With the goal of on-line diagnosis for automotive applications in mind, a real-time model of polymer electrolyte membrane (PEM) fuel cell is developed. The model draws from the authors’ previous modeling effort in this area and extends its domain to incorporate transport under the lands. Transport in the catalyst and micro-porous layers, which were previously omitted, are also included in the model. Membrane water transport model is modified accordingly. Moreover, a recently developed homogeneous catalyst layer model is used to describe local oxygen transport resistance in the cathode catalyst layer. Computational efficiency is achieved through spatio-temporal decoupling of the problem, which simplifies the handling of the nonlinear terms. This computational efficiency is demonstrated by a set of simulations that resemble operation under conditions encountered in automotive applications. Moreover, simulation results of the model are in qualitative agreement with earlier computationally intensive modeling studies as well as experimental observations. The current modeling study demonstrates a significant potential for using relatively high-fidelity physics-based models on-line to improve fuel cell performance and durability, which can have a profound impact on its commercialization.
- Dynamic Systems and Control Division
A Real-Time Pseudo-2D Bi-Domain Model of PEM Fuel Cells for Automotive Applications
- Views Icon Views
- Share Icon Share
- Search Site
Goshtasbi, A, Pence, BL, & Ersal, T. "A Real-Time Pseudo-2D Bi-Domain Model of PEM Fuel Cells for Automotive Applications." Proceedings of the ASME 2017 Dynamic Systems and Control Conference. Volume 1: Aerospace Applications; Advances in Control Design Methods; Bio Engineering Applications; Advances in Non-Linear Control; Adaptive and Intelligent Systems Control; Advances in Wind Energy Systems; Advances in Robotics; Assistive and Rehabilitation Robotics; Biomedical and Neural Systems Modeling, Diagnostics, and Control; Bio-Mechatronics and Physical Human Robot; Advanced Driver Assistance Systems and Autonomous Vehicles; Automotive Systems. Tysons, Virginia, USA. October 11–13, 2017. V001T25A001. ASME. https://doi.org/10.1115/DSCC2017-5053
Download citation file: