The aging model is critical for evaluating the state of health (SoH) and remaining useful life (RUL) of proton exchange membrane fuel cells (PEMFCs). However

uncertainty

caused by various reasons

reduces the accuracy and reliability of the model. Therefore

a model uncertainty and SoH simultaneous quantification (MUSQ) algorithm is proposed to guide and correct the long-term prediction of the convolutional neural network-long short-term memory (CNN-LSTM) neural network hybrid model and a new RUL hybrid prediction framework is constructed. Dynamic load cycle durability test data are used

and this hybrid prediction method is compared with the extended Kalman filtering algorithm

adaptive extended Kalman filtering algorithm

MUSQ algorithm

LSTM neural network

and CNN-LSTM hybrid model

etc. This method has the best long-term prediction performance and RUL estimation accuracy. Under the operating condition of a load current of 14.85 A

this method reduces cumulative errors by 49.64%

61.33%

30.65%

57.00%

and 52.90%

respectively.