{"ID":2852595,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2510.17214","arxiv_id":"2510.17214","title":"Diagnosis of Fuel Cell Health Status with Deep Sparse Auto-Encoder Neural Network","abstract":"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\\%.","short_abstract":"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 co...","url_abs":"https://arxiv.org/abs/2510.17214","url_pdf":"https://arxiv.org/pdf/2510.17214v1","authors":"[\"Chenyan Fei\",\"Dalin Zhang\",\"Chen Melinda Dang\"]","published":"2025-10-20T06:55:35Z","proceeding":"cs.LG","tasks":"[\"cs.LG\",\"cs.AI\"]","methods":"[]","has_code":false}