Fuel cell health prognosis using Unscented Kalman Filter: Postal fuel cell electric vehicles case study.

Abstract The Proton Exchange Membrane Fuel Cell (PEMFC) health monitoring and management are of critical importance for the performance and cost efficiency of Fuel Cell Electric Vehicle (FCEV). Prognostics play an important role in improving the lifetime and reducing maintenance costs of PEMFC by predicting the degradation trend. In this paper, the degradation prediction of PEMFC is based on a novel model-driven method which combines the Unscented Kalman Filter (UKF) algorithm with the proposed voltage degradation model. The experimental data originated from the FCEVs which achieve postal delivery mission in the real road are used for construction and validation of the proposed model-driven prognostic method. At our best knowledge, this is the first application which uses field-based data for FC health prognosis. The influence of different lengths of measured voltage data on degradation prediction of PEMFC, and the degradation prediction performance of PEMFC in different FCEVs are also investigated by the proposed method. Test results show that the proposed model-driven method is able to accurately estimate the voltage degradation trend of PEMFC in the FCEV. When more data are applied to learning the degradation of PEMFC, the mean Relative Error (RE) in the prediction phase will decrease. Especially, when the learning data exceeds 45 h, the mean RE in prediction phase is reduced to 0.68%. Considering that the maximum mean RE in the prediction phase is 2.03% for 3 postal FCEVs, the proposed method can be applied in the degradation trend prediction of PEMFC in FCEV under real conditions. Highlights • Prognostic method prediction of PEMFC based on the Unscented Kalman Filter. • The method is validated using real road data of fuel cell electric vehicles. • The method estimates accurately the voltage degradation trend of PEMFC in FCEV. [ABSTRACT FROM AUTHOR]