Remaining useful life prediction method of PEMFC based on joint feature extraction of LSTM and improved Transformer

Proton exchange membrane fuel cells (PEMFC) are a crucial component of modern sustainable clean energy generation technology. Accurate prediction of performance degradation is key to enhancing the performance of PEMFC systems and is also an important step in promoting this clean energy technology for broader applications. Traditional methods for predicting performance degradation typically achieve their aims through mechanistic models and forecasting algorithms, refining model parameters and algorithm structures to improve accuracy. However, these methods often fall short in fully considering the detailed characteristics implied by aging data over long-time scales and the phenomenon of voltage recovery. Therefore, this paper proposes a predictive model that combines long short-term memory (LSTM) networks with an enhanced Transformer for joint feature extraction to achieve precise predictions of PEMFC output voltage. Initially, based on traditional Transformer architecture, an optimized design is performed to build an improved Transformer model suitable for PEMFC remaining useful life (RUL) prediction. Secondly, the improved Transformer model is embedded into the conventional LSTM framework, constructing a combined LSTM and improved Transformer joint feature extraction model. Under steady-state, dynamic and pseudo-dynamic datasets, finally, the LSTM, convolutional neural networks (CNN), CNN-LSTM, the improved Transformer, and the joint feature extraction model were evaluated for output voltage prediction and quantitatively compared. The results indicate that the improved Transformer and the joint prediction model show significant improvements over other comparative models in evaluation metrics such as RMSE, MAE, and R 2 . This confirms that the proposed prediction model can enhance the RUL prediction accuracy of PEMFC to some extent.