@article {7201,
	title = {A data-driven method for multi-step-ahead prediction and long-term prognostics of proton exchange membrane fuel cell},
	journal = {Applied Energy},
	volume = {313},
	number = {1},
	year = {2022},
	abstract = {Fuel cell technology\&nbsp;has been rapidly developed in the last decade owing to its clean characteristic and high efficiency.\&nbsp;Proton exchange membrane fuel cells\&nbsp;(PEMFCs) are increasingly used in transportation applications and small stationary applications; however, the cost and the unsatisfying durability of the PEMFC stack have limited their successful\&nbsp;commercialization\&nbsp;and market penetration. In recent years, thanks to the availability and the quality of emerging data of PEMFCs, digitization is happening to offer possibilities to increase the productivity and the flexibility in\&nbsp;fuel cell applications. Therefore, it is crucial to clarify the potential of digitization measures, how and where they can be applied, and their benefits. This paper focuses on the\&nbsp;degradation performance\&nbsp;of the PEMFC stacks and develops a data-driven intelligent method to predict both the short-term and long-term degradation. The dilated\&nbsp;convolutional neural network\&nbsp;is for the first time applied for predicting the time-dependent\&nbsp;fuel cell performance\&nbsp;and is proved to be more efficient than other\&nbsp;recurrent networks. To deal with the long-term performance uncertainty, a conditional neural network is proposed. Results have shown that the proposed method can predict not only the degradation tendency, but also contain the degradation behaviour dynamics.},
	url = {https://www.sciencedirect.com/science/article/abs/pii/S0306261922002756},
	author = {Benaggoune, Khaled and Yue, Meiling and Jemei, Samir and Zerhouni, Noureddine}
}
@proceedings {7254,
	title = {A Knowledge Transfer Approach for Online PEMFC Degradation prediction with Uncertainty Quantification},
	journal = {12th International Conference on Power, Energy and Electrical Engineering (CPEEE)},
	year = {2022},
	address = {Shiga, Japan},
	abstract = {Proton Exchange Membrane Fuel Cells (PEMFCs) are a key challenger for the world{\textquoteright}s future clean and renewable energy solution. Yet, fuel cells are susceptible to operating conditions and hydrogen impurities, leading to performance loss over time in service. Hence, performance degradation prediction is gaining attention recently for fuel cell system reliability. In this work, we present a knowledge transfer approach for online voltage drop prediction. A dual-path convolution neural network is proposed to extract linearity and non-linearity from historical data and performs multi-steps ahead prediction with uncertainty quantification. Online voltage prediction is then evaluated with and without knowledge transfer using two different PEMFC datasets. Results indicate that our proposed approach with transfer knowledge can predict the voltage drop accurately with a small uncertainty range compared to the conventional approach.},
	url = {https://ieeexplore.ieee.org/document/9738717},
	author = {Benaggoune, Khaled and Meiling, Yue and Jemei, Samir and Zerhouni, Noureddine}
}
