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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2510.17214 |
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Table of Contents:
- 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\%.