Abstract
High-frequency resistance (HFR) is a critical quantity strongly related to a fuel cell system's performance. It is beneficial to estimate the fuel cell system's HFR from the measurable operating conditions without resorting to costly HFR measurement devices. In this study, we propose a data-driven approach for a real-time prediction of HFR. Specifically, we use a long short-term memory (LSTM) based machine learning model that takes into account both the current and past states of the fuel cell, as characterized through a set of sensors. These sensor signals form the input to the LSTM. The data is experimentally collected from a vehicle lab that operates a 100 kW automotive fuel cell stack running on an automotive-scale test station. Our current results indicate that our prediction model achieves high accuracy HFR predictions and outperforms other frequently used regression models. We also study the effect of the extracted features generated by our LSTM model. Our study finds that only very few dimensions of the extracted feature are influential in HFR prediction. The study highlights the potential to monitor HFR condition accurately and timely on a car.