Journal ArticleDOI
Parameter identification of proton exchange membrane fuel cell via Levenberg-Marquardt backpropagation algorithm
Bo Yang,Chunyuan Zeng,Long Wang,Yinyuan Guo,Guanghua Chen,Zhengxun Guo,Yijun Chen,Danyang Li,Pulin Cao,Hongchun Shu,Tao Yu,Jiawei Zhu +11 more
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TLDR
Levenberg-Marquardt backpropagation (LMBP) algorithm based on artificial neural networks (ANNs) is proposed for PEMFC parameter identification and simulation results indicate that LMBP performs a higher accuracy and faster speed for parameter identification.About:
This article is published in International Journal of Hydrogen Energy.The article was published on 2021-06-28. It has received 27 citations till now. The article focuses on the topics: Backpropagation.read more
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Journal ArticleDOI
Model parameters estimation of the proton exchange membrane fuel cell by a Modified Golden Jackal Optimization
M. Habibi Rezaie,Keyvan karamnejadi azar,Armin kardan sani,Ehsan Akbari,Noradin Ghadimi,Navid Razmjooy,Mojtaba Ghadamyari +6 more
TL;DR: In this paper , an optimal model of the proton exchange membrane fuel cell (PEMFC) model and simulating it, the efficiency, and the output power of the fuel cell will be predicted.
Journal ArticleDOI
Application of Machine Learning in Optimizing Proton Exchange Membrane Fuel Cells: A Review
Rui Ding,Shiqiao Zhang,Yawen Chen,Zhiyan Rui,Kang Feng Hua,Yongkang Wu,Xiaoke Li,Xiao Duan,Xuebin Wang,Jia Li,Jianguo Liu +10 more
TL;DR: In this article , a review of the applications and contributions of ML aiming at optimizing PEMFC performance regarding its potential to bring a research paradigm revolution is presented, in addition to introducing and summarizing information for newcomers who are interested in this emerging crosscutting field.
Journal ArticleDOI
An Efficient Parameter Estimation Algorithm for Proton Exchange Membrane Fuel Cells
TL;DR: In this paper, the artificial gorilla troops optimizer (GTO) is adapted for estimating the unknown parameters of the proton exchange membrane fuel cell (PEMFC) model.
Journal ArticleDOI
Thermal-physical modeling and parameter identification method for dynamic model with unmeasurable state in 10-kW scale proton exchange membrane fuel cell system
TL;DR: In this article , the authors used step integration with continued fractions to obtain the theoretical value and using the p-dimensional extremum seeking via simplex tuning to optimize the mean square error between the theoretical and sampled value.
Journal ArticleDOI
A comprehensive and comparative review on parameter estimation methods for modelling proton exchange membrane fuel cell
TL;DR: In this article , the authors present an inclusive review of various technique used for parameter estimation of PEMFC model such as Least Square method, Artificial Neural Networks, metaheuristic techniques and bio-inspired methods.
References
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Book
Neural network design
TL;DR: This book, by the authors of the Neural Network Toolbox for MATLAB, provides a clear and detailed coverage of fundamental neural network architectures and learning rules, as well as methods for training them and their applications to practical problems.
Journal ArticleDOI
Performance modeling of the Ballard Mark IV solid polymer electrolyte fuel cell. II: Empirical model development
TL;DR: In this paper, a parametric model predicting the performance of a solid polymer electrolyte, proton exchange membrane (PEM) fuel cell has been developed using a combination of mechanistic and empirical modeling techniques.
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Advances in stationary and portable fuel cell applications
TL;DR: In this paper, an overview of the technology and its advantages and disadvantages compared with competitive technologies was revealed, and some possible solutions to the challenges were named for both the portable and stationary fuel cell applications.
Journal ArticleDOI
Levenberg–Marquardt Backpropagation Training of Multilayer Neural Networks for State Estimation of a Safety-Critical Cyber-Physical System
TL;DR: Experimental results validate the feasibility and accuracy of the proposed ANN-based method for braking pressure estimation under real deceleration scenarios and compared with other available learning methods.