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Performance decay prediction model of proton exchange membrane fuel cell based on particle swarm optimization and gate recurrent unit
The durability of proton exchange membrane fuel cells (PEMFC) is an important issue that restricts their large-scale application. To improve their reliability during use, this paper proposes a short-term performance degradation prediction model using particle swarm optimization (PSO) to optimize the gate recurrent unit (GRU). After training using only the data from the first 300 h, good prediction accuracy can be achieved. Compared with the traditional GRU algorithm, the proposed prediction method reduces the root mean square error (RMSE) and mean absolute error (MAE) of the prediction results by 44.8 % and 35.1 %, respectively. It avoids the problem of low accuracy in predicting performance during the temporary recovery phase in traditional GRU models, which is of great significance for the health management of PEMFC.
Performance decay prediction model of proton exchange membrane fuel cell based on particle swarm optimization and gate recurrent unit
The durability of proton exchange membrane fuel cells (PEMFC) is an important issue that restricts their large-scale application. To improve their reliability during use, this paper proposes a short-term performance degradation prediction model using particle swarm optimization (PSO) to optimize the gate recurrent unit (GRU). After training using only the data from the first 300 h, good prediction accuracy can be achieved. Compared with the traditional GRU algorithm, the proposed prediction method reduces the root mean square error (RMSE) and mean absolute error (MAE) of the prediction results by 44.8 % and 35.1 %, respectively. It avoids the problem of low accuracy in predicting performance during the temporary recovery phase in traditional GRU models, which is of great significance for the health management of PEMFC.
Performance decay prediction model of proton exchange membrane fuel cell based on particle swarm optimization and gate recurrent unit
Ziliang Zhao (author) / Yifan Fu (author) / Ji Pu (author) / Zhangu Wang (author) / Senhao Shen (author) / Duo Ma (author) / Qianya Xie (author) / Fojin Zhou (author)
2024
Article (Journal)
Electronic Resource
Unknown
Metadata by DOAJ is licensed under CC BY-SA 1.0
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