Proton Exchange Membrane Fuel Cell Power Prediction Based on Ridge Regression and Convolutional Neural Network Data-Driven Model

Research on the power prediction of proton exchange membrane fuel cells (PEMFCs) has garnered considerable attention. Because mainstream computational-fluid-dynamics-based methods are time-consuming, this study aimed to design a data-driven method based on Ridge regression (Ridge) and convolutional neural network (CNN) algorithms that can efficiently predict PEMFC power under uncertain conditions in real-world scenarios and reduce the time consumption. The measured data from a PEMFC test bench (3 kW) were collected as the data source for the model. First, we adopted Ridge to eliminate abnormal samples. Second, we analyzed and selected the variables that have a significant effect on PEMFC power. Moreover, we optimized the model using batch normalization, dropout, Nadam, Swish, and Huber techniques. Finally, the performance of the model was evaluated by combining real datasets and real polarization curves. The experimental results demonstrate that the polarization curves predicted by the CNN-based model agree with the real curves, with a prediction accuracy of approximately 0.96, a prediction time of 1 μs, and an iteration period of less than 1 s per cycle. A comparative analysis shows that the CNN-based model prediction precision was superior to that of other mainstream machine learning algorithms. In real scenarios, the CNN-based model accurately predicts the power of PEMFC.