A fusion prognostics strategy for fuel cells operating under dynamic conditions

Transportation-oriented proton exchange membrane fuel cells (PEMFC) are attracting much attention, but the strong dynamic operating conditions in transportation applications limit the durability improvement and wide commercialization of fuel cells (FC). Prognostics dedicated to predicting the FC rem...

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Bibliographic Details
Main Authors: Dou, M. (Author), Li, Z. (Author), Liang, B. (Author), Outbib, R. (Author), Wang, C. (Author), Zhao, D. (Author)
Format: Article
Language:English
Published: Elsevier B.V. 2022
Subjects:
Online Access:View Fulltext in Publisher
LEADER 03018nam a2200481Ia 4500
001 10.1016-j.etran.2022.100166
008 220517s2022 CNT 000 0 und d
020 |a 25901168 (ISSN) 
245 1 0 |a A fusion prognostics strategy for fuel cells operating under dynamic conditions 
260 0 |b Elsevier B.V.  |c 2022 
856 |z View Fulltext in Publisher  |u https://doi.org/10.1016/j.etran.2022.100166 
520 3 |a Transportation-oriented proton exchange membrane fuel cells (PEMFC) are attracting much attention, but the strong dynamic operating conditions in transportation applications limit the durability improvement and wide commercialization of fuel cells (FC). Prognostics dedicated to predicting the FC remaining useful life (RUL) can facilitate the early provision of control/maintenance programs to improve durability and reduce costs. However, credible degradation indexes for prognostics are difficult to access or observe directly from the FC operating under dynamic conditions. Moreover, the long-term prediction performance of the state-of-the-art prognostics models is often not satisfactory. This paper proposes a fusion prognostics strategy to address these challenges. Specifically, the system dynamics is identified by using an electrochemical mechanism model and the degradation indexes are extracted based on the identified model parameters. Subsequently, a reduced-dimensional symbolic representation based long short-term memory network is developed for predicting the evolution of degradation. The proposed approach is validated using the long-term accelerated stress test data of a vehicle-oriented PEMFC. The results show that the degradation mechanism model can be used to identify degradation indexes in dynamic operating conditions. Based on the prognostics model, accurate RUL prediction can further be achieved over the extracted degradation indexes. © 2022 Elsevier B.V. 
650 0 4 |a Degradation 
650 0 4 |a Degradation index 
650 0 4 |a Degradation indexes 
650 0 4 |a Degradation model 
650 0 4 |a Durability 
650 0 4 |a Dynamic condition 
650 0 4 |a Dynamic conditions 
650 0 4 |a Dynamics 
650 0 4 |a Electrochemical degradation 
650 0 4 |a Electrochemical degradation model 
650 0 4 |a Electrochemical degradation model 
650 0 4 |a Failure (mechanical) 
650 0 4 |a Failure range 
650 0 4 |a Failure range 
650 0 4 |a Forecasting 
650 0 4 |a Gas fuel purification 
650 0 4 |a Operating condition 
650 0 4 |a Prognostic modeling 
650 0 4 |a Proton exchange membrane fuel cells 
650 0 4 |a Proton exchange membrane fuel cells (PEMFC) 
650 0 4 |a Proton-exchange membranes fuel cells 
650 0 4 |a Remaining useful life 
650 0 4 |a Remaining useful lives 
650 0 4 |a Systems engineering 
700 1 |a Dou, M.  |e author 
700 1 |a Li, Z.  |e author 
700 1 |a Liang, B.  |e author 
700 1 |a Outbib, R.  |e author 
700 1 |a Wang, C.  |e author 
700 1 |a Zhao, D.  |e author 
773 |t eTransportation