Abstract
The accurate estimation of state-of-charge (SOC) is one of the most important core functions of a battery management system (BMS), which provides the essential information needed for battery management, monitoring, and status analysis. In this paper, an AC impedance based method is proposed for the accurate estimation of the SOC of a Li-ion battery. First, a 1kHz sinusoidal ripple was injected into a 18650 Li-ion battery and the AC impedance values were measured at 0% to 100% charging status at 10% increments. The correlation between SOC and AC impedance was then determined by linear regression. When it comes to practical application, a sinusoidal ripple is simply superposed into the charge/discharge source. The AC voltage and current of the battery is used to calculate the AC impedance, and this allows a fairly accurate estimation of the battery SOC. Final validation using a power analyzer and actual measurement showed the average error of SOC estimation to be within 4%. This encouraging result showed the proposed method can provide a simple and practical solution for online estimation of the SOC of Li-ion batteries.
| Original language | English |
|---|---|
| Title of host publication | Proceedings of the 2017 IEEE International Conference on Information, Communication and Engineering |
| Subtitle of host publication | Information and Innovation for Modern Technology, ICICE 2017 |
| Editors | Artde Donald Kin-Tak Lam, Stephen D. Prior, Teen-Hang Meen |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| Pages | 53-56 |
| Number of pages | 4 |
| ISBN (Electronic) | 9781538632024 |
| DOIs | |
| Publication status | Published - 2018 Oct 1 |
| Event | 2017 IEEE International Conference on Information, Communication and Engineering, ICICE 2017 - Xiamen, Fujian, China Duration: 2017 Nov 17 → 2017 Nov 20 |
Publication series
| Name | Proceedings of the 2017 IEEE International Conference on Information, Communication and Engineering: Information and Innovation for Modern Technology, ICICE 2017 |
|---|
Other
| Other | 2017 IEEE International Conference on Information, Communication and Engineering, ICICE 2017 |
|---|---|
| Country | China |
| City | Xiamen, Fujian |
| Period | 17-11-17 → 17-11-20 |
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All Science Journal Classification (ASJC) codes
- Computer Networks and Communications
- Hardware and Architecture
- Information Systems and Management
Cite this
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AC impedance based online state-of-charge estimation for Li-ion battery. / Wu, Shing Lih; Chen, Hung Cheng; Tsai, Ming Yang; Lin, Tong Chou; Chen, Liang-Rui.
Proceedings of the 2017 IEEE International Conference on Information, Communication and Engineering: Information and Innovation for Modern Technology, ICICE 2017. ed. / Artde Donald Kin-Tak Lam; Stephen D. Prior; Teen-Hang Meen. Institute of Electrical and Electronics Engineers Inc., 2018. p. 53-56 8479183 (Proceedings of the 2017 IEEE International Conference on Information, Communication and Engineering: Information and Innovation for Modern Technology, ICICE 2017).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
TY - GEN
T1 - AC impedance based online state-of-charge estimation for Li-ion battery
AU - Wu, Shing Lih
AU - Chen, Hung Cheng
AU - Tsai, Ming Yang
AU - Lin, Tong Chou
AU - Chen, Liang-Rui
PY - 2018/10/1
Y1 - 2018/10/1
N2 - The accurate estimation of state-of-charge (SOC) is one of the most important core functions of a battery management system (BMS), which provides the essential information needed for battery management, monitoring, and status analysis. In this paper, an AC impedance based method is proposed for the accurate estimation of the SOC of a Li-ion battery. First, a 1kHz sinusoidal ripple was injected into a 18650 Li-ion battery and the AC impedance values were measured at 0% to 100% charging status at 10% increments. The correlation between SOC and AC impedance was then determined by linear regression. When it comes to practical application, a sinusoidal ripple is simply superposed into the charge/discharge source. The AC voltage and current of the battery is used to calculate the AC impedance, and this allows a fairly accurate estimation of the battery SOC. Final validation using a power analyzer and actual measurement showed the average error of SOC estimation to be within 4%. This encouraging result showed the proposed method can provide a simple and practical solution for online estimation of the SOC of Li-ion batteries.
AB - The accurate estimation of state-of-charge (SOC) is one of the most important core functions of a battery management system (BMS), which provides the essential information needed for battery management, monitoring, and status analysis. In this paper, an AC impedance based method is proposed for the accurate estimation of the SOC of a Li-ion battery. First, a 1kHz sinusoidal ripple was injected into a 18650 Li-ion battery and the AC impedance values were measured at 0% to 100% charging status at 10% increments. The correlation between SOC and AC impedance was then determined by linear regression. When it comes to practical application, a sinusoidal ripple is simply superposed into the charge/discharge source. The AC voltage and current of the battery is used to calculate the AC impedance, and this allows a fairly accurate estimation of the battery SOC. Final validation using a power analyzer and actual measurement showed the average error of SOC estimation to be within 4%. This encouraging result showed the proposed method can provide a simple and practical solution for online estimation of the SOC of Li-ion batteries.
UR - http://www.scopus.com/inward/record.url?scp=85056253730&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85056253730&partnerID=8YFLogxK
U2 - 10.1109/ICICE.2017.8479183
DO - 10.1109/ICICE.2017.8479183
M3 - Conference contribution
AN - SCOPUS:85056253730
T3 - Proceedings of the 2017 IEEE International Conference on Information, Communication and Engineering: Information and Innovation for Modern Technology, ICICE 2017
SP - 53
EP - 56
BT - Proceedings of the 2017 IEEE International Conference on Information, Communication and Engineering
A2 - Lam, Artde Donald Kin-Tak
A2 - Prior, Stephen D.
A2 - Meen, Teen-Hang
PB - Institute of Electrical and Electronics Engineers Inc.
ER -