Remaining useful life prediction of vehicle-oriented PEMFC systems based on IGWO-BP neural network under real-world traffic conditions

Accurately predicting the useful life can serve as a pivotal reference for effectively extending the lifespan of proton exchange membrane fuel cells (PEMFCs). Herein, this paper proposes a novel method that combines an improved grey wolf optimizer (IGWO) algorithm and a backpropagation (BP) neural network to predict the remaining useful life (RUL) of vehicle-oriented PEMFC systems using relative power loss rate (RPLR) as a health indicator under real-world traffic conditions. First, The Pearson correlation coefficient is used to analyze the correlation of various monitoring parameters and to verify the effectiveness of RPLR as a dynamic health indicator. Then, the IGWO-BP neural network-based prediction method is described and used to predict the RUL of PEMFC systems. Finally, the accuracy and reliability of the proposed method are validated against two separate datasets of PEMFC city buses operating under different traffic conditions. Compared with other methods, the proposed method has a relative error of less than 5 % and predicts a shorter RUL than the actual RUL. These findings illustrate that the proposed method has a high prediction accuracy and offers an early warning function, which is beneficial for practical applications.