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

Mustaza, Muhammad Syafiq Anwar (2021) Data redundancy reduction using sensitivity analysis method for machine-learning-based battery management system. Masters thesis, Universiti Tun Hussein Onn Malaysia.

Preview	Text 24p MUHAMMAD SYAFIQ ANWAR BIN MUSTAZA.pdf Download (502kB) | Preview
	Text (Copyright Declaration) MUHAMMAD SYAFIQ ANWAR BIN MUSTAZA COPYRIGHT DECLARATION.pdf Restricted to Repository staff only Download (284kB) | Request a copy
	Text (Full Text) MUHAMMAD SYAFIQ ANWAR BIN MUSTAZA WATERMARK.pdf Restricted to Registered users only Download (3MB) | Request a copy

Abstract

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.

Item Type:	Thesis (Masters)
Subjects:	T Technology > TA Engineering (General). Civil engineering (General) > TA1501-1820 Applied optics. Photonics
Divisions:	Faculty of Electrical and Electronic Engineering > Department of Electrical Engineering
Depositing User:	Miss Afiqah Faiqah Mohd Hafiz
Date Deposited:	22 Aug 2021 08:06
Last Modified:	22 Aug 2021 08:06
URI:	http://eprints.uthm.edu.my/id/eprint/987

Actions (login required)

View Item