Energy Management in a Fuel Cell based Hybrid Electric Vehicle Using Machine Learning

Abstract

This paper explores energy management in fuel cell hybrid electric vehicles, emphasizing energy efficiency and operational performance. The study involves the mathematical modelling of critical hybrid powertrain components, including the fuel cell, lithium-ion battery, supercapacitors, and power converters. By leveraging artificial intelligence techniques, particularly neural networks, the research proposes innovative and optimized energy management strategies. Simulations conducted in Python within the Jupyter environment evaluate the effectiveness of these strategies, aiming to enhance fuel efficiency and support sustainable mobility. Synthetic datasets are employed for model training and validation, demonstrating the potential of machine learning to deliver substantial improvements.