Battery Optimal Sizing Under a Synergistic Framework With DQN-Based Power Managements for the Fuel Cell Hybrid Powertrain

This article 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 FC/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 multiobjectives that minimize hydrogen consumption and suppress 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%, compared with the conventional FC configuration.

Automated summary

This article proposes a synergistic approach that optimizes the battery optimal size and power management using deep reinforcement learning (DRL). By studying a fuel cell hybrid electric vehicle (FC-HEV), it is found that the battery plays a crucial role in transportation electrification, and the optimal sizing and power distribution are critical for system performance and economic benefits.