Multi-timescale prediction of lifetime and operating temperatures of PEMFC system by hierarchical echo state network

The proton exchange membrane fuel cells (PEMFC) system is an environment-friendly power conversion equipment which can be employed in different applications. Prediction of the lifetime can help the users take some rewarding actions to extend the service life of the PEMFC system. Health indicators (HIs) serve to signify the extent of degradation under varying operational conditions. Given the constrained nature of dynamic HIs, a novel robust dynamic HI termed the relative power-loss rate (RPLR) is proposed in the paper. Besides the HIs, the output temperature of reactants (i.e., hydrogen and oxygen) are two important degradation-related operating parameters because they could reflect the electrochemical reaction process to some extent. Then the data-driven prediction method of hierarchical echo state network (HESN) is premiere proposed to predict the output temperatures of reactants and the lifespan at the same time. The first echo state network (ESN) is utilized for short-term temperature prediction, whereas a subsequent cascaded ESN is employed for long-term lifetime forecasting. Incorporating the predicted output temperature as an additional input into the second ESN within the Hierarchical ESN (HESN) structure enhances the accuracy of lifetime prediction. The experimental results reveal that hydrogen or air output temperature significantly impacts PEMFC degradation rates, increasing internal resistance and polarization loss, and causing intermittent temperature fluctuations within the stack. The coupling between hydrogen output temperature and PEMFC aging is particularly notable. Compared to methods without temperature input, the HESN model with multi-step prediction of hydrogen or air output temperature improves convergence speed and accuracy of RUL estimation for commercial development of PEMFC.