PEMFC DEGRADATION HYBRID PREDICTION BASED ON IMPROVED WHALE OPTIMIZATION ALGORITHM OPTIMIZED GRU-INFORMER

Su Hongyu; Gao Fengyang; Zha Pengtang; Yue Wenhan; Han Yunfei

doi:10.19912/j.0254-0096.tynxb.2025-1118

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Acta Energiae Solaris Sinica ›› 2026, Vol. 47 ›› Issue (12) : 91-100. DOI: 10.19912/j.0254-0096.tynxb.2025-1118

PEMFC DEGRADATION HYBRID PREDICTION BASED ON IMPROVED WHALE OPTIMIZATION ALGORITHM OPTIMIZED GRU-INFORMER

Su Hongyu¹, Gao Fengyang¹, Zha Pengtang¹, Yue Wenhan², Han Yunfei¹

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Abstract

To improve the accuracy and operating condition adaptability of proton exchange membrane fuel cell （PEMFC） degradation prediction models, a hybrid method combining a gated recurrent unit （GRU）-Informer neural network with an improved whale optimization algorithm （IWOA） is proposed. First, IWOA is used to optimize the key hyperparameters of the GRU-Informer model, yielding the best parameter sets under different operating conditions. Second, the GRU layer extracts short-term dynamic features from the input sequences, while the Informer layer captures global and long-term dependencies. Finally, five models are compared under both steady and dynamic conditions. The results demonstrate that the proposed method achieves the highest prediction accuracy across various scenarios and training set proportions, with a clear advantage in small-sample cases. Therefore, the method offers strong adaptability, stability, and generalization capability, effectively improving PEMFC performance degradation prediction.

Key words

proton exchange membrane fuel cell / aging prediction / whale optimization algorithm / GRU / Informer

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Su Hongyu, Gao Fengyang, Zha Pengtang, Yue Wenhan, Han Yunfei. PEMFC DEGRADATION HYBRID PREDICTION BASED ON IMPROVED WHALE OPTIMIZATION ALGORITHM OPTIMIZED GRU-INFORMER[J]. Acta Energiae Solaris Sinica. 2026, 47(12): 91-100 https://doi.org/10.19912/j.0254-0096.tynxb.2025-1118

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