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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.

Head-up Display (HUD) system can avoid drivers’ distraction on dashboard and effectively reduce collisions caused by emergency events, which is gradually being realized by researchers around the world. However, the current HUD only displays information like speed, fuel consumption, other information like acceleration and braking can’t be displayed yet. This research will use the indicator symbol‘s color and position change to remind drivers to brake or accelerate. Drivers can do driving operation timely and accurately. The system has the advantages of safety, intuition and real-time. The vehicle safe speed is calculated according to the road parameters, like adhesion coefficient and slope, and vehicle parameters, such as vehicle mass and centroid. Then, the appropriate braking operations are obtained by combining the vehicle driving state. The braking information is corresponded to the color and position change of the indicator symbol to prompt the drivers by the HUD interface.