Parameter optimization of membrane mlectrode assembly in Fuel Cell based on improved differential evolution algorithm: biomechanical stress and strain considerations
Abstract
The membrane electrode assembly in a proton exchange membrane fuel cell (PEMFC) functions as the electrochemical reaction region, where the generated electric current relies on the diffusion of reactant gases and electron conduction. Drawing inspiration from biomechanics, this study embarked on constructing a database of PEMFC performance data. Similar to how biomechanical studies use advanced imaging and sensing techniques to map the internal workings of organisms, three-dimensional computational fluid dynamics (CFD) simulations were employed to capture the intricate fluid and gas behaviors within the fuel cell. The data was then used to train data-driven surrogate models based on artificial neural network (ANN) and improved differential evolution for rapid prediction and optimization. When considering the biomechanical aspects, we analyze the mechanical stresses and strains that occur within the membrane electrode assembly during operation. These biomechanical factors can affect the durability and performance of the fuel cell. The gas diffusion layer (GDL) is similar to the pore structure in biological tissues. The pore structure of biological organisms, such as bones, not only ensures the diffusion and transport of nutrients, but also provides space for the attachment of cells to maintain the growth and metabolism of bones. The optimization results revealed that the pores of the GDL, just like the pores of biological tissues, affect the diffusion efficiency of the reactant gases (similar to nutrients) to the catalytic layer, and an appropriate porosity ensures the supply of the reactants required for the electrochemical reactions inside the cell, and improves the PEMFC performance of the cell. By utilizing the random forest algorithm (RF) to conduct feature importance evaluation, we can gain further understanding and interpretation of the factors influencing coupling relationships. The researchers successfully identified the optimal values of GDL porosity and thickness, resulting in an 8.75% increase in power density and significant improvement in oxygen distribution uniformity. To validate the effectiveness and accuracy of the optimization, the optimized structural parameters were incorporated into CFD simulations. The validation results demonstrated close alignment between the optimized model's performance and actual values, confirming the efficacy and reliability of the optimization framework. Overall, this data-driven optimization approach provides an effective tool for multi-variable optimization of complex systems and holds significant importance in enhancing the performance and power density of PEMFC, while also taking into account the biomechanical factors that influence its long-term operation and stability.
References
1. Y.D. Wang, Q. Meyer, K. Tang, J.E. McClure, R.T. White, S.T. Kelly, M.M. Crawford, F. Iacoviello, D.J.L. Brett, P.R. Shearing, P. Mostaghimi, C. Zhao, R.T. Armstrong, Large-scale physically accurate modelling of real proton exchange membrane fuel cell with deep learning, Nat Commun, 14 (2023) 745.
2. C. Lee, W.J.M. Kort-Kamp, H. Yu, D.A. Cullen, B.M. Patterson, T.A. Arman, S. Komini Babu, R. Mukundan, R.L. Borup, J.S. Spendelow, Grooved electrodes for high-power-density fuel cells, Nature Energy, (2023).
3. K. Jiao, J. Xuan, Q. Du, Z. Bao, B. Xie, B. Wang, Y. Zhao, L. Fan, H. Wang, Z. Hou, S. Huo, N.P. Brandon, Y. Yin, M.D. Guiver, Designing the next generation of proton -exchange membrane fuel cells, Nature, (2021).
4. A.C. Turkmen, C. Celik, The effect of different gas diffusion layer porosity on proton exchange membrane fuel cells, Fuel, (2018).
5. P. Yang, Y. Wang,Y. Yang, L. Yuan, Z. Jin, Effects of Gas Diffusion Layer Porosity Distribution on Proton Exchange Membrane Fuel Cell, Energy Technology, (2021).
6. B.W. Wang, B. Xie, J. Xuan, K. Jiao, AI-based optimization of PEM fuel cell catalyst layers for maximum power density via data-driven surrogate modeling, Energy Conversion and Management, 205 (2020).
7. H. Deng, Y. Hou, W. Chen, F. Pan, K. Jiao, Lattice Boltzmann simulation of oxygen diffusion and electrochemical reaction inside catalyst layer of PEM fuel cells, International Journal of Heat and Mass Transfer, 143 (2019).
8. S. Liu, C. Chen, J. Tan, H. Hu, D. Xuan, Multi‐objective optimization of porous layers for proton exchange membrane fuel cells based on neural network surrogate model, International Journal of Energy Research, 46 (2022) 19796-19813.
