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Enhancing battery state of charge estimation through hybrid integration of barnacles mating optimizer with deep learning

The precise determination of battery state of charge (SoC) holds paramount significance and has garnered considerable attention across diverse sectors, including academia. Accurate knowledge of the SoC percentage offers numerous advantages, ranging from optimizing travel planning to enhancing the ef...

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Main Authors:	Zuriani, Mustaffa, Mohd Herwan, Sulaiman
Format:	Article
Language:	English
Published:	Elsevier
Subjects:	QA75 Electronic computers. Computer science TK Electrical engineering. Electronics Nuclear engineering
Online Access:	http://umpir.ump.edu.my/id/eprint/41469/1/Enhancing%20battery%20state%20of%20charge%20estimation%20through%20hybrid%20integration.pdf http://umpir.ump.edu.my/id/eprint/41469/ https://doi.org/10.1016/j.fraope.2023.100053 https://doi.org/10.1016/j.fraope.2023.100053
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id	my.ump.umpir.41469
record_format	eprints
spelling	my.ump.umpir.414692024-06-05T08:28:01Z http://umpir.ump.edu.my/id/eprint/41469/ Enhancing battery state of charge estimation through hybrid integration of barnacles mating optimizer with deep learning Zuriani, Mustaffa Mohd Herwan, Sulaiman QA75 Electronic computers. Computer science TK Electrical engineering. Electronics Nuclear engineering The precise determination of battery state of charge (SoC) holds paramount significance and has garnered considerable attention across diverse sectors, including academia. Accurate knowledge of the SoC percentage offers numerous advantages, ranging from optimizing travel planning to enhancing the efficiency and reliability of electric vehicle operations through effective battery management systems. In response to the growing importance of SoC estimation, this study introduces a hybrid approach called the Barnacles Mating Optimizer with Deep Learning (BMO-DL) for SoC of Nissan Leaf batteries. The conventional methods for SoC estimation often suffer from limitations in accuracy and robustness, leading to suboptimal EV performance and battery management. In contrast, BMO-DL leverages the power of BMO algorithm to fine-tune the hyperparameters of DL, which is subsequently employed for the actual estimation. This synergistic combination enhances the accuracy and reliability of SoC estimation. The estimation model takes three inputs: voltage, current and conducted charge to generate a single output, the SoC percentage. he study's findings underscore the superiority of BMO-DL by revealing its capability to achieve significantly better results compared to the other benchmarking methods identified. Notably, BMO-DL exhibits significantly lower error rates when compared to competing algorithms, thereby reinforcing its potential to advance the efficiency and reliability of electric vehicle operations while addressing the critical challenge of SoC prediction. Elsevier Article PeerReviewed pdf en cc_by_4 http://umpir.ump.edu.my/id/eprint/41469/1/Enhancing%20battery%20state%20of%20charge%20estimation%20through%20hybrid%20integration.pdf Zuriani, Mustaffa and Mohd Herwan, Sulaiman Enhancing battery state of charge estimation through hybrid integration of barnacles mating optimizer with deep learning. Franklin Open, 5 (100053). ISSN 2773-1863. (Published) https://doi.org/10.1016/j.fraope.2023.100053 https://doi.org/10.1016/j.fraope.2023.100053
institution	Universiti Malaysia Pahang Al-Sultan Abdullah
building	UMPSA Library
collection	Institutional Repository
continent	Asia
country	Malaysia
content_provider	Universiti Malaysia Pahang Al-Sultan Abdullah
content_source	UMPSA Institutional Repository
url_provider	http://umpir.ump.edu.my/
language	English
topic	QA75 Electronic computers. Computer science TK Electrical engineering. Electronics Nuclear engineering
spellingShingle	QA75 Electronic computers. Computer science TK Electrical engineering. Electronics Nuclear engineering Zuriani, Mustaffa Mohd Herwan, Sulaiman Enhancing battery state of charge estimation through hybrid integration of barnacles mating optimizer with deep learning
description	The precise determination of battery state of charge (SoC) holds paramount significance and has garnered considerable attention across diverse sectors, including academia. Accurate knowledge of the SoC percentage offers numerous advantages, ranging from optimizing travel planning to enhancing the efficiency and reliability of electric vehicle operations through effective battery management systems. In response to the growing importance of SoC estimation, this study introduces a hybrid approach called the Barnacles Mating Optimizer with Deep Learning (BMO-DL) for SoC of Nissan Leaf batteries. The conventional methods for SoC estimation often suffer from limitations in accuracy and robustness, leading to suboptimal EV performance and battery management. In contrast, BMO-DL leverages the power of BMO algorithm to fine-tune the hyperparameters of DL, which is subsequently employed for the actual estimation. This synergistic combination enhances the accuracy and reliability of SoC estimation. The estimation model takes three inputs: voltage, current and conducted charge to generate a single output, the SoC percentage. he study's findings underscore the superiority of BMO-DL by revealing its capability to achieve significantly better results compared to the other benchmarking methods identified. Notably, BMO-DL exhibits significantly lower error rates when compared to competing algorithms, thereby reinforcing its potential to advance the efficiency and reliability of electric vehicle operations while addressing the critical challenge of SoC prediction.
format	Article
author	Zuriani, Mustaffa Mohd Herwan, Sulaiman
author_facet	Zuriani, Mustaffa Mohd Herwan, Sulaiman
author_sort	Zuriani, Mustaffa
title	Enhancing battery state of charge estimation through hybrid integration of barnacles mating optimizer with deep learning
title_short	Enhancing battery state of charge estimation through hybrid integration of barnacles mating optimizer with deep learning
title_full	Enhancing battery state of charge estimation through hybrid integration of barnacles mating optimizer with deep learning
title_fullStr	Enhancing battery state of charge estimation through hybrid integration of barnacles mating optimizer with deep learning
title_full_unstemmed	Enhancing battery state of charge estimation through hybrid integration of barnacles mating optimizer with deep learning
title_sort	enhancing battery state of charge estimation through hybrid integration of barnacles mating optimizer with deep learning
publisher	Elsevier
url	http://umpir.ump.edu.my/id/eprint/41469/1/Enhancing%20battery%20state%20of%20charge%20estimation%20through%20hybrid%20integration.pdf http://umpir.ump.edu.my/id/eprint/41469/ https://doi.org/10.1016/j.fraope.2023.100053 https://doi.org/10.1016/j.fraope.2023.100053
_version_	1822924379985543168
score	13.232414

Enhancing battery state of charge estimation through hybrid integration of barnacles mating optimizer with deep learning

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