Impedance Acquisition of Proton Exchange Membrane Fuel Cell Using Deeper Learning Network

Abstract

Electrochemical impedance is a powerful technique for elucidating the multi-scale polarization process of the proton exchange membrane (PEM) fuel cell from a frequency domain perspective. It is advantageous to acquire frequency impedance depicting dynamic losses from signals measured by the vehicular sensor without resorting to costly impedance measurement devices. Based on this, the impedance data can be leveraged to assess the fuel cell’s internal state and optimize system control. In this paper, a residual network (ResNet) with strong feature extraction capabilities is applied, for the first time, to estimate characteristic frequency impedance based on eight measurable signals of the vehicle fuel cell system. Specifically, the 2500 Hz high-frequency impedance (HFR) representing proton transfer loss and 10 Hz low-frequency impedance (LFR) representing charge transfer loss are selected. Based on the established dataset, the mean absolute percentage errors (MAPEs) of HFR and LFR of ResNet are 0.802% and 1.386%, respectively, representing a superior performance to other commonly used regression and deep learning models. Furthermore, the proposed framework is validated under different noise levels, and the findings demonstrate that ResNet can attain HFR and LFR estimation with MAPEs of 0.911% and 1.610%, respectively, even in 40 dB of noise interference. Finally, the impact of varying operating conditions on impedance estimation is examined.