Proton Exchange Membrane Fuel Cell Prognostics Using Genetic Algorithm and Extreme Learning Machine

The prognostics can predict the degradation of proton exchange membrane fuel cell (PEMFC) to formulate a reasonable maintenance plan for improving its lifetime and performance. In this paper, the voltage degradation for PEMFC at different conditions is predicted, by using a novel prognostics method based on genetic algorithm (GA) and extreme learning machine (ELM). The novel prognostics method considers the effects of the PEMFC load current, relative humidity, hydrogen pressure, and temperature on the degradation for PEMFC. Firstly, the voltage degradation prediction model for PEMFC is built by the ELM. Then, the GA is adopted to determine the optimal parameter of the proposed degradation prediction model. Finally, the voltage degradation prediction of the proposed prognostics method is validated, using data derived from the PEMFC in actual postal fuel cell electric vehicle (PFCEV) at real conditions and PEMFC at dynamic load current. The experimental results show that the proposed prognostics method can obtain good voltage degradation prediction for PEMFC in PFCEV at real conditions. The proposed method achieves better voltage degradation prediction for PEMFC at dynamic load current than other traditional data-based prognostics methods.