L. Eudy and M. B. Post, Fuel cell buses in us transit fleets: Current status 2017, National Renewable Energy Lab.(NREL), 2017.

J. Wang, System integration, durability and reliability of fuel cells: Challenges and solutions, Applied Energy, vol.189, pp.460-479, 2017.

N. M. Vichare and M. G. Pecht, Prognostics and health management of electronics, IEEE Transactions on Components and Packaging Technologies, vol.29, issue.1, pp.222-229, 2006.

M. Bressel, M. Hilairet, D. Hissel, and B. O. Bouamama, Remaining useful life prediction and uncertainty quantification of proton exchange membrane fuel cell under variable load, IEEE Transactions on Industrial Electronics, vol.63, issue.4, pp.2569-2577, 2016.
URL : https://hal.archives-ouvertes.fr/hal-02380397

X. Zhang and P. Pisu, An unscented kalman filter based approach for the health-monitoring and prognostics of a polymer electrolyte membrane fuel cell, vol.3, pp.1-9, 2012.

J. Wu, X. Z. Yuan, J. J. Martin, H. Wang, J. Zhang et al., A review of pem fuel cell durability: Degradation mechanisms and mitigation strategies, Journal of Power Sources, vol.184, issue.1, pp.104-119, 2008.

C. Lim, L. Ghassemzadeh, F. Van-hove, M. Lauritzen, J. Kolodziej et al., Membrane degradation during combined chemical and mechanical accelerated stress testing of polymer electrolyte fuel cells, Journal of Power Sources, vol.257, pp.102-110, 2014.

R. Silva, R. Gouriveau, S. Jemeï, D. Hissel, L. Boulon et al., Proton exchange membrane fuel cell degradation prediction based on adaptive neuro-fuzzy inference systems, International Journal of Hydrogen Energy, vol.39, issue.21, pp.11-128, 2014.
URL : https://hal.archives-ouvertes.fr/hal-01050920

K. Javed, R. Gouriveau, N. Zerhouni, and D. Hissel, Improving accuracy of long-term prognostics of pemfc stack to estimate remaining useful life, 2015 IEEE International Conference on Industrial Technology (ICIT), pp.1047-1052, 2015.

S. Morando, S. Jemei, R. Gouriveau, N. Zerhouni, and D. Hissel, Fuel cells prognostics using echo state network, pp.2013-2052
URL : https://hal.archives-ouvertes.fr/hal-00907631

, Annual Conference of the IEEE Industrial Electronics Society, pp.1632-1637, 2013.

S. Yin, X. Xie, J. Lam, K. C. Cheung, and H. Gao, An improved incremental learning approach for kpi prognosis of dynamic fuel cell system, IEEE Transactions on Cybernetics, vol.46, issue.12, pp.3135-3144, 2016.

Y. Wu, E. Breaz, F. Gao, and A. Miraoui, A Modified Relevance Vector Machine for PEM Fuel-Cell Stack Aging Prediction, IEEE Transactions on Industry Applications, vol.52, issue.3, pp.2573-2581, 2016.
URL : https://hal.archives-ouvertes.fr/hal-02380398

L. Zhu and J. Chen, Prognostics of pem fuel cells based on gaussian process state space models, Energy, vol.149, pp.63-73, 2018.

M. Jouin, R. Gouriveau, D. Hissel, M. C. Péra, and N. Zerhouni, Joint particle filters prognostics for proton exchange membrane fuel cell power prediction at constant current solicitation, IEEE Transactions on Reliability, vol.65, issue.1, pp.336-349, 2016.

M. Ibrahim, N. Y. Steiner, S. Jemei, and D. Hissel, Wavelet-Based Approach for Online Fuel Cell Remaining Useful Lifetime Prediction, IEEE Transactions on Industrial Electronics, vol.63, issue.8, pp.5057-5068, 2016.

H. Liu, J. Chen, M. Hou, Z. Shao, and H. Su, Data-based short-term prognostics for proton exchange membrane fuel cells, International Journal of Hydrogen Energy, vol.42, issue.32, pp.20-791, 2017.

Z. Li, S. Jemei, R. Gouriveau, D. Hissel, and N. Zerhouni, Remaining useful life estimation for pemfc in dynamic operating conditions, 2016 IEEE Vehicle Power and Propulsion Conference (VPPC), pp.1-6, 2016.
URL : https://hal.archives-ouvertes.fr/hal-02380268

S. Lira, V. Puig, J. Quevedo, and A. Husar, Lpv observer design for pem fuel cell system: Application to fault detection, Journal of Power Sources, vol.196, issue.9, pp.4298-4305, 2010.

A. Golabi, N. Meskin, R. Tóth, and J. Mohammadpour, A Bayesian Approach for LPV Model Identification and Its Application to Complex Processes, IEEE Transactions on Control Systems Technology, vol.25, issue.6, pp.2160-2167, 2017.

R. Tóth, . Laurain, . Vincent, W. Zheng, . Xing et al., Model structure learning: A support vector machine approach for LPV linear-regression models, pp.3192-3197, 2011.

H. Wang and D. Hu, Comparison of svm and ls-svm for regression, ICNN&B'05. International Conference on Neural Networks and Brain, vol.1, pp.279-283, 2005.

B. Schrauwen, D. Verstraeten, and J. Van-campenhout, An overview of reservoir computing: theory, applications and implementations, Proceedings of the 15th European Symposium on Artificial Neural Networks, pp.471-482, 2007.

G. Holzmann and H. Hauser, Echo state networks with filter neurons and a delay&sum readout, Neural Networks, vol.23, issue.2, pp.244-256, 2010.

M. Luko?evi?ius, A practical guide to applying echo state networks, Neural networks: Tricks of the trade, pp.659-686, 2012.