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:
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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
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