Project and Development of a Reinforcement Learning Based Control Algorithm for Hybrid Electric Vehicles

• Published in: Applied sciences Vol. 12; no. 2; p. 812
• Main Authors: Maino, Claudio, Mastropietro, Antonio, Sorrentino, Luca, Busto, Enrico, Misul, Daniela, Spessa, Ezio
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.01.2022
• Subjects: Air pollution; Algorithms; Artificial intelligence; Control algorithms; Control theory; Electric vehicles; Emissions; Energy management; energy management system; Greenhouse effect; Greenhouse gases; hybrid electric vehicle; Learning; Pollutants; Powertrain; Q-learning; real-time control; Reinforcement; reinforcement learning; Software
• ISSN: 2076-3417; 2076-3417
• DOI: 10.3390/app12020812

Hybrid electric vehicles are, nowadays, considered as one of the most promising technologies for reducing on-road greenhouse gases and pollutant emissions. Such a goal can be accomplished by developing an intelligent energy management system which could lead the powertrain to exploit its maximum energetic performances under real-world driving conditions. According to the latest research in the field of control algorithms for hybrid electric vehicles, Reinforcement Learning has emerged between several Artificial Intelligence approaches as it has proved to retain the capability of producing near-optimal solutions to the control problem even in real-time conditions. Nevertheless, an accurate design of both agent and environment is needed for this class of algorithms. Within this paper, a detailed plan for the complete project and development of an energy management system based on Q-learning for hybrid powertrains is discussed. An integrated modular software framework for co-simulation has been developed and it is thoroughly described. Finally, results have been presented about a massive testing of the agent aimed at assessing for the change in its performance when different training parameters are considered.