An Adaptive Energy Management Strategy for Off-Road Hybrid Tracked Vehicles

Lijin Han, Wenhui Shi and Ningkang Yang ()
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Lijin Han: School of Mechanical Engineering, Beijing Institute of Technology, Beijing 100081, China
Wenhui Shi: School of Mechanical Engineering, Beijing Institute of Technology, Beijing 100081, China
Ningkang Yang: School of Mechanical Engineering, Beijing Institute of Technology, Beijing 100081, China

Energies, 2025, vol. 18, issue 6, 1-15

Abstract: Conventional energy management strategies based on reinforcement learning often fail to achieve their intended performance when applied to driving conditions that significantly deviate from their training conditions. Therefore, the conventional reinforcement-learning-based strategy is not suitable for complex off-road conditions. This research suggests an energy management strategy for hybrid tracked vehicles operating in off-road conditions that is based on adaptive reinforcement learning. Power demand is described using a Markov chain model that is updated online in a recursive way. The technique updates the MC model and recalculates the reinforcement learning algorithm using the intrinsic matrix norm (IMN) as a criteria. According to the simulation results, the suggested method can increase the adaptability of energy management based on the reinforcement learning strategy in off-road conditions, as evidenced by the 7.66% reduction in equivalent fuel consumption when compared with the conventional Q-learning based energy management strategy.

Keywords: hybrid tracked vehicle; energy management strategy; Markov Chain; reinforcement learning (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
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