9. H.-W. Li, B. -S. Xu, C.-H. Du, Y. Yang, Performance prediction and power density maximization of a proton exchange membrane fuel cell based on deep belief network, Journal of Power Sources, 461 (2020).
10. H.-W. Li, B. -X. Qiao, J. -N. Liu, Y. Yang, W. Fan, G. -L. Lu, A data-driven framework for performance prediction and parameter optimization of a proton exchange membrane fuel cell, Energy Conversion and Management, 271 (2022).
11. Turgut, M.T. Coban, Optimal proton exchange membrane fuel cell modelling based on hybrid Teaching Learning Based Optimization – Differential Evolution algorithm, Ain Shams Engineering Journal, (2015).
12. Huo W, Li W, Zhang Z, et al. Performance prediction of proton-exchange membrane fuel cell based on convolutional neural network and random forest feature selection. Energy Conversion and Management. 2021; 243: 114367.
13. P. Abraham B, K. Murugavel K, Influence of Catalyst Layer and Gas Diffusion Layer Porosity in Proton Exchange Membrane Fuel Cell Performance, Electrochimica Acta, (2021).
14. Y. Liu, S. Wu, Y. Qin, M. Zhang, X. Liu, J. Zhang, Y. Yin, Mass transport and performance of proton exchange membrane fuel cell considering the influence of porosity distribution of gas diffusion layer, International Journal of Green Energy, (2021).
15. G. Zhang, L. Wu, C. Tongsh, Z. Qu, S. Wu, B. Xie, W. Huo, Q. Du, H. Wang, L. An, N. Wang, J. Xuan, W. Chen, F. Xi, Z. Wang, K. Jiao, Structure Design for Ultrahigh Power Density Proton Exchange Membrane Fuel Cell, Small Methods, 7 (2023) e2201537.
16. W. Fan, T. Zhao, K. Jiang, L. Sun, S. Jia, Q. Wu, G. Lu, Z. Liu, Plant vs. Animal Prototype for Designing Bio-inspired PEMFC Flow Fields: Corn Veins or Murray’s Law?, Journal of Bionic Engineering, (2022).
17. W. Yuan, Y. Tang, M. Pan, Z. Li, B. Tang, Model prediction of effects of operating parameters on proton exchange membrane fuel cell performance, Renewable Energy, (2010).
18. G. Zhang, B. Xie, Z. Bao, Z. Niu, K. Jiao, Multi‐phase simulation of proton exchange membrane fuel cell with 3D fine mesh flow field, International Journal of Energy Research, (2018).
19. S. Um, C.Y. Wang, Three-dimensional analysis of transport and electrochemical reactions in polymer electrolyte fuel cells, Journal of Power Sources, (2004).
20. K.Y. Hu, P.Y. Zhao, S.X. Wang, Y.L. Wang, Three-dimensional multiphase simulation of a partially narrowed flow field configuration for a high-performance polymer electrolyte membrane fuel cell, Applied Thermal Engineering, 223 (2023).
21. N. Guo, M.C. Leu, U.O. Koylu, Bio-inspired flow field designs for polymer electrolyte membrane fuel cells, International Journal of Hydrogen Energy, (2014).
22. D. Lorenzini-Gutierrez, A. Hernandez-Guerrero, B. Ramos-Alvarado, I. Perez-Raya, A. Alatorre-Ordaz, Performance analysis of a proton exchange membrane fuel cell using tree-shaped designs for flow distribution, International Journal of Hydrogen Energy, (2013).
23. D. Mahmoud, Z. Xing, Enhancing reliability and lifespan of PEM fuel cells through neural network-based fault detection and classification, International Journal of Hydrogen Energy, (2023).
24. T. Wilberforce, A.G. Olabi, Proton exchange membrane fuel cell performance prediction using artificial neural network, International Journal of Hydrogen Energy, (2020).
25. C. Zhijie, Z. Wei, Z. Kun, L. Qingqing, H. Yuhan, E. Jiaqiang, Multi-objective optimization of proton exchange membrane fuel cells by RSM and NSGA-II, Energy Conversion and Management, (2023).
26. B. Yang, J. Wang, L. Yu, H. Shu, T. Yu, X. Zhang, W. Yao, L. Sun, A critical survey on proton exchange membrane fuel cell parameter estimation using meta-heuristic algorithms, Journal of Cleaner Production, 265 (2020).
