Data-driven health state estimation and remaining useful life prediction of fuel cells

Abstract

Proton exchange membrane fuel cells (PEMFCs) can revolutionise transportation energy and promote environmentally friendly development. The purpose of this study is to predict the state of health (SOH) of PEMFCs and provide guidance for fuel cell maintenance. Under changing power demand situations, a practical method based on the Fréchet distance is proposed to predict the SOH, along with an empirical model to differentiate between the running-in and degradation periods. The proposed method does not require complex and expensive testing instruments and has a relative error of approximately 4.3 %. A voltage drop prediction model is established for steady power demand situations using the particle swarm optimisation-extreme learning machine (PSO-ELM) algorithm. Different activation functions and hidden layer neurons are investigated to enhance prediction accuracy. This study shows that the model effectively tracks the decreasing trend in the transmission voltage of the PEMFC stack. Additionally, a comprehensive analysis framework is developed to address the issue of the possibility of missing system parameters in practical applications. The influence of the system parameters on voltage drop prediction is thoroughly analysed, and the necessary parameters for accurate prediction are defined, providing theoretical guidance for practical monitoring and data collection.