An Unscented Kalman Filter Based Approach for the HealthMonitoring and Prognostics of a Polymer Electrolyte Membrane Fuel Cel



Published Sep 23, 2012
Xian Zhang Pierluigi Pisu


Poor long-term performance and durability combined with high production and maintenance costs remain the main obstacles for the commercialization of the polymer electrolyte membrane fuel cell (PEMFC). While on-line diagnosis and operating condition optimization play an important role in addressing the durability issue of the fuel cell, health-monitoring and prognosis (or PHM) techniques are of equally great significance in terms of scheduling condition-based maintenance (CBM) to minimize repair and maintenance costs, the associated operational disruptions, and also the risk of unscheduled downtime for the fuel cell systems.

In this paper, an unscented Kalman filter (UKF) approach is proposed for the purpose of damage tracking and remaining useful life (RUL) prediction of a PEMFC. To implement this model-based PHM framework, a physics-based, prognostic-oriented catalyst degradation model is developed to characterize the fuel cell damage that establishes the relationship between the operating conditions and the degradation rate of the electro-chemical surface area (ECSA). The model complexity is kept minimal for on-line prognostic purpose. Simulation is carried out for validation of the proposed algorithm, using a more detailed catalyst degradation model.

How to Cite

Zhang, X., & Pisu, P. (2012). An Unscented Kalman Filter Based Approach for the HealthMonitoring and Prognostics of a Polymer Electrolyte Membrane Fuel Cel. Annual Conference of the PHM Society, 4(1).
Abstract 683 | PDF Downloads 417




Daigle, M., Saha, B., & Goebel, K. (2012). A comparison of filter-based approaches for model-based prognostics. 2012 IEEE Aerospace Conference (pp. 1 –10). Presented at the 2012 IEEE Aerospace Conference. doi:10.1109/AERO.2012.6187363
Darling, R.M., & Meyers, J. P. (2003). Kinetic model of platinum dissolution in PEMFCs. Journal of the Electrochemical Society, J. Electrochem. Soc. (USA), 150(11), 1523–7. doi:10.1149/1.1613669
Darling, Robert M., & Meyers, J. P. (2005). Mathematical model of platinum movement in PEM fuel cells. Journal of the Electrochemical Society, 152(1), A242A247. doi:10.1149/1.1836156 Franco, A. A., Schott, P., Jallut, C., & Maschke, B. (2007).A Multi-Scale Dynamic Mechanistic Model for the Transient Analysis of PEFCs. Fuel Cells, 7(2), 99–117. doi:10.1002/fuce.200500204
Franco, A.A., & Tembely, M. (2007). Transient multiscale modeling of aging mechanisms in a PEFC cathode. Journal of the Electrochemical Society, 154, B712.
Franco, Alejandro A., Coulon, R., Ferreira de Morais, R., Cheah, S. K., Kachmar, A., & Gabriel, M. A. (2009). Multi-scale Modeling-based Prediction of PEM FuelCells MEA Durability under Automotive Operating Conditions (pp. 65–79). ECS. doi:10.1149/1.3210560
Franco, Alejandro A., & Gerard, M. (2008). Multiscale Model of Carbon Corrosion in a PEFC: Coupling with Electrocatalysis and Impact on Performance Degradation. Journal of The Electrochemical Society, 155(4), B367. doi:10.1149/1.2838165
Franco, Alejandro A., Gerard, M., Guinard, M., Barthe, B., & Lemaire, O. (2008). Carbon Catalyst-Support Corrosion in Polymer Electrolyte Fuel Cells: Mechanistic Insights (pp. 35–55). ECS. doi:10.1149/1.3002807
Okada, T. (2003). Effect of Ionic contaminants. Handbook of Fuel Cells – Fundamentals, Technology and Applications (pp. 627–646). Wiley & Sons.

Orchard, M. E, & Vachtsevanos, G. J. (2009). A particlefiltering approach for on-line fault diagnosis and failure prognosis. Transactions of the Institute of Measurement and Control, 31(3-4), 221–246.

Orchard, M., Wu, B., & Vachtsevanos, G. (2005). A particle filtering framework for failure prognosis. 2005 World Tribology Congress III, September 12, 2005 September 16, 2005, Proceedings of the World Tribology Congress III - 2005 (pp. 883–884). Washington, D.C., United states: American Society of Mechanical Engineers.

Orchard, Marcos E., & Vachtsevanos, G. J. (2007). A particle filtering-based framework for real-time fault diagnosis and failure prognosis in a turbine engine. 2007 Mediterranean Conference on Control and Automation, MED, July 27, 2007 - July 29, 2007, 2007 Mediterranean Conference on Control and Automation, MED. Athens, Greece: Inst. of Elec. and Elec. Eng. Computer Society. doi:10.1109/MED.2007.4433871
Saha, B., & Goebel, K. (2008). Uncertainty management for diagnostics and prognostics of batteries using Bayesian techniques. Aerospace Conference, 2008 IEEE (pp. 18).

Saha, B., Goebel, K., Poll, S., & Christophersen, J. (2007).An integrated approach to battery health monitoring using bayesian regression and state estimation. Autotestcon, 2007 IEEE (pp. 646–653).

Saha, B., Goebel, K., Poll, S., & Christophersen, J. (2009).Prognostics Methods for Battery Health Monitoring Using a Bayesian Framework. IEEE Transactions on Instrumentation and Measurement, 58(2), 291–296. doi:10.1109/TIM.2008.2005965
Saxena, A., Celaya, J., Balaban, E., Goebel, K., Saha, B., Saha, S., & Schwabacher, M. (2008). Metrics for evaluating performance of prognostic techniques. Prognostics and Health Management, 2008. PHM 2008. International Conference on (pp. 1–17).

Saxena, A., Celaya, J., Saha, B., Saha, S., & Goebel, K.(2009). On applying the prognostic performance metrics. Proceedings of the annual conference of the prognostics and health management society. Retrievedfrom mission/2009/phmc_09_39.pdf

Schmittinger, W., & Vahidi, A. (2008). A review of the main parameters influencing long-term performance and durability of PEM fuel cells. Journal of Power Sources, J. Power Sources (Switzerland), 180(1), 1–14. doi:10.1016/j.jpowsour.2008.01.070
Shimoi, R., Aoyama, T., & Iiyama, A. (2009). Development of Fuel Cell Stack Durability based on Actual Vehicle Test Data: Current Status and Future Work ( No. 200901-1014). Warrendale, PA: SAE International. Retrieved from
Wan, E. A., & van der Merwe, R. (2002). The Unscented Kalman Filter. In S. Haykin (Ed.), Kalman Filtering and Neural Networks (pp. 221–280). John Wiley & Sons, Inc. Retrieved from .ch7/
summary Xian, Z. (2012). Prognostic and Health-Management Oriented Fuel Cell Modeling and On-line Supervisory System Development. Clemson University.
Technical Research Papers

Most read articles by the same author(s)