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

Author

Listed:
  • 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)

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.

Suggested Citation

  • Lijin Han & Wenhui Shi & Ningkang Yang, 2025. "An Adaptive Energy Management Strategy for Off-Road Hybrid Tracked Vehicles," Energies, MDPI, vol. 18(6), pages 1-15, March.
    • Handle: RePEc:gam:jeners:v:18:y:2025:i:6:p:1371-:d:1609640
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    References listed on IDEAS

    as
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