Enhanced applicability of reinforcement learning-based energy management by pivotal state-based Markov trajectories

Jiaxin Chen, Xiaolin Tang, Meng Wang, Cheng Li, Zhangyong Li and Yechen Qin

Energy, 2025, vol. 319, issue C

Abstract: Data- or sample-driven reinforcement learning (RL) is crucial for advancing AI models, enabling supervised learning-based AI to evolve autonomously. However, sample efficiency remains a key challenge, and simply increasing the number of training samples is not a guaranteed solution. More importantly, the focus should be on the breadth and diversity of the data distribution. This paper focuses on hybrid electric vehicles, with an emphasis on energy management. A novel training scheme for RL-based energy-saving policies is proposed, which relies on pivotal Markov transitions as state-based trajectories, significantly enhancing the adaptability of learning-based strategies. Firstly, the contradictions and limitations of the optimization terms in traditional reward functions are highlighted, including the misguidance of cumulative states and the cumbersome adjustment of weights. To address these issues, an unweighted reward is designed to simplify the training process and make it more universal. Secondly, the state-based featured driving cycle, as a novel concept, employs a 'question bank' style environment to expose the RL agent to a more diverse state space. Even with more sources and larger volumes of velocity data, the representative driving cycle can be condensed into customizable lengths of time domain, serving as the pivotal state-based Markov trajectory. Finally, after finishing offline training on the Tencent cloud server, an online driver-in-the-loop test is performed. The core advantage of the proposed strategy lies in completing the training in one go while offering greater applicability, aligning with the training concept more suitable for RL-based agents.

Keywords: Hybrid electric vehicle; Energy management; Reinforcement learning; Enhanced applicability (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0360544225007571
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:319:y:2025:i:c:s0360544225007571

DOI: 10.1016/j.energy.2025.135115

Access Statistics for this article

Energy is currently edited by Henrik Lund and Mark J. Kaiser

More articles in Energy from Elsevier
Bibliographic data for series maintained by Catherine Liu ().