Energy and computational efficient estimation of battery intrinsic parameters

Chun Sing Cheng; Ricky Wing Hong Lau; Henry Shu Hung Chung; N. K. Rathi

doi:10.1109/ECCE.2016.7855079

Energy and computational efficient estimation of battery intrinsic parameters

Chun Sing Cheng, Ricky Wing Hong Lau, Henry Shu Hung Chung, N. K. Rathi

Centre for Smart Energy Conversion and Utilization Research

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

Abstract

This paper presents an efficient battery parameter extraction technique with energy recycling feature. Based on transferring the testing energy to and from a supercapacitor (storage device) through a bidirectional DC-DC converter, the charging and discharging current profile of a battery can be obtained for analyzing the battery characteristics and parameters extraction. With the testing energy stored in a supercapacitor, the concern of thermal management is eliminated. By applying a newly modified efficient particle swarm optimization algorithm, the voltage and current data are used to estimate the intrinsic parameters of a high-order electrical battery model. A prototype has been implemented for extracting the intrinsic parameters of four different types of 12V lead-acid battery, and with evaluation current evaluated up to 150A. The estimated parameters have been verified against the theoretical predictions as well as the test results obtained from the NHR battery testing system.

Original language	English
Title of host publication	ECCE 2016 - IEEE Energy Conversion Congress and Exposition, Proceedings
Publisher	IEEE
ISBN (Print)	9781509007370
DOIs	https://doi.org/10.1109/ECCE.2016.7855079
Publication status	Published - 13 Feb 2017
Event	8th IEEE Energy Conversion Congress and Exposition (ECCE 2016) - Milwaukee, United States Duration: 18 Sept 2016 → 22 Sept 2016 http://www.ieee-ecce.org/2016/

Conference

Conference	8th IEEE Energy Conversion Congress and Exposition (ECCE 2016)
Place	United States
City	Milwaukee
Period	18/09/16 → 22/09/16
Internet address	http://www.ieee-ecce.org/2016/

Research Keywords

battery
battery parameters
energy efficient
particle swarm optimization

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.1109/ECCE.2016.7855079

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@inproceedings{9f0e3eb6aef644428dc7fa928bd7e23e,

title = "Energy and computational efficient estimation of battery intrinsic parameters",

abstract = "This paper presents an efficient battery parameter extraction technique with energy recycling feature. Based on transferring the testing energy to and from a supercapacitor (storage device) through a bidirectional DC-DC converter, the charging and discharging current profile of a battery can be obtained for analyzing the battery characteristics and parameters extraction. With the testing energy stored in a supercapacitor, the concern of thermal management is eliminated. By applying a newly modified efficient particle swarm optimization algorithm, the voltage and current data are used to estimate the intrinsic parameters of a high-order electrical battery model. A prototype has been implemented for extracting the intrinsic parameters of four different types of 12V lead-acid battery, and with evaluation current evaluated up to 150A. The estimated parameters have been verified against the theoretical predictions as well as the test results obtained from the NHR battery testing system.",

keywords = "battery, battery parameters, energy efficient, particle swarm optimization",

author = "Cheng, {Chun Sing} and Lau, {Ricky Wing Hong} and Chung, {Henry Shu Hung} and Rathi, {N. K.}",

year = "2017",

month = feb,

day = "13",

doi = "10.1109/ECCE.2016.7855079",

language = "English",

isbn = "9781509007370",

booktitle = "ECCE 2016 - IEEE Energy Conversion Congress and Exposition, Proceedings",

publisher = "IEEE",

address = "United States",

note = "8th IEEE Energy Conversion Congress and Exposition (ECCE 2016) ; Conference date: 18-09-2016 Through 22-09-2016",

url = "http://www.ieee-ecce.org/2016/",

}

Cheng, CS , Lau, RWH , Chung, HSH & Rathi, NK 2017, Energy and computational efficient estimation of battery intrinsic parameters. in ECCE 2016 - IEEE Energy Conversion Congress and Exposition, Proceedings., 7855079, IEEE, 8th IEEE Energy Conversion Congress and Exposition (ECCE 2016), Milwaukee, Wisconsin, United States, 18/09/16. https://doi.org/10.1109/ECCE.2016.7855079

Energy and computational efficient estimation of battery intrinsic parameters. / Cheng, Chun Sing ; Lau, Ricky Wing Hong ; Chung, Henry Shu Hung et al.
ECCE 2016 - IEEE Energy Conversion Congress and Exposition, Proceedings. IEEE, 2017. 7855079.

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

TY - GEN

T1 - Energy and computational efficient estimation of battery intrinsic parameters

AU - Cheng, Chun Sing

AU - Lau, Ricky Wing Hong

AU - Chung, Henry Shu Hung

AU - Rathi, N. K.

PY - 2017/2/13

Y1 - 2017/2/13

N2 - This paper presents an efficient battery parameter extraction technique with energy recycling feature. Based on transferring the testing energy to and from a supercapacitor (storage device) through a bidirectional DC-DC converter, the charging and discharging current profile of a battery can be obtained for analyzing the battery characteristics and parameters extraction. With the testing energy stored in a supercapacitor, the concern of thermal management is eliminated. By applying a newly modified efficient particle swarm optimization algorithm, the voltage and current data are used to estimate the intrinsic parameters of a high-order electrical battery model. A prototype has been implemented for extracting the intrinsic parameters of four different types of 12V lead-acid battery, and with evaluation current evaluated up to 150A. The estimated parameters have been verified against the theoretical predictions as well as the test results obtained from the NHR battery testing system.

AB - This paper presents an efficient battery parameter extraction technique with energy recycling feature. Based on transferring the testing energy to and from a supercapacitor (storage device) through a bidirectional DC-DC converter, the charging and discharging current profile of a battery can be obtained for analyzing the battery characteristics and parameters extraction. With the testing energy stored in a supercapacitor, the concern of thermal management is eliminated. By applying a newly modified efficient particle swarm optimization algorithm, the voltage and current data are used to estimate the intrinsic parameters of a high-order electrical battery model. A prototype has been implemented for extracting the intrinsic parameters of four different types of 12V lead-acid battery, and with evaluation current evaluated up to 150A. The estimated parameters have been verified against the theoretical predictions as well as the test results obtained from the NHR battery testing system.

KW - battery

KW - battery parameters

KW - energy efficient

KW - particle swarm optimization

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UR - https://www.scopus.com/record/pubmetrics.uri?eid=2-s2.0-85015399982&origin=recordpage

U2 - 10.1109/ECCE.2016.7855079

DO - 10.1109/ECCE.2016.7855079

M3 - RGC 32 - Refereed conference paper (with host publication)

SN - 9781509007370

BT - ECCE 2016 - IEEE Energy Conversion Congress and Exposition, Proceedings

PB - IEEE

T2 - 8th IEEE Energy Conversion Congress and Exposition (ECCE 2016)

Y2 - 18 September 2016 through 22 September 2016

ER -

Energy and computational efficient estimation of battery intrinsic parameters

Abstract

Conference

Research Keywords

UN SDGs

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8th Annual IEEE Energy Conversion Congress and Exposition (ECCE 2016)

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Energy and computational efficient estimation of battery intrinsic parameters

Abstract

Conference

Research Keywords

UN SDGs

Access Output

Other Access Options

Permanent Link

Other Files and Links

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8th Annual IEEE Energy Conversion Congress and Exposition (ECCE 2016)

Cite this