Nonlinear distributed-parameter observer design for efficient estimation of internal temperature profiles in polymer electrolyte membrane fuel cells

Abstract

This paper presents a methodology to design a nonlinear distributed-parameter observer to estimate internal temperature profiles in polymer electrolyte membrane fuel cells. Accurate knowledge of the spatial temperature distributions allows beneficial insight into the cell’s condition since the temperature is strongly coupled to other cell states. The extended Kalman filter-based observer employs a high-fidelity nonlinear non-isothermal distributed-parameter model for predicting internal temperature distributions. A reduced-order model, preserving the major dynamics and coupling effects of the original model, is utilized for real-time capable and feasible state correction. To improve the estimation, the observer scheme is augmented to simultaneously estimate selected material parameters along the temperature profiles online. In a simulation experiment, the observer is compared to an open-loop simulation benchmark. Further, the consistency of the estimation is investigated. The observer methodology is validated using a high-resolution simulated reality since internal temperature distributions are inaccessible in a real system. The validation demonstrates the observer’s accuracy in estimating the unmeasurable internal temperature distributions. The results substantiate the observer’s advantage over open-loop model simulations and outline the importance of temperature profile estimation for meaningful condition monitoring.