An embedding layer-based quantum long short-term memory model with transfer learning for proton exchange membrane fuel stack remaining useful life prediction

Abstract

The battery management system (BMS) plays a critical role in electric vehicles (EVs), with the prediction of remaining useful life (RUL) being of utmost importance. This functionality enables real-time monitoring and enhances driver assistance through intelligent driving mechanisms. To improve the precision of stack voltage degradation and RUL prognostication for proton exchange membrane fuel cell (PEMFC) aging, especially with limited real-time training data, we propose a novel approach using a layer-enhanced quantum long short-term memory model with transfer learning. The proposed model is trained offline using historical stack voltage degradation data from a source stack, which is then fine-tuned and transferred to a target stack. Comparative evaluations demonstrate the model's robustness, with consistently low root mean square errors in stack voltage predictions, even with increased training data. The proposed model significantly improves RUL prediction accuracy, achieving a maximum relative accuracy enhancement of 1.33 % when initiating predictions at 712 h. These results affirm the effectiveness of our approach in enhancing the accuracy of PEMFC aging prognosis, thereby offering valuable insights for practical integration within the BMS framework for EVs.