of energy-efficient HEVs. Energy management plays a critical role in improving the autonomy and reducing the operational cost of plug-in hybrid electric vehicles (PHEVs). To address this, a novel approach employing intelligent control strategies is proposed to manage energy distribution in PHEVs under varying driver behavior and load profiles. This paper focuses on enhancing battery performance using a Adaptive Neuro-Fuzzy Inference System (ANFIS), with the battery’s State of Charge (SOC) serving as the primary decision-making parameter. Engine speed and SOC are used as inputs, based on which the controller determines the appropriate torque required for energy conversion and battery charging. This is achieved by adjusting the forward gain value through intelligent control logic. Two advanced controllers—FLC and ANFIS—are implemented to determine the optimal forward gain, and the system performance is simulated using MATLAB/Simulink. The results from both control methods are analysed and compared to identify the more effective technique for efficient energy management in PHEVs. The findings demonstrate that advanced intelligent control methods significantly improve vehicle performance and contribute to better fuel economy in hybrid electric vehicles.

Keywords: Artificial intelligence, artificial neural network, energy management strategy, fuzzy logic, hybrid electric vehicles.