Levenberg‐Marquardt backpropagation algorithm for parameter identification of solid oxide fuel cells

Summary Fast and precise identification of several unknown parameters for solid oxide fuel cell (SOFC) models play a critical role in modeling analysis, optimal control, and behavior prediction. However, inherent high‐nonlinear, multi‐variable, and strongly coupled features usually lead to thorny ob...

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Published in:International journal of energy research Vol. 45; no. 12; pp. 17903 - 17923
Main Authors: Yang, Bo, Chen, Yijun, Guo, Zhengxun, Wang, Jingbo, Zeng, Chunyuan, Li, Danyang, Shu, Hongchun, Shan, Jieshan, Fu, Ting, Zhang, Xiaoshun
Format: Journal Article
Language:English
Published: Chichester, UK John Wiley & Sons, Inc 10.10.2021
Subjects:
Accuracy
Algorithms
artificial neural network
Artificial neural networks
Back propagation
Back propagation networks
Cell culture
Electrochemistry
Extracellular matrix
Fuel cells
Fuel technology
Heuristic methods
Identification
Identification methods
Levenberg‐Marquardt backpropagation
Mathematical models
Neural networks
Optimal control
Optimization
optimization methods
Parameter estimation
Parameter identification
parameter identification/estimation
Parameters
solid oxide fuel cell
Solid oxide fuel cells
Stability
ISSN:0363-907X, 1099-114X
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Summary:Summary Fast and precise identification of several unknown parameters for solid oxide fuel cell (SOFC) models play a critical role in modeling analysis, optimal control, and behavior prediction. However, inherent high‐nonlinear, multi‐variable, and strongly coupled features usually lead to thorny obstacles that hinder conventional methods to identify them with a high speed, high accuracy, and reliable stability. Hence, a Levenberg‐Marquardt backpropagation (LMBP) algorithm‐based parameter identification technique is proposed in this study, which is applied to efficiently train designed artificial neural networks (ANNs) to implement the identification task. Furthermore, two typical models, for example, electrochemical model (ECM) and steady‐state model (SSM), are taken into account to validate the identification performance of the LMBP algorithm under different operation conditions. Simulation results based on MATLAB demonstrate that the LMBP algorithm can extremely improve the accuracy, speed, and stability for estimating these unknown parameters via a comprehensive comparison with four mainstream meta‐heuristic algorithms, that is, artificial ecosystem‐based optimization (AEO), equilibrium optimizer (EO), grey wolf optimization (GWO), and moth‐flame optimization (MFO).
Bibliography:Funding information
Key Program of National Natural Science Foundation of China, Grant/Award Number: 52037003; Major Special Project of Yunnan Province of China, Grant/Award Number: 202002AF080001; National Natural Science Foundation of China, Grant/Award Numbers: 61963020, 51907112
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ISSN:0363-907X
1099-114X
DOI:10.1002/er.6929