Proton Exchange Membrane Fuel Cell (PEMFC) has become a promising power source with wide applications to many electronic and electrical devices. However, even if it is a competitive energy converter, PEMFC still suffers from its limited lifespan. Prognostics appear to be a good solution to helping take actions to extend its lifetime. Considering both advantage and disadvantage of model-based and data-driven based prognostic methods, this study proposes a hybrid prognostic method for PEMFC based on a data-driven method, least square support vector machine (LSSVM) and a model-based method, regularized particle filter (RPF). The main contributions of the proposed method include: 1) It can provide not only an estimated value but also an uncertainty characterization of RUL with a probability distribution; 2) It has a better capability to capture the nonlinearities in degradation data and a lower reliance on PEMFC degradation model; 3) The RPF method improves the standard particle filter algorithm by reducing the degeneration phenomenon and loss of diversity among the particles. Effectiveness of the proposed method is verified based on PEMFC dataset provided by FCLAB Research Federation. The results indicate that the proposed hybrid method can effectively combine both advantages of data-driven and model-based methods, providing a higher accuracy of RUL prediction for PEMFC. (C) 2018 Hydrogen Energy Publications LLC. Published by Elsevier Ltd. All rights reserved.