Your browser doesn't support javascript.
loading
Degradation prediction of fuel cell systems based on different operating conditions in dynamic cycling condition.
Liu, Xiaohui; Chen, Jianhua; Wei, Yian; Liu, Shengjie; Zhou, Yilin.
Afiliación
  • Liu X; School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing, China.
  • Chen J; School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing, China.
  • Wei Y; School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing, China.
  • Liu S; School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing, China.
  • Zhou Y; School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing, China.
Heliyon ; 10(15): e34783, 2024 Aug 15.
Article en En | MEDLINE | ID: mdl-39144928
ABSTRACT
In this paper, the degradation of PEMFC under different operating conditions in dynamic cycle condition is studied. Firstly, according to the failure mechanism of PEMFC, various operating conditions in dynamic cycle condition are classified, and the health indexes are established. Simultaneously, the rates and degrees of the output voltage decline of the PEMFC under different operating conditions during the dynamic cycling process were compared. Then, a model based on variational mode decomposition and long short-term memory with attention mechanism (VMD-LSTM-ATT) is proposed. Aiming at the performance of PEMFC is affected by the external operation, VMD is used to capture the global information and details, and filter out interference information. To improve the prediction accuracy, ATT is used to assign weight to the features. Finally, in order to verify the effectiveness and superiority of VMD-LSTM-ATT, we respectively apply it to three current conditions under dynamic cycle conditions. The experimental results show that under the same test conditions, RMSE of VMD-LSTM-ATT is increased by 56.11 % and MAE is increased by 28.26 % compared with GRU attention. Compared with SVM, RNN, LSTM and LSTM-ATT, RMSE of VMD-LSTM-ATT is at least 17.26 % higher and MAE is at least 5.65 % higher.
Palabras clave

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: Heliyon Año: 2024 Tipo del documento: Article País de afiliación: China Pais de publicación: Reino Unido

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: Heliyon Año: 2024 Tipo del documento: Article País de afiliación: China Pais de publicación: Reino Unido