Degradation prediction of PEMFC based on BiTCN-BiGRU-ELM fusion prognostic method

• Published in: International journal of hydrogen energy Vol. 87; pp. 361 - 372
• Main Authors: Hua, Zhiguang, Yang, Qi, Chen, Jingwen, Lan, Tianyi, Zhao, Dongdong, Dou, Manfeng, Liang, Bin
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 18.10.2024
• Subjects: Extreme learning machine; Fuel cells; Gated recurrent unit; Prognostic; Remaining useful life; Temporal convolutional network; Gated recurrent unit; Extreme learning machine; Temporal convolutional network; Remaining useful life; Fuel cells; Prognostic
• ISSN: 0360-3199
• DOI: 10.1016/j.ijhydene.2024.08.502

Durability is one of the important factors limiting the large-scale commercial application of proton exchange membrane fuel cells (PEMFC) systems, and accurate lifetime prediction of the PEMFC is beneficial to enhance their durability and reliability. In this paper, a fusion prognostic model based on bidirectional temporal convolutional network, bidirectional gated recurrent unit, and extreme learning machine (BiTCN-BiGRU-ELM) is proposed to realize short-term degradation prediction and remaining useful life estimate of the PEMFC system. The nature-inspired metaheuristic of the crested porcupine optimizer algorithm is introduced to optimize the parameters of the proposed BiTCN-BiGRU-ELM method. The effectiveness of the hybrid method is verified under three current conditions: steady state, quasi-dynamic, and dynamic. The fusion model can significantly improve prediction performance compared to traditional machine learning methods. This method is of great significance for online lifetime prediction and health management of PEMFC. •A PEMFC degradation prediction model of BiTCN-BiGRU-ELM was proposed.•Improved prediction performance of ELM by BiTCN-BiGRU feature extraction.•Optimal model parameters were determined using a CPO algorithm.•The effect of different training lengths on prediction accuracy was investigated.•This fusion model can be applied to RUL's prediction under dynamic conditions.