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• W4297882578 abstract "Proton exchange membrane fuel cell (PEMFC) systems are emerging as one of the most promising solutions for carbon neutrality in transportation, however, durability problem remain a major obstacle to their large-scale commercialization. Developing an accurate model to predict the short-term aging state and long-term durability level of PEMFC is conducive to formulating optimal measures in time and further to improving durability. In this paper, the short-term voltage degradation and long-term durability level are predicted and evaluated by combining machine learning (ML) methods with time-series voltage degradation data obtained under vehicle dynamic load. In the short-term forecasting stage, the long short-term memory (LSTM) model, the support vector regression (SVR) model, and the LSTM-SVR combination model are developed respectively, and the prediction results of the three models are compared and evaluated. The LSTM-SVR combined model achieved the best short-term prediction accuracy of 96.6%, followed by LSTM model (95.5%). Considering the difficulty of model deployment and the feasibility of quickly assessing long-term durability in practical application, a LSTM-based model rolling prediction mechanism is proposed to rapidly evaluate the long-term durability index of the developed PEMFC system, the results show that the proposed forecasting model and method can accurately predict the voltage degradation trend and quickly evaluate the long-term durability level, which not only makes contributions to greatly saving the durability R&D costs, but also provides the possibility to adjust the optimization measures in real-time to further improve the durability according to the prediction results." @default.
• W4297882578 created "2022-10-01" @default.
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• W4297882578 date "2022-11-01" @default.
• W4297882578 modified "2023-10-14" @default.
• W4297882578 title "Durability estimation and short-term voltage degradation forecasting of vehicle PEMFC system: Development and evaluation of machine learning models" @default.
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• W4297882578 doi "https://doi.org/10.1016/j.apenergy.2022.119975" @default.
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