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The effects investigation of data-driven fitting cycle and deep deterministic policy gradient algorithm on energy management strategy of dual-motor electric bus

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  • Zhang, Kaixuan
  • Ruan, Jiageng
  • Li, Tongyang
  • Cui, Hanghang
  • Wu, Changcheng

Abstract

Nowadays, the trend of powertrain electrification in the public transportation sector is clear. To meet the dramatic load variation and relatively high handling stability requirements for battery electric buses, the dual-motor four-wheel powertrain architecture attracts great attention in recent years. Although the bus routes are fixed, the driving speed and load vary significantly with time, season, passenger capacity, and traffic conditions, which presents a serious challenge for efficient power coupling in a dual-motor system to reduce energy consumption. This study provides a data-driven fitting cycle for the specific bus route. Then, Deep Deterministic Policy Gradient (DDPG) algorithm is introduced in Energy Management Strategy (EMS) design to improve the vehicle's economic performance with uncertain demand in the unknown cycle. The simulation results show that the proposed DDPG-EMS achieves 93.91%–97.66% of the benchmark Dynamic Programming (DP) – based EMS under various testing cycles. In addition, the comparison of DDPG-EMS agent trained by fitting cycle, standard cycle, and real driving data reached 97.2%–97.66%, 93.91%–97.0%, and 94.41%–96.0% of DP, respectively, which demonstrates the effectiveness of data-driven fitting cycle and reinforcement learning algorithm combination in EMS design for dual-motor electrified bus.

Suggested Citation

  • Zhang, Kaixuan & Ruan, Jiageng & Li, Tongyang & Cui, Hanghang & Wu, Changcheng, 2023. "The effects investigation of data-driven fitting cycle and deep deterministic policy gradient algorithm on energy management strategy of dual-motor electric bus," Energy, Elsevier, vol. 269(C).
    • Handle: RePEc:eee:energy:v:269:y:2023:i:c:s0360544223001548
    DOI: 10.1016/j.energy.2023.126760
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    References listed on IDEAS

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    Cited by:

    1. Peng, Jiankun & Shen, Yang & Wu, ChangCheng & Wang, Chunhai & Yi, Fengyan & Ma, Chunye, 2023. "Research on energy-saving driving control of hydrogen fuel bus based on deep reinforcement learning in freeway ramp weaving area," Energy, Elsevier, vol. 285(C).
    2. Liang, Zhaowen & Ruan, Jiageng & Wang, Zhenpo & Liu, Kai & Li, Bin, 2024. "Soft actor-critic-based EMS design for dual motor battery electric bus," Energy, Elsevier, vol. 288(C).
    3. Jia, Chunchun & He, Hongwen & Zhou, Jiaming & Li, Jianwei & Wei, Zhongbao & Li, Kunang, 2023. "A novel health-aware deep reinforcement learning energy management for fuel cell bus incorporating offline high-quality experience," Energy, Elsevier, vol. 282(C).
    4. 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).
    5. Chi T. P. Nguyen & Bảo-Huy Nguyễn & Minh C. Ta & João Pedro F. Trovão, 2023. "Dual-Motor Dual-Source High Performance EV: A Comprehensive Review," Energies, MDPI, vol. 16(20), pages 1-28, October.
    6. Jia, Chunchun & He, Hongwen & Zhou, Jiaming & Li, Jianwei & Wei, Zhongbao & Li, Kunang, 2024. "Learning-based model predictive energy management for fuel cell hybrid electric bus with health-aware control," Applied Energy, Elsevier, vol. 355(C).

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