LIFETIME PREDICTION OF PEMFC BASED ON ENSEMBLE EMPIRICAL MODE DECOMPOSITION AND EXTREME LEARNING MACHINE

Chen Jingwen; Yang Qi; Lan Tianyi; Hua Zhiguang; Zhao Dongdong

doi:10.19912/j.0254-0096.tynxb.2023-1698

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Acta Energiae Solaris Sinica ›› 2025, Vol. 46 ›› Issue (2) : 135-141. DOI: 10.19912/j.0254-0096.tynxb.2023-1698

LIFETIME PREDICTION OF PEMFC BASED ON ENSEMBLE EMPIRICAL MODE DECOMPOSITION AND EXTREME LEARNING MACHINE

Chen Jingwen¹, Yang Qi¹, Lan Tianyi¹, Hua Zhiguang², Zhao Dongdong²

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Abstract

The data-driven prediction method can predict the lifetime of proton exchange membrane fuel cells （PEMFC）. To improve the accuracy of PEMFC lifetime prediction, this paper proposes a PEMFC remaining useful life prediction method that combines the ensemble empirical mode decomposition （EEMD） and extreme learning machine （ELM） optimized by particle swarm algorithm （PSO）. First, the moving average filter method is used to retain the main trends of the original data while filtering out noise and spikes. Second, the original data are decomposed in multiple time scales by EEMD to obtain the aging information of PEMFC in different time scales. Last, the intrinsic mode functions are respectively predicted by the PSO-ELM model, which can reduce the computational complexity while ensuring the prediction accuracy. By comparing the prediction results with the classical ELM model, this method can predict the aging trend of PEMFC more accurately.

Key words

proton exchange membrane fuel cell / prediction / empirical mode decomposition / extreme learning machine

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Chen Jingwen, Yang Qi, Lan Tianyi, Hua Zhiguang, Zhao Dongdong. LIFETIME PREDICTION OF PEMFC BASED ON ENSEMBLE EMPIRICAL MODE DECOMPOSITION AND EXTREME LEARNING MACHINE[J]. Acta Energiae Solaris Sinica. 2025, 46(2): 135-141 https://doi.org/10.19912/j.0254-0096.tynxb.2023-1698

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