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Optimal neural network approach for estimating state of energy of lithium-ion battery using heuristic optimization techniques

Backpropagation algorithms; Errors; Learning algorithms; Mean square error; Neural networks; Particle swarm optimization (PSO); Torsional stress; Back propagation neural networks; Backtracking search algorithms; Heuristic optimization technique; Optimal neural network; Optimization algorithms; Parti...

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Main Authors: Lipu M.S.H., Hussain A., Saad M.H.M., Hannan M.A.
Other Authors: 36518949700
Format: Conference Paper
Published: Institute of Electrical and Electronics Engineers Inc. 2023
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spelling my.uniten.dspace-238742023-05-29T14:52:39Z Optimal neural network approach for estimating state of energy of lithium-ion battery using heuristic optimization techniques Lipu M.S.H. Hussain A. Saad M.H.M. Hannan M.A. 36518949700 57208481391 7202075525 7103014445 Backpropagation algorithms; Errors; Learning algorithms; Mean square error; Neural networks; Particle swarm optimization (PSO); Torsional stress; Back propagation neural networks; Backtracking search algorithms; Heuristic optimization technique; Optimal neural network; Optimization algorithms; Particle swarm optimization algorithm; Root mean square errors; state of energy; Lithium-ion batteries This paper presents an optimal state of energy (SOE) estimation strategy of a lithium-ion battery using the back-propagation neural network (BPNN). Two heuristic optmization techniques named backtracketing search algorithm (BSA) and particle swarm optimization (PSO) algorithm are applied to improve the accuracy of BPNN model. Optimization algorithms are developed to determine the optimal value of hidden layer neurons and learning rate of BPNN model. Three most influencing factors including current, voltage and temperature are considered as the inputs to the optimal BPNN model. Federal Urban Driving Schedule (FUDS) is used to check the model robustness at 0�C, 25�C and 45�C. The model performance is evaluated based on the root mean square error (RMSE) and mean absolute error (MAE). The results show that the proposed model obtains good accuracy with an absolute error of �5%. The BPNN based BSA model improves the SOE estimation accuracy by reducing RMSE and MAE by 2.8% and 4.4% compared to BPNN based PSO model at 25�C. � 2017 IEEE. Final 2023-05-29T06:52:39Z 2023-05-29T06:52:39Z 2018 Conference Paper 10.1109/ICEEI.2017.8312418 2-s2.0-85050757119 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85050757119&doi=10.1109%2fICEEI.2017.8312418&partnerID=40&md5=d7fdbdec3d9b85fff0234b2a42c0d5f5 https://irepository.uniten.edu.my/handle/123456789/23874 2017-November 1 6 Institute of Electrical and Electronics Engineers Inc. Scopus
institution Universiti Tenaga Nasional
building UNITEN Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Tenaga Nasional
content_source UNITEN Institutional Repository
url_provider http://dspace.uniten.edu.my/
description Backpropagation algorithms; Errors; Learning algorithms; Mean square error; Neural networks; Particle swarm optimization (PSO); Torsional stress; Back propagation neural networks; Backtracking search algorithms; Heuristic optimization technique; Optimal neural network; Optimization algorithms; Particle swarm optimization algorithm; Root mean square errors; state of energy; Lithium-ion batteries
author2 36518949700
author_facet 36518949700
Lipu M.S.H.
Hussain A.
Saad M.H.M.
Hannan M.A.
format Conference Paper
author Lipu M.S.H.
Hussain A.
Saad M.H.M.
Hannan M.A.
spellingShingle Lipu M.S.H.
Hussain A.
Saad M.H.M.
Hannan M.A.
Optimal neural network approach for estimating state of energy of lithium-ion battery using heuristic optimization techniques
author_sort Lipu M.S.H.
title Optimal neural network approach for estimating state of energy of lithium-ion battery using heuristic optimization techniques
title_short Optimal neural network approach for estimating state of energy of lithium-ion battery using heuristic optimization techniques
title_full Optimal neural network approach for estimating state of energy of lithium-ion battery using heuristic optimization techniques
title_fullStr Optimal neural network approach for estimating state of energy of lithium-ion battery using heuristic optimization techniques
title_full_unstemmed Optimal neural network approach for estimating state of energy of lithium-ion battery using heuristic optimization techniques
title_sort optimal neural network approach for estimating state of energy of lithium-ion battery using heuristic optimization techniques
publisher Institute of Electrical and Electronics Engineers Inc.
publishDate 2023
_version_ 1806423458054144000
score 13.214268

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