Soft Sensor for Real-Time Monitoring of Automotive PEM Fuel Cell Systems

Abstract

System strategies to enhance the performance and mitigate degradation of automotive polymer electrolyte membrane (PEM) fuel cells require knowledge of the internal states such as the membrane hydration and its temperature [1]. This information is rarely available through direct measurements onboard the vehicle, since measuring such quantities requires advanced hardware sensors that can significantly add to the system costs. Therefore, accurate mathematical models are necessary to provide the critical information about internal states. These models should have a high enough fidelity to describe the salient physical phenomena with an acceptable degree of accuracy, while maintaining a high level of computational efficiency so that the required computations can be carried out online. Despite significant efforts in the PEM fuel cell modeling community, balancing the computational cost and model fidelity requirements remains a challenge. Most of the models proposed to date obtain high fidelity by solving detailed transport equations on a fine spatial grid. These models have been instrumental in clarifying the role of complex and coupled transport phenomena in an operating fuel cell [2], [3]. Nevertheless, they typically require significant computational resources and are not suitable for online applications. On the other hand, available computationally-efficient models often disregard multiple important physical phenomena to achieve high performance [4], [5]. To fill this gap, we have developed a model that takes most of the important physical phenomena into account and achieves computational efficiency by spatio-temporal decoupling of the problem [6], [7]. In this talk, we will present our recent efforts on further improving the model by incorporating mass transport losses in the cathode catalyst layer and designing a new iterative scheme that enables simulation of counter-flow configuration. Moreover, we present our most recent results on model parameterization and validation with different performance datasets. The results indicate the computational efficiency of the model and its capability to reproduce experimental performance measurements with high accuracy. * B.L. Pence is currently with the department of Mechanical Engineering at the Brigham Young University, Rexburg, ID. Acknowledgement: Financial support for this work was provided by Ford Motor Company. [1] A. Goshtasbi and T. Ersal, “LQ-MPC Design for Degradation-Conscious Control of PEM Fuel Cells,” in Proceedings of the 2019 American Control Conference, 2019. [2] L. Hao, K. Moriyama, W. Gu, and C.-Y. Wang, “Three dimensional computations and experimental comparisons for a large-scale proton exchange membrane fuel cell,” J. Electrochem. Soc., vol. 163, no. 7, pp. F744–F751, 2016. [3] A. Goshtasbi, P. Garcia-Salaberri, J. Chen, K. Talukdar, D. G. Snachez, and T. Ersal, “Through-the-Membrane Transient Phenomena in PEM Fuel Cells: A Modeling Study,” J. Electrochem. Soc., vol. 166, no. 7, 2019. [4] A. Headley, V. Yu, R. Borduin, D. Chen, and W. Li, “Development and experimental validation of a physics-based pem fuel cell model for cathode humidity control design,” IEEE/ASME Trans. Mechatronics, vol. 21, no. 3, pp. 1775–1782, 2016. [5] M. Grötsch and M. Mangold, “A two-phase PEMFC model for process control purposes,” Chem. Eng. Sci., vol. 63, no. 2, pp. 434–447, 2008. [6] A. Goshtasbi, B. L. Pence, and T. Ersal, “A real-time pseudo-2D bi-domain model of PEM fuel cells for automotive applications,” in ASME 2017 Dynamic Systems and Control Conference, 2017, p. V001T25A001-V001T25A001. [7] A. Goshtasbi, B. L. Pence, and T. Ersal, “Computationally efficient pseudo-2D non-isothermal modeling of polymer electrolyte membrane fuel cells with two-phase phenomena,” J. Electrochem. Soc., vol. 163, no. 13, pp. F1412–F1432, 2016.