Diagnosis of Fuel Cell Health Status with Deep Sparse Auto-Encoder Neural Network

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%.