Degradation prediction of hydrogen fuel cell based on dynamic comprehensive aging indicator and hybrid forecasting method

Predicting the degradation of proton exchange membrane fuel cell (PEMFC) is a critical yet complex task under dynamic operating conditions, essential for their prognostics and health management. However, the uncertainty of the dynamic condition index and the localization of the short-term lifetime forecasting mechanism leads to many limitations in the actual predicting process. To improve the actual degradation prediction ability of prognostic methods, the comprehensive aging indicator (CAI) and the hybrid forecasting method which combines real-time estimation and long-term prediction under dynamic working conditions are proposed in this study. To be specific, firstly, the equivalent circuit model (ECM) is constructed to extract the feature parameters. Afterward, the correlation model can be obtained from the semi-empirical equation of fuel cell aging and ECM parameters, and then the initial parameters of the model are extracted. Finally, the working voltage and CAI of PEMFC are estimated by the extended Kalman filter using the pre-planned working current and temperature as input. Then, the long-term prediction is realized by the cascaded echo state network, and the remaining useful life is estimated. The effectiveness and accuracy of the proposed aging indicator and hybrid long-term lifetime prediction method are verified under dynamic working conditions.