Data redundancy reduction using sensitivity analysis method for machine-learning-based battery management system

This thesis proposes a sensitivity analysis method to reduce the computational effort of machine-learning (ML) techniques in the battery management system (BMS). The novel approach analyzes the sensitivity of lithium-ion battery model parameters towards their discharge performances. The sensitivity...

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Main Author:	Mustaza, Muhammad Syafiq Anwar
Format:	Thesis
Language:	English English English
Published:	2021
Subjects:	TA1501-1820 Applied optics Photonics
Online Access:	http://eprints.uthm.edu.my/987/1/24p%20MUHAMMAD%20SYAFIQ%20ANWAR%20BIN%20MUSTAZA.pdf http://eprints.uthm.edu.my/987/4/MUHAMMAD%20SYAFIQ%20ANWAR%20BIN%20MUSTAZA%20COPYRIGHT%20DECLARATION.pdf http://eprints.uthm.edu.my/987/5/MUHAMMAD%20SYAFIQ%20ANWAR%20BIN%20MUSTAZA%20WATERMARK.pdf
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spelling	my-uthm-ep.9872021-08-22T08:06:35Z Data redundancy reduction using sensitivity analysis method for machine-learning-based battery management system 2021-01 Mustaza, Muhammad Syafiq Anwar TA1501-1820 Applied optics. Photonics This thesis proposes a sensitivity analysis method to reduce the computational effort of machine-learning (ML) techniques in the battery management system (BMS). The novel approach analyzes the sensitivity of lithium-ion battery model parameters towards their discharge performances. The sensitivity analysis is based on the sum-of-difference method to identify redundant model parameters that characterized the battery’s discharge performance. From the sensitivity analysis, it is found out that the current, discharge time from state-of-charge (SOC), and power discharge output show minimum influence towards the variation of battery parameters. Thus, this finding indicates that current, discharge time, and power were redundant and may be excluded in the formation of the training dataset. The newly discovered finding is applied to the ML-based BMS. In the development of the training dataset, a reduced-sized dataset is formed by excluding current, discharge time, and power from the training dataset for the real-time battery-parameter monitoring in BMS. The newly formed reduced-sized dataset is applied to ML techniques: artificial neural network (ANN), deep learning (DL), and modified adaptive neuro-fuzzy inference system (MANFIS). Consequently, the training performances of all three ML techniques are observed, analyzed, and compared. The results demonstrate that the reduced-sized training dataset that is formed based on the sum-of-different method reduced the training time by up to 60.25% as compared with the full-sized dataset. Also, estimation accuracy is improved due to the improvement in training data bias. This result suggests that the proposed method significantly improved the training performance of the ML techniques in BMS application. The implementation of sensitivity analysis in the development of the training dataset for ML applications improved the performance of the real-time monitoring of lithium-ion battery parameters in advanced BMS applications. 2021-01 Thesis http://eprints.uthm.edu.my/987/ http://eprints.uthm.edu.my/987/1/24p%20MUHAMMAD%20SYAFIQ%20ANWAR%20BIN%20MUSTAZA.pdf text en public http://eprints.uthm.edu.my/987/4/MUHAMMAD%20SYAFIQ%20ANWAR%20BIN%20MUSTAZA%20COPYRIGHT%20DECLARATION.pdf text en staffonly http://eprints.uthm.edu.my/987/5/MUHAMMAD%20SYAFIQ%20ANWAR%20BIN%20MUSTAZA%20WATERMARK.pdf text en validuser mphil masters Universiti Tun Hussein Onn Malaysia Faculty of Electrical and Electronic Engineering
institution	Universiti Tun Hussein Onn Malaysia
collection	UTHM Institutional Repository
language	English English English
topic	TA1501-1820 Applied optics Photonics
spellingShingle	TA1501-1820 Applied optics Photonics Mustaza, Muhammad Syafiq Anwar Data redundancy reduction using sensitivity analysis method for machine-learning-based battery management system
description	This thesis proposes a sensitivity analysis method to reduce the computational effort of machine-learning (ML) techniques in the battery management system (BMS). The novel approach analyzes the sensitivity of lithium-ion battery model parameters towards their discharge performances. The sensitivity analysis is based on the sum-of-difference method to identify redundant model parameters that characterized the battery’s discharge performance. From the sensitivity analysis, it is found out that the current, discharge time from state-of-charge (SOC), and power discharge output show minimum influence towards the variation of battery parameters. Thus, this finding indicates that current, discharge time, and power were redundant and may be excluded in the formation of the training dataset. The newly discovered finding is applied to the ML-based BMS. In the development of the training dataset, a reduced-sized dataset is formed by excluding current, discharge time, and power from the training dataset for the real-time battery-parameter monitoring in BMS. The newly formed reduced-sized dataset is applied to ML techniques: artificial neural network (ANN), deep learning (DL), and modified adaptive neuro-fuzzy inference system (MANFIS). Consequently, the training performances of all three ML techniques are observed, analyzed, and compared. The results demonstrate that the reduced-sized training dataset that is formed based on the sum-of-different method reduced the training time by up to 60.25% as compared with the full-sized dataset. Also, estimation accuracy is improved due to the improvement in training data bias. This result suggests that the proposed method significantly improved the training performance of the ML techniques in BMS application. The implementation of sensitivity analysis in the development of the training dataset for ML applications improved the performance of the real-time monitoring of lithium-ion battery parameters in advanced BMS applications.
format	Thesis
qualification_name	Master of Philosophy (M.Phil.)
qualification_level	Master's degree
author	Mustaza, Muhammad Syafiq Anwar
author_facet	Mustaza, Muhammad Syafiq Anwar
author_sort	Mustaza, Muhammad Syafiq Anwar
title	Data redundancy reduction using sensitivity analysis method for machine-learning-based battery management system
title_short	Data redundancy reduction using sensitivity analysis method for machine-learning-based battery management system
title_full	Data redundancy reduction using sensitivity analysis method for machine-learning-based battery management system
title_fullStr	Data redundancy reduction using sensitivity analysis method for machine-learning-based battery management system
title_full_unstemmed	Data redundancy reduction using sensitivity analysis method for machine-learning-based battery management system
title_sort	data redundancy reduction using sensitivity analysis method for machine-learning-based battery management system
granting_institution	Universiti Tun Hussein Onn Malaysia
granting_department	Faculty of Electrical and Electronic Engineering
publishDate	2021
url	http://eprints.uthm.edu.my/987/1/24p%20MUHAMMAD%20SYAFIQ%20ANWAR%20BIN%20MUSTAZA.pdf http://eprints.uthm.edu.my/987/4/MUHAMMAD%20SYAFIQ%20ANWAR%20BIN%20MUSTAZA%20COPYRIGHT%20DECLARATION.pdf http://eprints.uthm.edu.my/987/5/MUHAMMAD%20SYAFIQ%20ANWAR%20BIN%20MUSTAZA%20WATERMARK.pdf
_version_	1747830723857874944

Data redundancy reduction using sensitivity analysis method for machine-learning-based battery management system

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