Health state prognostic of fuel cell based on wavelet neural network and cuckoo search algorithm

• Published in: ISA transactions Vol. 113; pp. 175 - 184
• Main Authors: Chen, Kui, Laghrouche, Salah, Djerdir, Abdesslem
• Format: Journal Article
• Language: English
• Published: United States Elsevier Ltd 01.07.2021; Elsevier
• Subjects: Automatic; Cuckoo search algorithm; Degradation prognosis; Electric power; Engineering Sciences; Fluid mechanics; Mechanics; Physics; Proton exchange membrane fuel cell; Remaining useful life; Thermics; Wavelet neural network; Degradation prognosis; Proton exchange membrane fuel cell; Cuckoo search algorithm; Remaining useful life; Wavelet neural network
• ISSN: 0019-0578; 1879-2022; 1879-2022
• DOI: 10.1016/j.isatra.2020.03.012

This paper proposes a novel degradation prognosis of Proton Exchange Membrane Fuel Cell (PEMFC) based on Wavelet Neural Network (WNN) and Cuckoo Search Algorithm (CSA). The proposed method considering the main operating conditions of PEMFC can be applied to the health state prognostic of PEMFC under different conditions. First, the operating data of PEMFC are reconstructed by the locally weighted scatterplot smoothing method to filter noise. Then, the WNN that can analyze the degradation characteristics of PEMFC (global degradation trend and reversible phenomena) is adopted to build the degradation model of PEMFC. Finally, the structure and parameters of WNN are optimized by CSA to improve the accuracy for the degradation prognosis of PEMFC. The optimized degradation prognosis method is used to predict the remaining useful life of PEMFC. The proposed prognostic method is validated by 3 degradation tests of PEMFC under different conditions. The results show that CSA can greatly improve the degradation prognosis accuracy of PEMFC based on WNN. The proposed CSA-WNN can achieve higher precision than other traditional prognostic methods.