Research on joint control of water pump and radiator of PEMFC based on TCO-DDPG

Zhao, Huiru; Pan, Sichao; Ma, Libo; Wu, Yue; Guo, Xiaomei; Liu, Jiafeng

doi:10.1016/j.ijhydene.2023.05.020

Cited by 2 publications

(1 citation statement)

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“…To solve this problem, the utilization and stability of renewable energy sources can be significantly improved by using energy storage and conversion systems. The production of green hydrogen from waste photovoltaic and waste wind energy, which is then utilized in fuel cells to generate electricity, can be an essential means of smoothing out fluctuations in renewable energy, proton exchange membrane fuel cells (PEMFC) are increasingly gaining popularity in fixed power stations, energy storage devices, transportable power sources, automobiles, aviation, and various other applications due to their high energy conversion efficiency, pollution-free emissions, low operating temperature, and rapid start-stop capabilities [1][2][3]. When hydrogen produces water through an electrochemical reaction, it releases thermal energy.…”

Section: Introductionmentioning

confidence: 99%

Temperature Control of Fuel Cell Based on PEI-DDPG

Lu,

Yan

2024

Energies

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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.

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Section: Introductionmentioning

confidence: 99%