Online voltage consistency prediction of proton exchange membrane fuel cells using a machine learning method. (6th October 2021)

• Record Type: Journal Article
• Authors: Chen, Huicui; Shan, Wanchao; Liao, Hongyang; He, Yuxiang; Zhang, Tong; Pei, Pucheng; Deng, Chenghao; Chen, Jinrui
• Is Part Of: International journal of hydrogen energy. Volume 46:Number 69(2021)
• Page Start: 34399
• Page End: 34412
• Publication Date: 2021-10-06
• Subjects: Proton exchange membrane fuel cell -- Voltage consistency -- Machine learning -- Regression analysis; Hydrogen as fuel -- Periodicals; Hydrogène (Combustible) -- Périodiques; Hydrogen as fuel; Periodicals; 665.81
• DOI: 10.1016/j.ijhydene.2021.08.003
• Languages: English
• ISSNs: 0360-3199

Abstract

Abstract: Widely acknowledged by experts, the inconsistency between the cells of the proton exchange membrane fuel cell stack during operation is an important cause of the fuel cell life decay. Existing studies mainly focus on qualitative analysis of the effects of operating parameters on fuel cell stack consistency. However, there is currently almost no quantitative research on predicting the voltage consistency through operating parameters with machine learning methods. To solve this problem, a three-dimensional model of proton exchange membrane fuel cell stack with five single cells is established in this paper. The Computational Fluid Dynamic (CFD) method is used to provide the source data for prediction model. After predicting the voltage consistency with several machine learning methods and comparing the accuracy through simulation data, the integrated regression method based on Gradient Boosting Decision Tree (GBDT) gets the highest score (0.896) and is proposed for quickly predicting the consistency of cell voltage through operating parameters. After verifying the GBDT method with the experimental data from the fuel cell stack of SUNRISE POWER, in which the accuracy score is 0.910, the universality and accuracy of the method is confirmed. The influencing sensitivity of each operating parameter is evaluated and the current density has the greatest influence on the predicted value, which accounts for 0.40. The prediction of voltage consistency under different combination of operating parameters can guide the optimization of structural parameters in the process of the fuel cell design and operating parameters in the process of fuel cell control. Highlights: Predict voltage consistency through operating parameters with machine learning. Gradient Boosting Decision Tree (GBDT) method gets the highest score. The universality and accuracy of GBDT method are verified by experiments. The influencing sensitivity of each parameter is evaluated.