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Deep reinforcement learning with deep-Q-network based energy management for fuel cell hybrid electric truck

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  • Wang, Zhifu
  • Zhang, Shunshun
  • Luo, Wei
  • Xu, Song

Abstract

To better utilize hydrogen energy from fuel cell electric vehicles (FCEVs), an investigation is conducted into how a proton exchange membrane fuel cell (PEMFC) hybrid vehicle's 100-km hydrogen consumption rate is affected by a dynamic programming-based energy management method. The Dueling Deep Q-Network (Dueling DQN) algorithm's energy management approach is proposed. The DQN algorithm is optimized to increase the method's stability and quicken its pace of convergence. The simulation results show that the fuel economy of DQN and Dueling DQN algorithm are 94.28 % and 95.7 % respectively, both of which are improved, while the Dueling DQN algorithm converges at 480 steps higher than the DQN algorithm converges at 800 steps, and the algorithm comparison verifies the two algorithms' The validity of both algorithms was verified by algorithm comparison. In China Truck Driving Conditions (CHTC-LT), the hardware-in-the-loop simulation of CAN communication protocol using NI-PXI hardware-in-the-loop test system achieves the target vehicle speed following and verifies the real-time performance of the strategy.

Suggested Citation

  • Wang, Zhifu & Zhang, Shunshun & Luo, Wei & Xu, Song, 2024. "Deep reinforcement learning with deep-Q-network based energy management for fuel cell hybrid electric truck," Energy, Elsevier, vol. 306(C).
    • Handle: RePEc:eee:energy:v:306:y:2024:i:c:s0360544224023053
    DOI: 10.1016/j.energy.2024.132531
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    References listed on IDEAS

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    1. İnci, Mustafa & Büyük, Mehmet & Demir, Mehmet Hakan & İlbey, Göktürk, 2021. "A review and research on fuel cell electric vehicles: Topologies, power electronic converters, energy management methods, technical challenges, marketing and future aspects," Renewable and Sustainable Energy Reviews, Elsevier, vol. 137(C).
    2. Renzhi Lyu & Zhenpo Wang & Zhaosheng Zhang, 2024. "Multi-Objective Optimization Strategy for Fuel Cell Hybrid Electric Trucks Based on Driving Patern Recognition," Energies, MDPI, vol. 17(6), pages 1-15, March.
    3. Ganesh, Akhil Hannegudda & Xu, Bin, 2022. "A review of reinforcement learning based energy management systems for electrified powertrains: Progress, challenge, and potential solution," Renewable and Sustainable Energy Reviews, Elsevier, vol. 154(C).
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    Cited by:

    1. Wang, Xuan & Kong, Chen & Han, Yunxiao & Chang, Juntao, 2024. "Prediction model for aero-engine combustor temperature field with physical constraints of high temperature deviation," Energy, Elsevier, vol. 313(C).

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