Integrating multiple feature selection and self-attention mechanism in LSTM for fuel cell degradation prediction
The process of proton exchange membrane fuel cells (PEMFC) involves strong coupling of multiple physical fields,components,and factors,inevitably leading to prolonged performance degradation and local performance fluctuations during operation. However,effectively identifying key features from the multitude of parameters under the multiple couplings and capturing the overall performance degradation trend becomes exceptionally challenging. In response to these issues,a PEMFC degradation prediction model based on XGBoost and Self-Atten-LSTM is developed. First,a wavelet threshold denoising method is employed to remove noise interference from the original PEMFC data. Then,the XGBoost algorithm is used to select the main features significantly affecting PEMFC performance from the numerous parameters,achieving precise feature selection. Finally,the introduction of the self-attention mechanism in LSTM addresses its limitations in global modeling and complex interaction among multi-dimensional vectors when dealing with long sequences. Through adaptive weighting,it more effectively utilizes PEMFC degradation information. Compared to traditional LSTM,Bi-LSTM,and GRU models,the developed model can more accurately predict fuel cell degradation under both steady-state and dynamic conditions. The model exhibits a reduction in the average mean absolute error by 56.34％ to 77.04％,with a predictive accuracy of up to 99.09％. This approach can find broad applications in developing vehicle operation and maintenance strategies and enhancing system reliability.
万方数据
维普
