考虑长-短程时间序列异质性的质子交换膜燃料电池剩余使用寿命预测方法

doi:10.19595/j.cnki.1000-6753.tces.251473

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Abstract To improve the accuracy and stability of aging prediction for proton exchange membrane fuel cell (PEMFC), this study proposes a novel predictive modeling framework that explicitly distinguishes long-term and short-term time-series characteristics to capture the heterogeneity in fuel cell voltage degradation. PEMFC aging is simultaneously influenced by global, gradual degradation processes and local, abrupt fluctuations induced by complex electrochemical recovery phenomena. Traditional single-model approaches often struggle to accurately capture both types of characteristics, thereby reducing generalization capability and robustness under varying operating conditions.
The proposed method first employs a multi-scale resolution decomposition strategy to separate the long-term and short-term components of the PEMFC voltage signal. The long-term component reflects the overall degradation trend and is modeled using the Mamba state-space architecture, which effectively represents long-range temporal dependencies and extracts smooth global aging trajectories. The short-term component represents transient, high-frequency fluctuations caused by recovery and other localized effects, and is modeled using a Transformer equipped with a local window attention mechanism. The Transformer excels at capturing nonlinear relationships and abrupt variations over short temporal spans, thereby modeling intricate short-term dynamics that are otherwise overlooked by global models. After the two components are modeled separately, a long-short router with learnable fusion weights is introduced to adaptively integrate the outputs through end-to-end training, enabling the model to automatically adjust their contribution ratios and generate the final prediction of the voltage degradation trajectory. This fusion strategy leverages the complementary strengths of the two architectures: the Mamba's global stability in trend estimation and the Transformer's high sensitivity to local fluctuations.
The proposed method is validated using aging datasets collected under static, quasi-dynamic, and fully dynamic operating conditions, and is compared with several classical machine learning and deep learning methods, including backpropagation neural network (BP), long short-term memory (LSTM), extreme learning machine (ELM), gated recurrent unit (GRU), and Transformer. When the training set proportion is 55%, under static conditions, the proposed method improves RMSE by 81.77%, 10.62%, 85.51%, 85.26%, and 72.92% compared with BP, ELM, LSTM, GRU, and Transformer, respectively. Under quasi-dynamic conditions, RMSE is improved by 5%, 50%, 24%, 35%, and 42% relative to the same baselines. Under dynamic conditions, the proposed method achieves the lowest prediction error. Across different failure thresholds, the remaining useful life (RUL) prediction accuracy of the proposed method consistently surpasses that of the benchmark approaches. The results show that, under various training data proportions and failure voltage thresholds, the proposed fusion model achieves the lowest prediction error. It is capable of accurately tracking both long-term degradation trends and short-term fluctuation patterns, enabling high-precision estimation of RUL.
Comprehensive simulation analyses highlight two major advantages of the proposed framework: (1) The proposed method enhances the joint perception of global degradation trends and local dynamic variations, thereby improving prediction stability and generalization capability under unknown operating conditions. (2) By integrating the state-space modeling strengths of Mamba with the local attention mechanism of the Transformer, and by employing a learnable-weight long-short router for adaptive fusion, the method effectively handles the complex temporal dependencies, nonlinear behaviors, and multi-scale dynamic characteristics inherent in PEMFC aging processes. (3) The proposed fusion model provides a robust and high-precision prediction tool for PEMFC systems across diverse operating environments, contributing to improved maintenance planning and operational reliability.

Key words： Proton exchange membrane fuel cell (PEMFC) remaining useful life degradation prediction fusion weight heterogeneity

Received: 21 August 2025

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TM911

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	Zha Pengtang
	Su Hongyu
	Gao Fengyang
	Han Yunfei
	Yue Wenhan

Cite this article:

Zha Pengtang,Su Hongyu,Gao Fengyang等. Remaining Useful Life Prediction of Proton Exchange Membrane Fuel Cell Considering Long-Short Range Time Series Heterogeneity[J]. Transactions of China Electrotechnical Society, 2026, 41(15): 5330-5344.

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https://dgjsxb.ces-transaction.com/EN/10.19595/j.cnki.1000-6753.tces.251473 OR https://dgjsxb.ces-transaction.com/EN/Y2026/V41/I15/5330

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