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Neural network approach for estimating state of charge of lithium-ion battery using backtracking search algorithm

Backpropagation; Backpropagation algorithms; Charging (batteries); Electric batteries; Electric vehicles; Errors; Ions; Learning algorithms; Learning systems; Lithium; Lithium-ion batteries; Mean square error; Neural networks; Optimization; Radial basis function networks; Secondary batteries; Torsio...

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Main Authors: Hannan M.A., Lipu M.S.H., Hussain A., Saad M.H., Ayob A.
Other Authors: 7103014445
Format: Article
Published: Institute of Electrical and Electronics Engineers Inc. 2023
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spelling my.uniten.dspace-239262023-05-29T14:53:13Z Neural network approach for estimating state of charge of lithium-ion battery using backtracking search algorithm Hannan M.A. Lipu M.S.H. Hussain A. Saad M.H. Ayob A. 7103014445 36518949700 57208481391 7202075525 26666566900 Backpropagation; Backpropagation algorithms; Charging (batteries); Electric batteries; Electric vehicles; Errors; Ions; Learning algorithms; Learning systems; Lithium; Lithium-ion batteries; Mean square error; Neural networks; Optimization; Radial basis function networks; Secondary batteries; Torsional stress; Back propagation neural networks; Backtracking search algorithms; Battery residual capacity; Extreme learning machine; Generalized Regression Neural Network(GRNN); Mean absolute percentage error; Radial basis function neural networks; State of charge; Battery management systems The state of charge (SOC) is a critical evaluation index of battery residual capacity. The significance of an accurate SOC estimation is great for a lithium-ion battery to ensure its safe operation and to prevent from over-charging or over-discharging. However, to estimate an accurate capacity of SOC of the lithium-ion battery has become a major concern for the electric vehicle (EV) industry. Therefore, numerous researches are being conducted to address the challenges and to enhance the battery performance. The main objective of this paper is to develop an accurate SOC estimation approach for a lithium-ion battery by improving back-propagation neural network (BPNN) capability using backtracking search algorithm (BSA). BSA optimization is utilized to improve the accuracy and robustness of BPNN model by finding the optimal value of hidden layer neurons and learning rate. In this paper, Dynamic Stress Test and Federal Urban Driving Schedule drive profiles are applied for testing the model at three different temperatures. The obtained results of the BPNN based BSA model are compared with the radial basis function neural network, generalized regression neural network and extreme learning machine model using statistical error values of root mean square error, mean absolute error, mean absolute percentage error, and SOC error to check and validate the model performance. The obtained results show that the BPNN based BSA model outperforms other neural network models in estimating SOC with high accuracy under different EV profiles and temperatures. � 2013 IEEE. Final 2023-05-29T06:53:13Z 2023-05-29T06:53:13Z 2018 Article 10.1109/ACCESS.2018.2797976 2-s2.0-85040974639 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85040974639&doi=10.1109%2fACCESS.2018.2797976&partnerID=40&md5=624fe570a4d3e072457b6386d1dd3917 https://irepository.uniten.edu.my/handle/123456789/23926 6 10069 10079 All Open Access, Gold 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; Backpropagation algorithms; Charging (batteries); Electric batteries; Electric vehicles; Errors; Ions; Learning algorithms; Learning systems; Lithium; Lithium-ion batteries; Mean square error; Neural networks; Optimization; Radial basis function networks; Secondary batteries; Torsional stress; Back propagation neural networks; Backtracking search algorithms; Battery residual capacity; Extreme learning machine; Generalized Regression Neural Network(GRNN); Mean absolute percentage error; Radial basis function neural networks; State of charge; Battery management systems
author2 7103014445
author_facet 7103014445
Hannan M.A.
Lipu M.S.H.
Hussain A.
Saad M.H.
Ayob A.
format Article
author Hannan M.A.
Lipu M.S.H.
Hussain A.
Saad M.H.
Ayob A.
spellingShingle Hannan M.A.
Lipu M.S.H.
Hussain A.
Saad M.H.
Ayob A.
Neural network approach for estimating state of charge of lithium-ion battery using backtracking search algorithm
author_sort Hannan M.A.
title Neural network approach for estimating state of charge of lithium-ion battery using backtracking search algorithm
title_short Neural network approach for estimating state of charge of lithium-ion battery using backtracking search algorithm
title_full Neural network approach for estimating state of charge of lithium-ion battery using backtracking search algorithm
title_fullStr Neural network approach for estimating state of charge of lithium-ion battery using backtracking search algorithm
title_full_unstemmed Neural network approach for estimating state of charge of lithium-ion battery using backtracking search algorithm
title_sort neural network approach for estimating state of charge of lithium-ion battery using backtracking search algorithm
publisher Institute of Electrical and Electronics Engineers Inc.
publishDate 2023
_version_ 1806427725374685184
score 13.214268

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