An Explainable Data-Driven Framework for Fuel Cell Aging Prediction Under Dynamic Condition

Abstract

Fuel cells are considered as the preferred future power source due to their environmental friendliness and high efficiency, whereas short lifespan and high cost hinder their large-scale commercialization. Fuel cell prognostic can contribute to prolonging the fuel cell life and reducing the overall cost, and it has attracted research attention recently. However, most of the prognostic methods treat the measured voltage as the health indicator and thus can only be applied to the fuel cell that works under constant current condition. To handle the prognostic under frequent load change condition, this paper proposes an attention mechanism-based explainable data-driven framework. In the framework, system parameters are used to construct a virtual voltage as the health indicator for degradation prediction. Attention mechanism is integrated to find the essential parameters that affect fuel cell degradation, which can reduce the sensors and provide an explainable prediction result. The proposed framework is testified using aging data obtained under dynamic condition. Results show that the current, outlet air temperature and time are the most important parameters that affect the prediction. Comparisons with conventional methods indicate the proposed method can get a promising prediction.