PEMFC (proton exchange membrane fuel cell) high-current density performance prediction method, system, equipment and medium

By establishing a multi-physics coupled model and deep learning method, the problem of low prediction accuracy at high current density of fuel cells is solved, efficient and accurate performance prediction and status monitoring are achieved, and the operation and life management of fuel cells are optimized.

CN120373111APending Publication Date: 2025-07-25XI AN JIAOTONG UNIV
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Patent Information

Application Number
CN202510468919.1
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-04-15
Publication Date
2025-07-25

AI Technical Summary

Technical Problem

The prior art is difficult to accurately describe the complex physical and chemical processes of proton exchange membrane fuel cells under high current density, with low prediction accuracy, high experimental cost, and lack of a comprehensive multi-physics coupling model, making it impossible to effectively integrate multi-source data.

Method used

A multi-physics coupled model was established, combined with deep learning methods, and predict fuel cell performance through multimodal data fusion and deep neural networks. The Butler-Volmer equation was used to describe electrochemical reactions. The proton conduction model considered the influence of temperature and humidity. The gas diffusion model corrected Knutson diffusion, and used convolutional neural networks, recurrent neural networks and autoencoders to extract features to build a deep neural network of Transformer architecture for prediction.

Benefits of technology

It significantly improves the accuracy and accuracy of fuel cell performance prediction at high current density, reduces experimental costs, supports real-time performance prediction and status monitoring, optimizes control strategies, and extends fuel cell life.

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Abstract

The invention relates to a proton exchange membrane fuel cell (PEMFC) high current density performance prediction method, system, equipment and medium. The method comprises the following steps: establishing a multi-physical field coupling model which comprehensively considers complex processes such as electrochemical reaction, proton conduction, gas diffusion and heat transfer, and describing the change of each physical quantity by adopting a partial differential equation based on a basic physical law; performing grid division and numerical discretization on the proton exchange membrane fuel cell model; selecting model parameters, and verifying the model through experimental data of different working conditions; inputting actual working condition parameters into a model to predict performance, and analyzing a simulation result; using a convolutional neural network, a recurrent neural network and an auto-encoder to extract features from different types of data and fuse the features to form a comprehensive feature vector; a deep neural network prediction model is constructed, and a cross entropy loss function and an Adam optimizer are adopted for training; dropout, L1 and L2 regularization, k-fold cross validation and transfer learning are utilized to optimize the model, and the generalization ability is improved; the system, the equipment and the medium realize high current density performance prediction of the proton exchange membrane fuel cell (PEMFC) based on the method; the prediction precision is improved, the experiment cost is reduced, the internal mechanism can be deeply understood, and powerful support is provided for design optimization, operation management and fault diagnosis of the fuel cell.
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