Efficient fault diagnosis method of PEMFC thermal management system for various current densities

The temperature of a fuel cell has a considerable impact on the saturation of a membrane, electrochemical reaction speed, and durability. So thermal management is considered one of the critical issues in polymer electrolyte membrane fuel cells. Therefore, the reliability of the thermal management system is also crucial for the performance and durability of a fuel cell system. In this work, a methodology for component-level fault diagnosis of polymer electrolyte membrane fuel cell thermal management system for various current densities is proposed. Specifically, this study suggests fault diagnosis using limited data, based on an experimental approach. Normal and five component-level fault states are diagnosed with a support vector machine model using temperature, pressure, and fan control signal data. The effects of training data at different operating current densities on fault diagnosis are analyzed. The effects of data preprocessing method are investigated, and the cause of misdiagnosis is analyzed. On this basis, diagnosis results show that the proposed methodology can realize efficient component-level fault diagnosis using limited data. The diagnosis accuracy is over 92% when the residual basis scaling method is used, and data at the highest operating current density is used to train the support vector machine. (C) 2020 Hydrogen Energy Publications LLC. Published by Elsevier Ltd. All rights reserved.

Automated summary

This study proposes a methodology for component-level fault diagnosis of polymer electrolyte membrane fuel cell thermal management system for different current densities, analyzing the effects of training data and data preprocessing methods on fault diagnosis. By utilizing temperature, pressure, and fan control signal data, a support vector machine model is used to diagnose normal and five component-level fault states with an accuracy of over 92% when using the residual basis scaling method.