Multistep prediction of remaining useful life of proton exchange membrane fuel cell based on temporal convolutional network

Proton-exchange membrane fuel cells (PEMFCs), as potential energy converters with broad application prospects, have low durability owing to several factors that make it difficult to quantify the degradation of PEMFC components. The accurate prediction of the remaining useful life (RUL) can help users understand the degradation status of PEMFCs and adopt reasonable maintenance strategies to improve durability. This paper proposes an RUL prediction framework based on a temporal convolutional network (TCN). First, an equivalent circuit model of the PEMFC is established, and complex nonlinear least squares regression is used to fit the model to estimate the polarization resistance. Then, the prediction framework and joint degradation indicator of the TCN are constructed to predict the RUL. The TCN is compared with four models: linear regression, Holt-Winters, seasonal autoregressive integrated moving average, and Prophet. The results show that the TCN performs significantly better in terms of all the predictive metrics, including the root-mean-squared error which is at least 13.43% lower than those of the four models. The RUL prediction accuracy of the TCN is at least 7.76% higher than that of the four models. Except at 800 h, the average RUL accuracy of TCN is 92.20%. This confirms that the TCN (double variables) can accurately predict the RUL of PEMFCs.