Lifetime Prediction Modeling of Automotive Proton Exchange Membrane Fuel Cells
Knowledge about the health conditions and expected lifetime of an operating fuel cell stack is essential to system control and maintenance of a fuel cell vehicle. To quickly and accurately estimate a stack’s lifetime, a data-driven prediction model for proton exchange membrane fuel cells (PEMFCs) is proposed in this study. In this model, the voltage output of the fuel cell stack is taken as the lifetime evaluation index. Two methods are used to establish the lifetime decay evaluation criteria of the PEMFC stack, i.e., (1) Least Squares Fitting (LSF) method that establishes the standard for stack voltage degradation behavior, and (2) Back Propagation (BP) neural network that learns the stack’s voltage decay characteristics and establishes the standard for the stack’s voltage degradation behavior. The Autoregressive Moving Average (ARMA) time series model is then employed to learn part of the known decay behavior of stack voltage so as to predict future stack decay.