A Degradation Prediction Method for PEM Fuel Cell Based on Deep Temporal Feature Extraction and Transfer Learning

The increasing environmental issues such as air pollution and energy scarcity of fossil fuels require the acceleration of electrification in various fields, especially for transportation. According to this development trend, fuel cell systems gradually become a potential alternative to traditional power systems for high efficiency, high energy density, and zero pollution, while the relatively short lifetime has tremendously limited its large-scale commercial application. To extend the fuel cell lifespan, degradation predictive methods are proved to have an outstanding effect. In this article, a hybrid prediction model based on deep temporal feature extraction and transfer learning is proposed. First, the health index (HI) is constructed by extracting the fuel cell degradation features through a temporal convolutional network (TCN); on this basis, transfer learning is performed according to the feature extraction, and finally, the extracted features are input into the long short-term memory model to complete the fuel cell degradation prediction. The effectiveness of the algorithm is verified by experimentally extracting degradation data under different operating conditions, and the results show that the proposed method can effectively improve the prediction accuracy and decrease the computation by extracting key parameters of fuel cell degradation and is independent of operating conditions.