27. C. Guo, J. Lu, Z. Tian, W. Guo, A. Darvishan, Optimization of critical parameters of PEM fuel cell using TLBO-DE based on Elman neural network, Energy Conversion and Management, (2019).
28. D. Runben, W. Xuezhe, W. Xueyuan, C. Siqi, Y. Hao, D. Haifeng, M. Pingwen, A fault diagnosis model for proton exchange membrane fuel cell based on impedance identification with differential evolution algorithm, International Journal of Hydrogen Energy, (2021).
29. W. Zhu, J.-a. Fang, W. Zhang, Y. Xu, L. Tong, A Hybrid Differential Evolution for Optimum Modeling of PEM Fuel Cells, Arabian Journal for Science and Engineering, (2014).
30. Fathy, M.A. Elaziz, A.G. Alharbi, A Novel Approach Based on Hybrid Vortex Search Algorithm and Differential Evolution for Identifying the Optimal Parameters of PEM Fuel Cell, Renewable Energy, (2020).
31. W. Gong, Z. Cai, Accelerating parameter identification of proton exchange membrane fuel cell model with ranking-based differential evolution, Energy, (2013).
32. H. Guo, Y. Li, J. Li, H. Sun, D. Wang,X. Chen, Differential evolution improved with self-adaptive control parameters based on simulated annealing, Swarm and Evolutionary Computation, (2014).
33. W. Tabbi, B. Mohammad, O. Abdelnasir, Power and Voltage Modelling of a Proton-Exchange Membrane Fuel Cell Using Artificial Neural Networks, Energies, (2022).
34. C. Youngtak, H. Gyuyeong, G. Dela Quarme, H. Sungwon, Artificial neural network-based model predictive control for optimal operating conditions in proton exchange membrane fuel cells, Journal of Cleaner Production, (2022).
35. S.V. Puranik, A. Keyhani, F. Khorrami, Neural Network Modeling of Proton Exchange Membrane Fuel Cell, IEEE Transactions on Energy Conversion, (2010).
36. H.-W. Li, J. -N. Liu, Y. Yang, G. -L. Lu, B.-X. Qiao, Coupling flow channel optimization and Bagging neural network to achieve performance prediction for proton exchange membrane fuel cells with varying imitated water-drop block channel, International Journal of Hydrogen Energy,(2022).
37. S. Feng, W. Huang, Z. Huang, Q. Jian, Optimization of maximum power density output for proton exchange membrane fuel cell based on a data-driven surrogate model, Applied Energy, (2022).
38. G. Zhang, L. Wu, K. Jiao, P. Tian, B. Wang, Y. Wang, Z. Liu, Optimization of porous media flow field for proton exchange membrane fuel cell using a data-driven surrogate model, Energy Conversion and Management, (2020).
39. R. Pang, C. Zhang, H. Dai, Y. Bai, D. Hao, J. Chen, B. Zhang, Intelligent health states recognition of fuel cell by cell voltage consistency under typical operating parameters, Applied Energy, 305 (2022).
40. L. Shengnan, T. Jiaqi, H. Haoqin, L. Chenlei, X. Dongji, Multi-objective optimization of proton exchange membrane fuel cell geometry and operating parameters based on three new performance evaluation indexes, Energy Conversion and Management, (2023).
41. M. Xuping, L. Shengnan, T. Jiaqi, H. Haoqin, L. Chenlei, X. Dongji, Multi-objective optimization of gradient porosity of gas diffusion layer and operation parameters in PEMFC based on recombination optimization compromise strategy, International Journal of Hydrogen Energy, (2023).
42. X. Guoping, Y. Zeting, X. Lei, W. Changjiang, J. Shaobo, Performance improvement of solid oxide fuel cells by combining three-dimensional CFD modeling, artificial neural network and genetic algorithm, Energy Conversion and Management, (2022).
Copyright (c) 2025 Ting Lu, Yan Liu
This work is licensed under a Creative Commons Attribution 4.0 International License.
Copyright on all articles published in this journal is retained by the author(s), while the author(s) grant the publisher as the original publisher to publish the article.
Articles published in this journal are licensed under a Creative Commons Attribution 4.0 International, which means they can be shared, adapted and distributed provided that the original published version is cited.
Submit a Paper
