A Dimension-Reduced Artificial Neural Network Model for the Cell Voltage Consistency Prediction of a Proton Exchange Membrane Fuel Cell Stack
Source abstract
Abstract
The voltage consistency of hundreds of cells in a proton exchange membrane fuel cell stack significantly influences the stack’s performance and lifetime. Using the physics-based model to estimate the cell voltage consistency is highly challenging due to the massive calculation efforts and the complicated fuel cell designs. In this research, an artificial neural network (ANN) model is developed to efficiently predict the cell voltage distribution and the consistency of a commercial-size fuel cell stack. To balance the computation efficiency and accuracy, a dimension-reduced method is proposed with different output-grouping strategies to optimize the ANN structure based on the experiment test of a 100-cell stack. The model’s training time falls nonlinearly from 16 min to 6 s with the output neuron number decreasing from 100 to 5, while the model can still predict the cell voltage distribution trends. With the proposed model, the stack’s cell voltage distributions could be reproduced with significantly lowered computation time, which is beneficial to evaluate the fuel cell status and optimize the control strategies.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Fuel Cells and Related Materials · Electrocatalysts for Energy Conversion · Analytical Chemistry and Sensors
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
School of Automation Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China
Hydrogen and Fuel Cell Institute, University of Electronic Science and Technology of China, Chengdu 611731, China
Department of Computer Science, Western University, London, ON N6A 3K7, Canada
Funding
National Key R&D Program of China · Science and Technology Program of Sichuan Province
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