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

Abstract

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.

Challenges and opportunities of FACTS devices interacting with electric vehicles in distribution networks: A technological review

“…This article discusses a thorough strategy for the entire project and the creation of an energy planning solution for hybrid drivetrains founded on Q-learning [47]. The prototype model of an interstate electric vertical take-off and landing (VTOL) airplane is discussed in this study [48].…”

Optimal Control of Electrified Powertrains in Offline and Online Application Concerning Dimensioning of Li-Ion Batteries

“…Here, for example, also approximated approaches of the DP (ADP) are used, whereby, due to the significantly reduced computational effort, an online capability of the DP can be realized [49,50]. In recent years, however, a variety of other approaches have been developed in which the strategy is based purely on Artificial Intelligence [51][52][53].…”