Aging Prognosis and Uncertainty Quantification of Hydrogen Fuel Cell Based on Semiempirical and Equivalent Circuit Joint Model

Under varying operational conditions, prognostics of degradation for the proton exchange membrane fuel cell (PEMFC) is a vital, intricate endeavor, pivotal for predictive servicing and condition monitoring. Nevertheless, the unpredictability of time-varying operating regimes indices, as well as the limitation of transient lifespan prognosis mechanisms, pose significant challenges in the practical prediction process. To boost the forecasting precision of degradation methods, this study introduces the symphysis dynamic factor (SDF), a single health indicator (HI) created by blending two pieces of information. First, it needs to compute the factor-I deriving from feature parameters by establishing an equivalent circuit model (ECM). Second, a correlation model of aging PEMFC is derived from the semiempirical equation and ECM parameters, enabling the extraction of the correlation coefficient to compute factor-II. Then, the SDF is obtained by combining factor-I with factor-II. Employing the preconfigured current load and thermal conditions as variables, the particle filter (PF) estimates the operating voltage and the SDF, while quantifying the uncertainty of aging factor estimation. Following this, the decoupled echo state network is realized to employ unlimited period prediction, allowing for the estimation of the leftover useful duration. The efficacy and precision regarding the novel aging metric and the combined forecasting technique introduced have been validated under time-varying operating states.