CLC number: TM912.1
On-line Access: 2024-08-27
Received: 2023-10-17
Revision Accepted: 2024-05-08
Crosschecked: 2017-03-09
Cited: 0
Clicked: 5681
Xi-ming Cheng, Li-guang Yao, Michael Pecht. Lithium-ion battery state-of-charge estimation based on deconstructed equivalent circuit at different open-circuit voltage relaxation times[J]. Journal of Zhejiang University Science A, 2017, 18(4): 256-267.
@article{title="Lithium-ion battery state-of-charge estimation based on deconstructed equivalent circuit at different open-circuit voltage relaxation times",
author="Xi-ming Cheng, Li-guang Yao, Michael Pecht",
journal="Journal of Zhejiang University Science A",
volume="18",
number="4",
pages="256-267",
year="2017",
publisher="Zhejiang University Press & Springer",
doi="10.1631/jzus.A1600251"
}
%0 Journal Article
%T Lithium-ion battery state-of-charge estimation based on deconstructed equivalent circuit at different open-circuit voltage relaxation times
%A Xi-ming Cheng
%A Li-guang Yao
%A Michael Pecht
%J Journal of Zhejiang University SCIENCE A
%V 18
%N 4
%P 256-267
%@ 1673-565X
%D 2017
%I Zhejiang University Press & Springer
%DOI 10.1631/jzus.A1600251
TY - JOUR
T1 - Lithium-ion battery state-of-charge estimation based on deconstructed equivalent circuit at different open-circuit voltage relaxation times
A1 - Xi-ming Cheng
A1 - Li-guang Yao
A1 - Michael Pecht
J0 - Journal of Zhejiang University Science A
VL - 18
IS - 4
SP - 256
EP - 267
%@ 1673-565X
Y1 - 2017
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/jzus.A1600251
Abstract: Equivalent circuit model-based state-of-charge (SOC) estimation has been widely studied for power lithium-ion batteries. An appropriate relaxation period to measure the open-circuit voltage (OCV) should be investigated to both ensure good SOC estimation accuracy and improve OCV test efficiency. Based on a battery circuit model, an SOC estimator in the combination of recursive least squares (RLS) and the extended Kalman filter is used to mitigate the error voltage between the measurement and real values of the battery OCV. To reduce the iterative computation complexity, a two-stage RLS approach is developed to identify the model parameters, the battery circuit of which is divided into two simple circuits. Then, the measurement values of the OCV at varying relaxation periods and three temperatures are sampled to establish the relationships between SOC and OCV for the developed SOC estimator. Lastly, dynamic stress test and federal test procedure drive cycles are used to validate the model-based SOC estimation method. Results show that the relationships between SOC and OCV at a short relaxation time, such as 5 min, can also drive the SOC estimator to produce a good performance.
This paper shows original work towards development of a two stage process to estimate the EC model parameters and determine battery SOC. The circuit deconstruction method was validated with DST and FTP test cycles. The uniqueness of the work lies in the reduction in error in estimation of SOC and also reducing in the OCV test time. The authors have followed a reasonable scientific approach to explain the model development, theory and results.
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