Four dimensional bio‐inspired optimization approach with artificial intelligence for proton exchange membrane fuel cell

Musharavati, Farayi

doi:10.1002/er.8007

Cited by 5 publications

(2 citation statements)

References 72 publications

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“…Previous researchers are trying to use ML to design and optimize membranes [44,[144][145][146][147][148][149][150][151], predict membrane properties [36,43,152], diagnose membrane conditions [153,154], and prevent membrane degradation [155][156][157]. Cho et al [43] collected data from a 1.2 kW PEMFC in a MATLAB/Simulink environment and used that data to train a nonlinear autoregressive network (NARX) with Bayesian optimization to predict the voltage, temperature, and membrane water content (Max.…”

Section: In the Field Of Membranementioning

confidence: 99%

Towards Reliable Prediction of Performance for Polymer Electrolyte Membrane Fuel Cells via Machine Learning-Integrated Hybrid Numerical Simulations

Kaiser,

Ahn,

Kim

et al. 2024

Processes

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For mitigating global warming, polymer electrolyte membrane fuel cells have become promising, clean, and sustainable alternatives to existing energy sources. To increase the energy density and efficiency of polymer electrolyte membrane fuel cells (PEMFC), a comprehensive numerical modeling approach that can adequately predict the multiphysics and performance relative to the actual test such as an acceptable depiction of the electrochemistry, mass/species transfer, thermal management, and water generation/transportation is required. However, existing models suffer from reliability issues due to their dependency on several assumptions made for the sake of modeling simplification, as well as poor choices and approximations in material characterization and electrochemical parameters. In this regard, data-driven machine learning models could provide the missing and more appropriate parameters in conventional computational fluid dynamics models. The purpose of the present overview is to explore the state of the art in computational fluid dynamics of individual components of the modeling of PEMFC, their issues and limitations, and how they can be significantly improved by hybrid modeling techniques integrating with machine learning approaches. Furthermore, a detailed future direction of the proposed solution related to PEMFC and its impact on the transportation sector is discussed.

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Section: In the Field Of Membranementioning

confidence: 99%

Towards Reliable Prediction of Performance for Polymer Electrolyte Membrane Fuel Cells via Machine Learning-Integrated Hybrid Numerical Simulations

Kaiser,

Ahn,

Kim

et al. 2024

Processes

View full text Add to dashboard Cite

show abstract

“…Wilberforce et al proposed an ANN model to predict the dynamic electrical and thermal performance of the PEMFC stack under various operating conditions [38,39]. Musharavati et al investigated a bio-inspired ANN model to find the optimal design and control variables for fuel cell dynamic operations [40].…”

Section: Introductionmentioning

confidence: 99%

A Dimension-Reduced Artificial Neural Network Model for the Cell Voltage Consistency Prediction of a Proton Exchange Membrane Fuel Cell Stack

et al. 2022

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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.

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AI-Assisted Flow Field Design for Proton Exchange Membrane Fuel Cells: Progress and Perspective

Zheng,

Meng,

Fan

et al. 2024

J Bionic Eng

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Four dimensional bio‐inspired optimization approach with artificial intelligence for proton exchange membrane fuel cell

Cited by 5 publications

References 72 publications

Towards Reliable Prediction of Performance for Polymer Electrolyte Membrane Fuel Cells via Machine Learning-Integrated Hybrid Numerical Simulations

Towards Reliable Prediction of Performance for Polymer Electrolyte Membrane Fuel Cells via Machine Learning-Integrated Hybrid Numerical Simulations

A Dimension-Reduced Artificial Neural Network Model for the Cell Voltage Consistency Prediction of a Proton Exchange Membrane Fuel Cell Stack

AI-Assisted Flow Field Design for Proton Exchange Membrane Fuel Cells: Progress and Perspective

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