用深度稀疏自编码网络预测燃料电池阻抗，准确率超92%
Diagnosis of Fuel Cell Health Status with Deep Sparse Auto-Encoder Neural Network
- 用深度稀疏自编码网络预测高频率阻抗
- 模型准确率超过92%，FPGA部署后识别率近90%
- 适合燃料电池健康监测与嵌入式系统开发者
燃料电池堆的稳定运行依赖于健康状态的精准诊断。其中，高频阻抗是评估燃料电池状态与健康状况的关键指标，但其在线测试复杂且成本高昂。本文采用深度稀疏自编码网络对燃料电池的高频阻抗进行预测与分类，实现准确率超过92%。该网络进一步部署于FPGA，实现基于硬件的识别率接近90%。
原文摘要 · Abstract (English)
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\%.
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