A Comparative study on state of charge estimation techniques for Lithium-ion Batteries

Aryal, A; Hossain, MJ; Khalilpour, K

A Comparative study on state of charge estimation techniques for Lithium-ion Batteries

Aryal, A Hossain, MJ Khalilpour, K

Permalink

Publisher:: IEEE
Publication Type:: Conference Proceeding
Citation:: 2021 IEEE PES Innovative Smart Grid Technologies - Asia (ISGT Asia), 2022, 00, pp. 1-5
Issue Date:: 2022-01-01

Open Access

Copyright Clearance Process

Recently Added
In Progress
Open Access

This item is open access.

Adobe PDF

Download Accepted versionAdobe PDF (2.61 MB)

View on publisher's site

View statistics

Full metadata record

Field	Value	Language
dc.contributor.author	Aryal, A
dc.contributor.author	Hossain, MJ
dc.contributor.author	Khalilpour, K https://orcid.org/0000-0003-4104-2488
dc.date	2021-12-05
dc.date.accessioned	2023-03-22T00:53:25Z
dc.date.available	2023-03-22T00:53:25Z
dc.date.issued	2022-01-01
dc.identifier.citation	2021 IEEE PES Innovative Smart Grid Technologies - Asia (ISGT Asia), 2022, 00, pp. 1-5
dc.identifier.isbn	978-1-6654-3339-6
dc.identifier.issn	2378-8534
dc.identifier.issn	2378-8542
dc.identifier.uri	http://hdl.handle.net/10453/168027
dc.description.abstract	State of Charge (SOC) estimation is significantly important for the optimal utilization and protection of batteries. This paper implements and compares the performance of a neural network (NN) algorithm and Coulomb Counting method for estimating state of charge (SOC) for batteries. This algorithm is applied to a battery management system (BMS) in electric vehicles. Accurate SOC information can avoid over charging and over discharging of battery, and thus increase battery life. Also, control system uses accurate SOC information to make rational decisions to save energy in electric vehicles. The advantage of NN model over Coulomb Counting method is it can be implemented in BMS Hardware where online measurements like current, voltage and temperature are available. The feature of this neural network approach is that it optimizes two important hyper-parameters to achieve a reasonable MAPE error. The performance of the proposed method is tested using two Datasets for city driving conditions. The results reveal that both methods (NN and Coulomb counting) can predict SOC with reasonable error (<6%). However, Coulomb counting outperforms Neural network MAPE for both Datasets.
dc.language	en
dc.publisher	IEEE
dc.relation.ispartof	2021 IEEE PES Innovative Smart Grid Technologies - Asia (ISGT Asia)
dc.relation.ispartof	IEEE PES Innovative Smart Grid Technologies - Asia Conference
dc.relation.isbasedon	10.1109/ISGTASIA49270.2021.9715593
dc.rights	info:eu-repo/semantics/openAccess
dc.title	A Comparative study on state of charge estimation techniques for Lithium-ion Batteries
dc.type	Conference Proceeding
utslib.citation.volume	00
utslib.location.activity	Brisbane, AUSTRALIA
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Electrical and Data Engineering
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Professional Practice and Leadership
utslib.copyright.status	open_access	*
pubs.consider-herdc	false
dc.date.updated	2023-03-22T00:53:23Z
pubs.finish-date	2021-12-08
pubs.place-of-publication	Piscataway, USA
pubs.publication-status	Published
pubs.start-date	2021-12-05
pubs.volume	00
dc.location	Piscataway, USA

Abstract:

State of Charge (SOC) estimation is significantly important for the optimal utilization and protection of batteries. This paper implements and compares the performance of a neural network (NN) algorithm and Coulomb Counting method for estimating state of charge (SOC) for batteries. This algorithm is applied to a battery management system (BMS) in electric vehicles. Accurate SOC information can avoid over charging and over discharging of battery, and thus increase battery life. Also, control system uses accurate SOC information to make rational decisions to save energy in electric vehicles. The advantage of NN model over Coulomb Counting method is it can be implemented in BMS Hardware where online measurements like current, voltage and temperature are available. The feature of this neural network approach is that it optimizes two important hyper-parameters to achieve a reasonable MAPE error. The performance of the proposed method is tested using two Datasets for city driving conditions. The results reveal that both methods (NN and Coulomb counting) can predict SOC with reasonable error (<6%). However, Coulomb counting outperforms Neural network MAPE for both Datasets.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/168027