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An integrated power, energy, and thermal management strategy using cascaded Control for off-road autonomous hybrid vehicles

Author

Listed:
  • Sundar, Anirudh
  • Ghate, Atharva
  • Zhu, Qilun
  • Prucka, Robert
  • Figueroa-Santos, Miriam
  • Barron, Morgan

Abstract

Off-road hybrid vehicles operating in unstructured and thermally harsh environments require supervisory controllers capable of managing highly transient power demands while ensuring energy efficiency and component longevity. This paper presents a cascaded control strategy for integrated power, energy, and thermal management (IPETM) that addresses this multi-timescale challenge. The real-time framework combines a long-horizon optimizer that jointly minimizes fuel consumption and battery degradation with a fast compensatory controller that mitigates transient power fluctuations and uncertainties. Hardware-in-the-loop (HIL) testing using the actual vehicle control hardware and a high-fidelity virtual vehicle model demonstrates that the proposed approach maintains constraint-compliant operation under model and preview uncertainties. Benchmarking across nominal and extreme temperature off-road driving conditions shows that the IPETM strategy substantially reduces capacity degradation by ∼ 70 % relative to a real-time, long-horizon benchmark, without a significant increase in fuel consumption. Moreover, it achieves performance comparable to a synthesized short-update long-horizon controller that is computationally infeasible for real-time implementation and requires detailed future-demand previews. Sensitivity studies on control weights and update frequency further establish practical configuration guidelines. Overall, the results demonstrate that the proposed IPETM framework bridges the gap between real-time implementable and ideal optimization-based controllers, providing a computationally tractable and robust solution for integrated power, energy, and thermal management in autonomous hybrid off-road vehicles.

Suggested Citation

  • Sundar, Anirudh & Ghate, Atharva & Zhu, Qilun & Prucka, Robert & Figueroa-Santos, Miriam & Barron, Morgan, 2026. "An integrated power, energy, and thermal management strategy using cascaded Control for off-road autonomous hybrid vehicles," Applied Energy, Elsevier, vol. 406(C).
  • Handle: RePEc:eee:appene:v:406:y:2026:i:c:s0306261925019993
    DOI: 10.1016/j.apenergy.2025.127269
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    1. Valquíria D. V. Rodrigues & Alcido E. Wander & Fabricia S. da Rosa, 2025. "Poultry Eco-Controls: Performance and Accounting," Agriculture, MDPI, vol. 15(12), pages 1-16, June.
    2. Jia, Chunchun & Zhou, Jiaming & He, Hongwen & Li, Jianwei & Wei, Zhongbao & Li, Kunang, 2024. "Health-conscious deep reinforcement learning energy management for fuel cell buses integrating environmental and look-ahead road information," Energy, Elsevier, vol. 290(C).
    3. Du, Guodong & Zou, Yuan & Zhang, Xudong & Liu, Teng & Wu, Jinlong & He, Dingbo, 2020. "Deep reinforcement learning based energy management for a hybrid electric vehicle," Energy, Elsevier, vol. 201(C).
    4. Lian, Renzong & Peng, Jiankun & Wu, Yuankai & Tan, Huachun & Zhang, Hailong, 2020. "Rule-interposing deep reinforcement learning based energy management strategy for power-split hybrid electric vehicle," Energy, Elsevier, vol. 197(C).
    5. Fan Wang & Yina Hong & Xiaohuan Zhao, 2025. "Research and Comparative Analysis of Energy Management Strategies for Hybrid Electric Vehicles: A Review," Energies, MDPI, vol. 18(11), pages 1-28, May.
    6. Chen, Quanyi & Zhang, Xuan & Nie, Pengbo & Zhang, Siwei & Wei, Guodan & Sun, Hongbin, 2023. "A fast thermal simulation and dynamic feedback control framework for lithium-ion batteries," Applied Energy, Elsevier, vol. 350(C).
    7. Du, Guodong & Zou, Yuan & Zhang, Xudong & Guo, Lingxiong & Guo, Ningyuan, 2022. "Energy management for a hybrid electric vehicle based on prioritized deep reinforcement learning framework," Energy, Elsevier, vol. 241(C).
    8. Oecd, 2025. "Efficiencies in merger control," OECD Roundtables on Competition Policy Papers 321, OECD Publishing.
    9. Zuchang Gao & Cheng Siong Chin & Wai Lok Woo & Junbo Jia, 2017. "Integrated Equivalent Circuit and Thermal Model for Simulation of Temperature-Dependent LiFePO 4 Battery in Actual Embedded Application," Energies, MDPI, vol. 10(1), pages 1-22, January.
    10. Du, Guodong & Zou, Yuan & Zhang, Xudong & Kong, Zehui & Wu, Jinlong & He, Dingbo, 2019. "Intelligent energy management for hybrid electric tracked vehicles using online reinforcement learning," Applied Energy, Elsevier, vol. 251(C), pages 1-1.
    11. Jia, Chunchun & Liu, Wei & He, Hongwen & Chau, K.T., 2025. "Health-conscious energy management for fuel cell vehicles: An integrated thermal management strategy for cabin and energy source systems," Energy, Elsevier, vol. 333(C).
    12. Shivam Anand & Yamini G & Samarth Srivastava & Siddharth Kumar, 2025. "Vocal Gaze Mouse Controller," International Journal of Latest Technology in Engineering, Management & Applied Science, International Journal of Latest Technology in Engineering, Management & Applied Science (IJLTEMAS), vol. 14(6), pages 808-814, June.
    Full references (including those not matched with items on IDEAS)

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