Temperature Control of Fuel Cell Based on PEI-DDPG

Proton exchange membrane fuel cells (PEMFCs) constitute nonlinear systems that are challenging to model accurately. Therefore, a controller with robustness and adaptability is imperative for temperature control within the PEMFC stack. This paper introduces a data-driven controller utilizing deep rei...

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Bibliographic Details
Published inEnergies (Basel) Vol. 17; no. 7; p. 1728
Main Authors Lu, Zichen, Yan, Ying
Format Journal Article
LanguageEnglish
Published Basel MDPI AG 01.04.2024
Subjects
Algorithms
Alternative energy sources
Control algorithms
Controllers
Cooling
Data mining
deep deterministic policy gradient
Deep learning
deep reinforcement learning
Energy resources
Energy storage
Force and energy
Fuel cell industry
Fuel cells
Green hydrogen
Heat
Hydrogen as fuel
importance sampling
Information storage and retrieval
Machine learning
Neural networks
PEI-DDPG
PEMFC
Renewable resources
temperature control
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Summary:Proton exchange membrane fuel cells (PEMFCs) constitute nonlinear systems that are challenging to model accurately. Therefore, a controller with robustness and adaptability is imperative for temperature control within the PEMFC stack. This paper introduces a data-driven controller utilizing deep reinforcement learning for stack temperature control. Given the PEMFC system’s characteristics, such as nonlinearity, uncertainty, and environmental conditions, we propose a novel deep reinforcement learning algorithm—the deep deterministic policy gradient with priority experience playback and importance sampling method (PEI-DDPG). Algorithm design incorporates technologies such as priority experience playback, importance sampling, and optimized sample data storage structure, enhancing the controller’s performance. Simulation results demonstrate the proposed algorithm’s superior effectiveness in temperature control for PEMFC, leveraging the PEI-DDPG algorithm’s high adaptability and robustness. The proposed algorithm’s effectiveness is additionally validated on the RT-LAB experimental platform. The proposed PEI-DDPG algorithm reduces the average adjustment time by 8.3%, 17.13%, and 24.56% and overshoots by 2.12 times, 4.16 times, and 4.32 times compared to the TD3, GA-PID, and PID algorithms, respectively.
ISSN:1996-1073
1996-1073
DOI:10.3390/en17071728