Online Identification of PEMFC Polarization Curves With Nonlinear Concentration Loss

Abstract

Polarization curve plays a significant role in proton exchange membrane fuel cells (PEMFCs) monitoring and control. However, the classical semiempirical model of polarization curve is nonseparably parameterizated due to its nonlinear concentration loss. To transform the model into a linear regression equation (LRE), a differential operator-based method is introduced. Then, for the estimation of the derived LRE, four online estimators including gradient descent (GD) estimator, least squares (LS) estimator, dynamic regressor extension and mixing-based least squares (DREM+LS) estimator, and composite learning (CL) estimator are employed and compared in practical PEMFC systems. To further validate their universality, two experiments with different operating conditions were conducted on two different PEMFCs. Based on the experimental results, an in-depth comparative analysis of these estimators is provided to verify theoretical results. The experimental results reveal that, compared with the classical GD and LS estimators, DREM+LS and CL estimators achieved better performance in both steady and transient states, showing a smoother tracking process and faster response speed.