Study of current distribution generation in PEMFC based on conditional variational auto-encoder

The Proton Exchange Membrane Fuel Cell (PEMFC) converts the chemical energy of hydrogen fuel directly into electrical energy with broad application prospects. Understanding how current density is distributed in the PEMFC systems is crucial as it is a key factor influencing system performance. However, direct modeling for current distribution may encounter the challenge of dimensional catastrophe owing to the high dimensionality of the data. This paper uses a high-resolution segmented measurement device with 396 points to conduct experimental tests on the current distribution of a PEMFC with reactive area of 406 cm² during a stepwise increase in load current. The current distribution is modeled based on the test results to learn the mapping relationship between the experimental parameters and the current distribution. The proposed model utilizes a Conditional Variational Auto-Encoder (CVAE) to generate current distributions. The MSE (Mean-Square Error) of the trained CVAE model reaches 9.2 × 10^–5, and the comparison results show that the 222.9A current distribution error has the largest MSE of 6.36 × 10^–4 and a KL Divergence (Kullback-Leibler Divergence) of 9.55 × 10^–4, both of which are at a low level. This model enables the direct determination of the current distribution based on the experimental parameters, thereby establishing a technical foundation for investigating the impact of experimental conditions on fuel cells. This model is also of great significance for research on fuel cell system control strategies and fault diagnosis.