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

TL;DR

This paper presents a deep sparse auto-encoder neural network for diagnosing fuel cell health by predicting high-frequency impedance, achieving over 92% accuracy and nearly 90% recognition rate on FPGA hardware.

Contribution

It introduces a novel deep sparse auto-encoder approach for fuel cell health diagnosis and demonstrates its effective deployment on FPGA hardware.

Findings

Accuracy rate above 92% in impedance prediction

Hardware recognition rate of nearly 90% on FPGA

Effective online diagnosis of fuel cell health

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\%.