Degradation Prediction of PEM Fuel Cell Stack Based on Multiphysical Aging Model With Particle Filter Approach

• Published in: IEEE transactions on industry applications Vol. 53; no. 4; pp. 4041 - 4052
• Main Authors: Daming Zhou, Yiming Wu, Fei Gao, Breaz, Elena, Ravey, Alexandre, Miraoui, Abdellatif
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.07.2017; The Institute of Electrical and Electronics Engineers, Inc. (IEEE); Institute of Electrical and Electronics Engineers
• Subjects: Aging; Automatic; Curve fitting; Degradation; Electric power; Electrochemical activation; Electrodes; Engineering Sciences; Extrapolation; Extrapolation method; Fluid mechanics; Fuel cells; Fuels; Mass transfer; Mathematical model; Mathematical models; Mechanics; multiphysical aging model; particle filter (PF); Physics; Polarization; Predictive models; Proton exchange membrane fuel cells; proton-exchange-membrane fuel cell (PEMFC); Robustness; Thermics
• ISSN: 0093-9994; 1939-9367
• DOI: 10.1109/TIA.2017.2680406

In this paper, a novel degradation prediction model for proton-exchange-membrane fuel cell (PEMFC) performance is proposed based on a multiphysical aging model with particle filter (PF) and extrapolation approach. The proposed multiphysical aging model considers major internal physical aging phenomena of fuel cells, including fuel cell ohmic losses, reaction activity losses, and reactants mass transfer losses. Furthermore, in order to obtain accurate values of electrochemical activation losses under a variable load profile, a bisection solver is presented to solve the implicit Butler-Volmer equation. The proposed aging model is initialized at first by fitting the PEMFC polarization curve at the beginning of lifetime. During the prediction process, the aging dataset is then divided into two parts, learning and prediction phases. The PF framework is used to study the degradation characteristics and update the aging parameters during the learning phase. The suitable fitting curve functions are then selected to satisfy the degradation trends of trained aging parameters, and used to further extrapolate the future values of aging parameters in the prediction phase. By using these extrapolated aging parameters, the prediction results are thus obtained from the proposed aging model. Three experimental validations with different aging testing profiles have been performed. The results demonstrate the robustness and advantages of the proposed prediction method.