A proton exchange membrane fuel cells degradation prediction method based on multi-scale temporal information merging network

• Published in: Energy (Oxford) 2024-12, Vol.313, p.133995, Article 133995
• Main Authors: Wang, Zifei, Tao, Jili, Liu, Zhitao, Feng, Han, Ma, Longhua, Xu, Ming, Su, Hongye
• Format: Article
• Language: English
• Subjects: Feature extraction; Multi-scale decomposition; Multi-scale merging; P-M scores; PEMFC performance degradation prediction; Wavelet
• ISSN: 0360-5442
• DOI: 10.1016/j.energy.2024.133995

Performance degradation prediction is considered as an effective method to improve the service life of proton exchange membrane fuel cells (PEMFCs). A hybrid method Multi-scale Temporal Information Merging Network (MTIMN) is then proposed for PEMFC degradation prediction, which is designed to merge information from both macro and micro perspectives in PEMFC historical working data. Firstly, a data preprocessing method based on P-M scores and min-max wavelet denoising is designed to eliminate redundant variables and noise considering the structure of PEMFC and data statistics. Secondly, aiming at the voltage recovery phenomenon during the aging process, a Multi-scale Exponential Decomposition Encoding Module (MEDEM) is presented to extract and encode different time scales of sequences from the PEMFC degradation data. Thirdly, a two-layer Historical Information Integration Module (HIIM) is established, which consists of the trend extraction module and the local extraction module, to extract deep dynamic nonlinear characteristics. Finally, the Merging Prediction Module (MPM) merges information from trend and local modules for the final output. The experimental results show that Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) are 0.0107 and 0.0079, respectively. Therefore, MTIMN can effectively extract deep dynamic nonlinear characteristics in PEMFC degradation data and improve prediction accuracy greatly. •Principles of PEMFC are elucidated and a pre-processing method with P-M scores is employed to eliminate redundant variables and noise.•Multi-scale exponential decomposition encoding module decomposes the operating data and expand dimensions to mine possible temporal modes.•Historical information integration module extracts deep dynamic nonlinear characteristics from both trend and local perspectives with low complexity.