Efficient model selection for real-time adaptive cold start strategy of a fuel cell system on vehicular applications

Amamou, A., Kandidayeni, M., Macias, A., Boulon, L. et Kelouwani, S. (2020). Efficient model selection for real-time adaptive cold start strategy of a fuel cell system on vehicular applications. International Journal of Hydrogen Energy . ISSN 0360-3199 DOI 10.1016/j.ijhydene.2020.04.253

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Résumé

The PEMFC maximum power is greatly influenced by subfreezing temperature and degradation phenomena. Therefore, a dependable model is required to estimate the power with respect to the variation of the operating conditions and state of health. Semi-empirical models are potent tools in this regard. Nonetheless, there is not much information about their cold environment reliability. This paper comprehensively compares the performance of some models (already tested in normal ambient temperature) in subfreezing condition to introduce the most reliable one for PEMFC cold start-up application. Firstly, seven models are compared regarding voltage losses and precision. Subsequently, the three most dependable ones are selected and experimentally compared at sub-zero temperature in terms of polarization curve estimation for three PEMFCs with different degradation levels. The results of this study indicate that the model introduced by Amphlett et al. has a superior performance compared to other ones regarding the characteristic's estimation in below-zero temperature.

Type de document:	Article
Mots-clés libres:	cold start experimental approach online identification proton exchange membrane fuel cell semi-empirical model
Date de dépôt:	14 sept. 2020 15:27
Dernière modification:	14 sept. 2020 15:27
Version du document déposé:	Post-print (version corrigée et acceptée)
URI:	https://depot-e.uqtr.ca/id/eprint/9250

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