Machine learning and Bayesian optimization for performance prediction of proton-exchange membrane fuel cells - Archive ouverte HAL Accéder directement au contenu
Article Dans Une Revue Energy and AI Année : 2024

Machine learning and Bayesian optimization for performance prediction of proton-exchange membrane fuel cells

Résumé

Proton-exchange membrane fuel cells (PEMFCs) are critical components of zero-emission electro-hydrogen generators. Accurate performance prediction is vital to the optimal operation management and preventive maintenance of these generators. Polarization curve remains one of the most important features representing the performance of PEMFCs in terms of efficiency and durability. However, predicting the polarization curve is not trivial as PEMFCs involve complex electrochemical reactions that feature multiple nonlinear relationships between the operating variables as inputs and the voltage as outputs. Herein, we present an artificial-intelligence-based approach for predicting the PEMFCs’ performance. In that way, we propose first an explainable solution for selecting the relevant features based on kernel principal component analysis and mutual information. Then, we develop a machine learning approach based on XGBRegressor and Bayesian optimization to explore the complex features and predict the PEMFCs’ performance. The performance and the robustness of the proposed machine learning based prediction approach is tested and validated through a real industrial dataset including 10 PEMFCs. Furthermore, several comparison studies with XGBRegressor and the two popular machine learning-based methods in predicting PEMFC performance, such as artificial neural network (ANN) and support vector machine regressor (SVR) are also conducted. The obtained results show that the proposed approach is more robust and outperforms the two conventional methods and the XGBRegressor for all the considered PEMFCs. Indeed, according to the coefficient of determination criterion, the proposed model gains an improvement of 6.35%, 6.8%, and 4.8% compared with ANN, SVR, and XGBRegressor respectively.
Fichier principal
Vignette du fichier
Machine learning and Bayesian optimization for performance prediction of proton-exchange membrane fuel cells.pdf (3.55 Mo) Télécharger le fichier
Origine Fichiers éditeurs autorisés sur une archive ouverte

Dates et versions

hal-04611875 , version 1 (14-06-2024)

Identifiants

Citer

Soufian Echabarri, Phuc Do, Hai-Canh Vu, Bastien Bornand. Machine learning and Bayesian optimization for performance prediction of proton-exchange membrane fuel cells. Energy and AI, 2024, 17, pp.100380. ⟨10.1016/j.egyai.2024.100380⟩. ⟨hal-04611875⟩
60 Consultations
5 Téléchargements

Altmetric

Partager

Gmail Mastodon Facebook X LinkedIn More