Artificial intelligence and energy management in hybrid tracked and wheeled vehicles

Abstract

This article explores the role of artificial intelligence (AI) in optimizing energy management systems for hybrid transport. The study examines machine learning methods, neural networks, and fuzzy logic systems used for the adaptive distribution of energy between internal combustion engines and electric powertrains. The authors analyze the technical aspects of integrating intelligent algorithms into real-time onboard systems, including hierarchical control structures and computing module functionality. Particular attention is paid to challenges related to limited onboard computing resources, data quality, and the necessity of ensuring cybersecurity. Furthermore, the paper discusses economic and infrastructure factors influencing technological development, providing an overview of successful global practices and future prospects for scaling intelligent energy solutions in the context of the global energy transition.