FUEL CELL LIFE ESTIMATION BASED ON INFORMER ALGORITHM

Shi Yong; Zhao Hongxiao; Xie Di; Wang Liangliang; Su Jianhui; Xie Bao

doi:10.19912/j.0254-0096.tynxb.2024-0592

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Acta Energiae Solaris Sinica ›› 2025, Vol. 46 ›› Issue (8) : 240-248. DOI: 10.19912/j.0254-0096.tynxb.2024-0592

FUEL CELL LIFE ESTIMATION BASED ON INFORMER ALGORITHM

Shi Yong¹, Zhao Hongxiao¹, Xie Di², Wang Liangliang², Su Jianhui¹, Xie Bao¹

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Abstract

In the present, common methods for estimating fuel cell life include long short-term memory （LSTM） and gated recurrent unit （GRU） neural networks. However, these methods have drawbacks in capturing long-term dependencies and ensuring accuracy. To address these issues, a fuel cell life estimation approach based on the Informer algorithm is proposed, aiming to enhance both accuracy and efficiency. Data smoothing techniques such as the weighted average method and the Pearson coefficient method are employed to reduce noise and improve data trend. By leveraging the multi-scale information fusion and long-term dependency modeling capabilities of the Informer model, a life estimation framework is crafted to enable online fuel cell life estimation. Subsequently, three sets of experiments are conducted to compare with traditional LSTM and GRU models. When the training set constitutes 80%, the Informer model exhibits the smallest mean absolute error （MAE）, root mean square error （RMSE）, and mean absolute percentage error （MAPE）, indicating higher estimation accuracy compared to LSTM and GRU models. These findings demonstrate the outstanding performance of the Informer model in long-term series estimation, thereby establishing a dependable foundation for fuel cell life estimation.

Key words

neural networks / fuel cells / parallel processing / life estimation / ProbSparse self-attention mechanism

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Shi Yong, Zhao Hongxiao, Xie Di, Wang Liangliang, Su Jianhui, Xie Bao. FUEL CELL LIFE ESTIMATION BASED ON INFORMER ALGORITHM[J]. Acta Energiae Solaris Sinica. 2025, 46(8): 240-248 https://doi.org/10.19912/j.0254-0096.tynxb.2024-0592

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