A novel multivariable prognostic approach for PEMFC degradation and remaining useful life prediction using random forest and temporal convolutional network

Tian Zhang, Zhengmeng Hou, Xiaoqin Li, Qianjun Chen, Qichen Wang, Christian Lüddeke, Lin Wu, Xuning Wu and Wei Sun

Applied Energy, 2025, vol. 385, issue C, No S0306261925002995

Abstract: Data-driven methods are effective in predicting future degradation trends (FDT) and remaining useful life (RUL) of proton exchange membrane fuel cells (PEMFCs). However, the complex and dynamic degradation behaviour of PEMFCs, influenced by diverse operational variables, poses significant challenges to existing prognostic approaches. This paper proposes a novel multivariable prognostic approach, termed RF-TCN, which combines random forest (RF) with temporal convolutional networks (TCN) to address these challenges. The approach incorporates three key innovations: (1) A hybrid RF and recursive feature elimination (RFE) method is employed to automatically select features most relevant to fuel cell degradation, reducing manual intervention and enhancing input robustness. (2) An improved TCN-based model is developed to effectively capture temporal degradation patterns, enabling accurate FDT and RUL predictions. (3) Particle swarm optimization (PSO) is utilized for automatic hyperparameter configuration, further boosting predictive performance. Empirical validation on ageing durability datasets demonstrates that the RF-TCN approach achieves superior prediction accuracy with selected optimal features and outperforms baseline TCN, CNN, RNN, and existing methods in the literature. This work advances prognostic methodologies, contributing to extending fuel cell lifespan and optimizing control strategies.

Keywords: Proton exchange membrane fuel cell; Prognostics; Future degradation trend; Remaining useful life; Temporal convolutional network; Random forest (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1016/j.apenergy.2025.125569

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