Transformer Based Long-Term Prognostics for Dynamic Operating PEM Fuel Cells

Abstract

Proton Exchange Membrane Fuel Cell (PEMFC) is considered as a clean alternative energy that has been widely used in many fields. However, the large-scale commercialization of PEMFC is still limited by its durability performance. Prognostics and Health Management (PHM) is considered as a helpful solution to improve the durability of PEMFC with degradation prediction and health-based control and maintenance methods. The long-term PEMFC prognostic task, as a primary component in PHM, has received extensive attention from both academia and industry. Recently, the combination of deep neural networks and PEMFC prognostics has expressed a broad research perspective. The deep-learning-based methods, such as Convolutional Neural Network (CNN), Echo State Network (ESN), and Recurrent Neural Network (RNN) family methods, have been well-studied by many researchers and have shown satisfactory performance in degradation prediction. However, the long-term prediction performance is still limited by the model structure and accumulative errors. This paper focuses on the degradation of the PEMFC and a Transformer-based PEMFC prognostic framework is proposed to predict the long-term degradation of the PEMFC system. The Transformer model is applied for the first time to predict the degradation of the PEMFC, and a series-attention mechanism is proposed to replace the self-attention mechanism in the Vanilla Transformer which could improve the health indicator (HI) prediction performance. Finally, the PEMFC dynamic durability test data is utilized to evaluate the performance of the proposed framework in both multi-step-ahead prediction and long-term prognostic conditions, and the experimental results illustrate the feasibility and effectiveness of the proposed method.