DLINE Journals portal

[1] Energy, Transport and Ghg Emissions Trends to 2050.

[2] Singhal, S.C. and Kendall, K. (2003). High Temperature Solid Oxide Fuel Cells: Fundamentals, Design, and Applications. Elsevier: Amsterdam.

[3] Barbir, F. (2013) PEM fuel cells. Theory into Practice. Elsevier: Amsterdam.

[4] Li, Q., Aili, D. and Jensen, H., editors (2016). High Temperature Polymer Electrolyte Membrane Fuel Cells. Springer: Berlin.

[5] Kirubakaran, A., Jain, S. and Nema, R.K. (2009) A review on fuel cell technologies and power electronic interface. Renewable and Sustainable Energy Reviews, 13, 2430–2440.

[6] Barelli, L., Barluzzi, E. and Bidini, G. (2013) Diagnosis methodology and technique for solid oxide fuel cells: A review. International Journal of Hydrogen Energy, 38, 5060–5074.

[7] Jouin, M., Gouriveau, R., Hissel, D., Péra, M.-C. and Zerhouni, N. (2013) [Online]. Available: www.sciencedirect.com/science/article/pii/S036031991302274X Prognostics and health management of pemfc – State of the art and remaining challenges. International Journal of Hydrogen Energy, 38, 15307–15317.

[8] Lebold, M. and Thurston, M. (2001) Open standards for condition-based maintenance and prognostic systems 5th Annual Maintenance and Reliability Conference.

[9] Marra, D., Pianese, C., Polverino, P. and Sorrentino, M. (2016). Models for Solid Oxide Fuel Cell Systems: Exploitation of Models Hierarchy for Industrial Design of Control and Diagnosis Strategies. Springer: Berlin.

[10] Sorce, A., Greco, A., Magistri, L. & Costamagna, P. (2014) FDI oriented modeling of an experimental SOFC system, model validation and simulation of faulty states. Applied Energy, 136, 894–908.

[11] Sorrentino, M., Marra, D., Pianese, C., Guida, M., Postiglione, F., Wang, K. and Pohjoranta, A. (2014, ati 2013) On the use of neural networks and statistical tools for nonlinear modeling and on-field diagnosis of solid oxide fuel cell stacks. Energy Procedia, 45, 298–307- 68th Conference of the Italian Thermal Machines Engineering Association.

[12] Pahon, E., Yousfi Steiner, N.Y., Jemei, S., Hissel, D., Péra, M.C., Wang, K. and Moçoteguy, P. (2016) Solid oxide fuel cell fault diagnosis and ageing estimation based on wavelet transform approach. International Journal of Hydrogen Energy, 41, 13678–13687.

[13] Boškoski, P., Debenjak, A. and Mileva, B. (2017) Boshkoska, Fast Electrochemical Impedance Spectroscopy as a Statistical Condition Monitoring Tool, ser. SpringerBriefs in Applied Sciences and Technology. Springer International Publishing.

[14] Polverino, P., Sorrentino, M. and Pianese, C. (2017) A model-based diagnostic technique to enhance faults isolability in solid oxide fuel cell systems. Applied Energy, 204, 1198–1214.

[15] Rezaei Niya, S.M.R., Phillips, R.K. and Hoorfar, M. (2016) Process modeling of the impedance characteristics of proton exchange membrane fuel cells. Electrochimica Acta, 191, 594–605.

[16] Heinzmann, M., Weber, A. and Ivers-Tiffée, E. (2018) Advanced impedance study of polymer electrolyte membrane single cells by means of distribution of relaxation times. Journal of Power Sources, 402, 24–33.

[17] A Wavelet Tour of Signal Processing (Third Edition) (2009) (edited by M. Stéphane). Academic Press: Cambridge, USA.

