Battery Optimal Sizing under a Synergistic Framework with DQN Based Power Managements for the Fuel Cell Hybrid Powertrain

Jianwei Li, Hanxiao Wang, Hongwen He, Zhongbao Wei, Qingqing Yang, Petar Igic

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This paper proposes a synergistic approach that traverses the battery optimal size simultaneously against the optimal power management based on deep reinforcement learning (DRL). A fuel cell hybrid electric vehicle (FC-HEV) with the fuel cell/battery hybrid powertrain is used as the study case. The battery plays a key role in current transportation electrification, and the optimal sizing of the battery is critical for both system technical performances and economical revenues, especially in the hybrid design. The optimal battery design should coordinate the static sizing study against the dynamic power distribution for a given system, but few works provided the synergistic consideration of the two parts. In this study, the interaction happens in each sizing point with the optimal power sharing between the battery and the FC, aiming at minimizing the summation of hydrogen consumption, FC degradation and battery degradation. Under the proposed framework, the power management is developed with deep Q network (DQN) algorithm, considering multi-objectives that minimizing hydrogen consumption and suppressing system degradation. In the case study, optimal sizing parameters with lowest cost are determined. Leveraged by the optimal size, the hybrid system economy with synergistic approach is improved by 16.0%, comparing with the conventional FC configuration.

Original languageEnglish
JournalIEEE Transactions on Transportation Electrification
Publication statusE-pub ahead of print - 21 Apr 2021

Bibliographical note

Publisher Copyright:


  • Batteries
  • deep reinforcement learning
  • Degradation
  • fuel cell hybrid electric vehicle
  • Fuel cells
  • Heuristic algorithms
  • hybrid energy storage system
  • Load modeling
  • power management
  • Power system management
  • sizing study
  • Vehicle dynamics

ASJC Scopus subject areas

  • Automotive Engineering
  • Transportation
  • Energy Engineering and Power Technology
  • Electrical and Electronic Engineering


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