Li-ion battery parameter identification with low pass filter for measurement noise rejection

Cong Sheng Huang; Tommy Wai-Shing Chow; Mo-Yuen Chow

doi:10.1109/ISIE.2017.8001575

Li-ion battery parameter identification with low pass filter for measurement noise rejection

Cong Sheng Huang
, Tommy Wai-Shing Chow
, Mo-Yuen Chow

Department of Electrical Engineering

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

24 Citations (Scopus)

Abstract

The advent of Energy Management (EM) and Electric Vehicles (EV) have completely changed the use of batteries. Accurately estimating the remaining power in batteries has become increasingly important. In order to estimate precise battery state of charge (SOC)/state of health (SOH) value, accurate parameter identification is essential when constructing an accurate battery model. Even though we are able to exactly identify battery parameters offline, the precision of online parameter identification usually suffers from measurement noise, which is an unavoidable phenomenon. In this paper we investigate how battery parameter identification is influenced by measurement noise. The selection of a low pass filter is also discussed and a fourth order Butterworth filter is adopted to effectively reject high frequency measurement noise. This
algorithm can help with the identification of battery parameter that rejects measurement noise and maintains the accuracy of online battery parameter identification for future online modelbased battery SOC/SOH estimation.

Original language	English
Title of host publication	2017 IEEE 26th International Symposium on Industrial Electronics (ISIE)
Publisher	IEEE
Pages	2075-2080
ISBN (Print)	9781509014125
DOIs	https://doi.org/10.1109/ISIE.2017.8001575
Publication status	Published - Aug 2017
Event	26th IEEE International Symposium on Industrial Electronics, ISIE 2017 - Edinburgh, Scotland, United Kingdom Duration: 18 Jun 2017 → 21 Jun 2017

Conference

Conference	26th IEEE International Symposium on Industrial Electronics, ISIE 2017
Place	United Kingdom
City	Edinburgh, Scotland
Period	18/06/17 → 21/06/17

UN SDGs

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

SDG 7 Affordable and Clean Energy

Research Keywords

Battery parameter identification
Butterworth filter
Low pass filter
Measurement noise
RC time constant
Thevenin's circuit model

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10.1109/ISIE.2017.8001575

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@inproceedings{4dd6ea2433f14175ac2af88dcc9cdc5e,

title = "Li-ion battery parameter identification with low pass filter for measurement noise rejection",

abstract = " The advent of Energy Management (EM) and Electric Vehicles (EV) have completely changed the use of batteries. Accurately estimating the remaining power in batteries has become increasingly important. In order to estimate precise battery state of charge (SOC)/state of health (SOH) value, accurate parameter identification is essential when constructing an accurate battery model. Even though we are able to exactly identify battery parameters offline, the precision of online parameter identification usually suffers from measurement noise, which is an unavoidable phenomenon. In this paper we investigate how battery parameter identification is influenced by measurement noise. The selection of a low pass filter is also discussed and a fourth order Butterworth filter is adopted to effectively reject high frequency measurement noise. This algorithm can help with the identification of battery parameter that rejects measurement noise and maintains the accuracy of online battery parameter identification for future online modelbased battery SOC/SOH estimation. ",

keywords = "Battery parameter identification, Butterworth filter, Low pass filter, Measurement noise, RC time constant, Thevenin's circuit model",

author = "Huang, \{Cong Sheng\} and Chow, \{Tommy Wai-Shing\} and Mo-Yuen Chow",

year = "2017",

month = aug,

doi = "10.1109/ISIE.2017.8001575",

language = "English",

isbn = "9781509014125",

pages = "2075--2080",

booktitle = "2017 IEEE 26th International Symposium on Industrial Electronics (ISIE)",

publisher = "IEEE",

address = "United States",

note = "26th IEEE International Symposium on Industrial Electronics, ISIE 2017 ; Conference date: 18-06-2017 Through 21-06-2017",

}

Huang, CS, Chow, TW-S & Chow, M-Y 2017, Li-ion battery parameter identification with low pass filter for measurement noise rejection. in 2017 IEEE 26th International Symposium on Industrial Electronics (ISIE)., 8001575, IEEE, pp. 2075-2080, 26th IEEE International Symposium on Industrial Electronics, ISIE 2017, Edinburgh, Scotland, United Kingdom, 18/06/17. https://doi.org/10.1109/ISIE.2017.8001575

Li-ion battery parameter identification with low pass filter for measurement noise rejection. / Huang, Cong Sheng; Chow, Tommy Wai-Shing; Chow, Mo-Yuen.
2017 IEEE 26th International Symposium on Industrial Electronics (ISIE). IEEE, 2017. p. 2075-2080 8001575.

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

TY - GEN

T1 - Li-ion battery parameter identification with low pass filter for measurement noise rejection

AU - Huang, Cong Sheng

AU - Chow, Tommy Wai-Shing

AU - Chow, Mo-Yuen

PY - 2017/8

Y1 - 2017/8

N2 - The advent of Energy Management (EM) and Electric Vehicles (EV) have completely changed the use of batteries. Accurately estimating the remaining power in batteries has become increasingly important. In order to estimate precise battery state of charge (SOC)/state of health (SOH) value, accurate parameter identification is essential when constructing an accurate battery model. Even though we are able to exactly identify battery parameters offline, the precision of online parameter identification usually suffers from measurement noise, which is an unavoidable phenomenon. In this paper we investigate how battery parameter identification is influenced by measurement noise. The selection of a low pass filter is also discussed and a fourth order Butterworth filter is adopted to effectively reject high frequency measurement noise. This algorithm can help with the identification of battery parameter that rejects measurement noise and maintains the accuracy of online battery parameter identification for future online modelbased battery SOC/SOH estimation.

AB - The advent of Energy Management (EM) and Electric Vehicles (EV) have completely changed the use of batteries. Accurately estimating the remaining power in batteries has become increasingly important. In order to estimate precise battery state of charge (SOC)/state of health (SOH) value, accurate parameter identification is essential when constructing an accurate battery model. Even though we are able to exactly identify battery parameters offline, the precision of online parameter identification usually suffers from measurement noise, which is an unavoidable phenomenon. In this paper we investigate how battery parameter identification is influenced by measurement noise. The selection of a low pass filter is also discussed and a fourth order Butterworth filter is adopted to effectively reject high frequency measurement noise. This algorithm can help with the identification of battery parameter that rejects measurement noise and maintains the accuracy of online battery parameter identification for future online modelbased battery SOC/SOH estimation.

KW - Battery parameter identification

KW - Butterworth filter

KW - Low pass filter

KW - Measurement noise

KW - RC time constant

KW - Thevenin's circuit model

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U2 - 10.1109/ISIE.2017.8001575

DO - 10.1109/ISIE.2017.8001575

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

SN - 9781509014125

SP - 2075

EP - 2080

BT - 2017 IEEE 26th International Symposium on Industrial Electronics (ISIE)

PB - IEEE

T2 - 26th IEEE International Symposium on Industrial Electronics, ISIE 2017

Y2 - 18 June 2017 through 21 June 2017

ER -

Li-ion battery parameter identification with low pass filter for measurement noise rejection

Abstract

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UN SDGs

Research Keywords

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