[18] Iatsenko, D., McClintock, P.V.E. and Stefanovska, A. (2015) [Online]. Available: https://link.aps.org/doi/10.1103/ Nonlinear mode decomposition: A noise-robust, adaptive decomposition method. Physical Review. E, Statistical, Nonlinear, and Soft
Matter Physics, 92, 032916 [PubMed: 26465549]. PhysRevE.92.032916

[19] Ciucci, F. and Chen, C. (2015) [Online]. Available: www.sciencedirect.com/science/article/pii/S0013468615007203 Analysis of electrochemical impedance spectroscopy data using the distribution of relaxation times: A bayesian and hierarchical bayesian approach. Electrochimica Acta, 167, 439–454.

[20] Fuoss, R.M. and Kirkwood, J.G. (1941) Electrical properties of solids. viii. dipole moments in polyvinyl chloride-diphenyl systems*. Journal of the American Chemical Society, 63, 385–394.

[21] Caliandro, P., Diethelm, S. and Van herle, J. (2017) [Online]. Available: https://onlinelibrary.wiley.com/doi/abs/10.1002/fuce.201600196 Triode Solid Oxide Fuel Cell Operation Under Sulfur-Poisoning Conditions. Fuel Cells, 17, 457–463.

[22] Kruschke, J.K. (2010). Do in Bayesian Data Analysis.

[23] Polverino, P., Pianese, C., Sorrentino, M. and Marra, D. (2015) Model-based development of a fault signature matrix to improve solid oxide fuel cell systems on-site diagnosis. Journal of Power Sources, 280, 320–338.

[24] Jouin, M., Gouriveau, R., Hissel, D., Péra, M.-C. and Zerhouni, N. (2016) Degradations analysis and aging modeling for health assessment and prognostics of PEMFC. Reliability Engineering and System Safety, 148, 78–95.

[25] Zhang, X. and Pisu, P. (2012) An unscented kalman filter based approach for the health monitoring and prognostics of a polymer electrolyte membrane fuel cell. In: Annual Conference of Prognostics and Health Management Society.

[26] Kimotho, J.K., Meyer, T. and Sextro, W. (2014) PEM fuel cell prognostics using particle filter with model parameter adaptation. In: IEEE Conference on Prognostics and Health Management, pp. 1–6.

[27] Silva, R.E., Gouriveau, R., Jemeï, S., Hissel, D., Boulon, L., Agbossou, K. and Yousfi Steiner, N. (2014) Proton exchange membrane fuel cell degradation prediction based on adaptive neuro-fuzzy inference systems. International Journal of Hydrogen Energy, 39, 11128–11144, p. 11.

[28] Morando, S., Jemei, S., Gouriveau, R., Zerhouni, N. and Hissel, D. (2014) Fuel cells remaining useful lifetime forecasting using echo state network. In: IEEE Vehicle Power and Propulsion Conference, pp. 1–6.

[29] Javed, K., Gouriveau, R. and Zerhouni, N. (2015) Data-driven prognostics of proton exchange membrane fuel cell stack with constraint based summation-wavelet extreme learning machine. In: 6th international conference on fundamentals and development of fuel cells, pp. 1–8.

[30] Hochstein, A., Ahn, H., Leung, Y. and Denesuk, M. (2014) Switching vector autoregressive models with higher-order regime dynamics. In: IEEE Conference on Prognostics and Health Management (PHM), no. 1-10.

[31] Dolenc, B., Boškoski, P., Stepanèiè, M., Pohjoranta, A. and Juricic, Ð. (2017) State of health estimation and remaining useful life prediction of solid oxide fuel cell stack. Energy Conversion and Management, 148, 993–1002.

[32] Zaccaria, V., Tucker, D. and Traverso, A. (2016) A distributed real-time model of degradation in a solid oxide fuel cell, Part i: Model characterization. Journal of Power Sources, 311, 175–181.

[33] Dolenc, B. (2019) Characterisation of coupling functions between process and degradation dynamics in solid oxide fuel cells. Philosophical Transactions of the Royal Society of London Series. Part A.