| Citation: | DONG J，SU Y L，ZHANG D C. Ensemble-based prediction using multi-level degradation parameters for micro direct methanol fuel cells[J]. Journal of Beijing University of Aeronautics and Astronautics，2025，51（10）：3567-3577 （in Chinese） doi: 10.13700/j.bh.1001-5965.2023.0517
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The deterioration of the membrane electrode assembly in micro direct methanol fuel cells (µDMFC) limits cell efficiency and lifespan. Accurate prediction of the state of health (SOH) and remaining useful life (RUL) is essential for ensuring the safe and reliable operation of µDMFCs in industrial applications. The degradation trend of the output voltage fluctuates depending on the operating conditions. Traditional trend regression methods, however, are inadequate for capturing such stochastic fluctuations. Therefore, an RUL ensemble-based prediction method based on the output voltage and equivalent circuit model (ECM) was proposed under a combination of data-driven and mechanistic models. The degradation covariate of load current was introduced to account for dynamic operating conditions. The load changes in the future were reconstructed using a random process, and combined with the degradation trend of ECM parameters, the accurate estimation of the output voltage and RUL prediction were achieved. The proposed approach was validated using accelerated aging tests under the China light vehicle test cycle (CLTC). Experimental results show that the ensemble-based method can adapt to varying operating conditions. RUL prediction accuracy and precision are 88.18% and 85.71%, respectively, outperforming the best individual model by 3.27% and 14.28%.
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