Diagnosis of Fuel Cell Health Status with Deep Sparse Auto-Encoder Neural Network
This work addresses the challenge of costly and complex online testing for fuel cell health monitoring, offering a practical solution for fuel cell system operators.
The paper tackled the problem of diagnosing fuel cell health status by predicting high-frequency impedance using a deep sparse auto-encoding network, achieving an accuracy rate above 92% and a hardware-based recognition rate of almost 90% on an FPGA.
Effective and accurate diagnosis of fuel cell health status is crucial for ensuring the stable operation of fuel cell stacks. Among various parameters, high-frequency impedance serves as a critical indicator for assessing fuel cell state and health conditions. However, its online testing is prohibitively complex and costly. This paper employs a deep sparse auto-encoding network for the prediction and classification of high-frequency impedance in fuel cells, achieving metric of accuracy rate above 92\%. The network is further deployed on an FPGA, attaining a hardware-based recognition rate almost 90\%.