BL-DATransformer Lifespan Degradation Prediction Model of Fuel Cell Using Relative Voltage Loss Rate Health Indicator

The durability of fuel cells is the main obstacle to their large-scale application. Deep learning-based methods improve the accuracy of fuel cell lifespan degradation prediction. However, their reliance on static health indicators and application in bench experiment environments limits their ability to capture degradation trends under dynamic conditions. This paper proposes a novel lifespan degradation prediction method for fuel cells operating in real-world traffic environments, utilizing Relative Voltage Loss Rate (RVLR) as the health indicator. Initially, fuel cell lifespan degradation data with varying characteristics are obtained through a dynamic bench experiment and two sets of road driving experiments. Subsequently, a lifespan degradation prediction model based on the Bidirectional Long Short-Term Memory Dual-Attention Transformer (BL-DATransformer) is proposed. An ablation study is conducted on this architecture, with analysis performed to evaluate the influence of diverse input features on model performance. Finally, the comparison results with LSTM, Transformer, and Informer indicate that under smooth traffic conditions, when the training length is 70%, the RMSE is reduced by 84.32%, 74.94%, and 18.49%, respectively. Under congested traffic conditions, with the same training length, the RMSE is reduced by 88.30%, 78.33%, and 26.52%, respectively. The result demonstrates that the prediction method has high accuracy and practical application value.