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

arXiv: https://arxiv.org/abs/2510.17214 · HTML: https://mlsanity.com/paper/2510.17214/

## Key facts

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- First posted on arXiv 20 Oct 2025.

## Abstract (arXiv)

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%.