Interpretable degradation forecasting of fuel cells under steady, quasi-dynamic and dynamic conditions: An ALA optimized TimesNet model based on ICEEMDAN decomposition
Rui Quan, Wen Li, Cheng Gong, Jianglan Liu, Lingkang Zheng, Xinhang Huang
Abstract
To improve the degradation prediction precision for fuel cells (FCs) under different operating conditions, a hybrid method was proposed by combining data decomposition with the artificial lemming algorithm (ALA) optimized TimesNet model (ICEEMDAN-ALA-TimesNet). The ICEEMDAN-ALA-TimesNet model exhibits substantially improved predictive performance over 12 benchmark methods across steady-state, quasi-dynamic, and dynamic conditions. Point prediction results for the FC1, FC2, and FC3 datasets indicate that, using only 30% of the training data, the ICEEMDAN-ALA-TimesNet model reaches MAE, MAPE, and RMSE of 0.017%–0.0966%, 0.0184–0.0298%, and 0.025%–0.1265%, respectively, with R2 ranging from 0.9951 to 0.9983. Even under a 60% missing data ratio, the model maintains strong robustness, with MAE, MAPE, and RMSE remaining within 0.0166%–0.1158%, 0.018%–0.0359%, and 0.0243%–0.1572%, respectively, and R2 values preserved between 0.9818 and 0.9987, highlighting its stability and generalization capability. A 95% confidence interval-based interval forecasting framework is further constructed to assess the reliability of predictions. Results indicate that the model achieves an average coverage of 95.17%–95.79% with an interval width (MS) narrowed to 0.025%–0.13%, thereby striking an optimal balance between Coverage and MS. This study provides an accurate and reliable reference for PEMFC degradation trend prediction and remaining useful life assessment.