On state-of-charge determination for lithium-ion batteries

Li, Zhe; Huang, Jun; Liaw, Bor Yann; Zhang, Jianbo

doi:10.1016/j.jpowsour.2017.03.001

Title: On state-of-charge determination for lithium-ion batteries

Journal Article · Thu Mar 09 00:00:00 EST 2017 · Journal of Power Sources

DOI:https://doi.org/10.1016/j.jpowsour.2017.03.001· OSTI ID:1470981

Li, Zhe ^[1];

^[1];

^[2]; Zhang, Jianbo ^[3]

Tsinghua Univ., Beijing (China). State Key Lab. of Automotive Safety and Energy, Dept. of Automotive Engineering
Idaho National Lab. (INL), Idaho Falls, ID (United States). Energy Storage and Advanced Vehicles
Tsinghua Univ., Beijing (China). State Key Lab. of Automotive Safety and Energy, Dept. of Automotive Engineering; Beijing Inst. of Technology, Beijing (China). Beijing Co-innovation Center for Electric Vehicles

The estimation of state-of-charge (SOC) of a battery is a challenging topic in battery research. Although improved precisions have been reported at times, almost all are based on empirical approaches. Without sufficient fundamental understanding, the accuracy and precision of any SOC estimation remain questionable and to assess if a SOC estimation method were properly constructed remains difficult. Here, we review a range of topics including SOC definitions, methodologies of SOC estimation, calibration, regression (including modeling methods), and validation in terms of precision and accuracy. At the end, we intend to answer fundamental questions, such as: 1) can SOC estimation be self-adaptive without bias? 2) Why Ah-counting is a necessity in most battery-model-assisted regression methods? 3) How to determine the principle of multi-physical coupling in battery models? 4) To assess the accuracy in the SOC estimation, statistical methods should be employed to analyze factors that contribute to the uncertainty. By answering these questions, we shall reveal how fundamental principles work to aid the understanding of proper SOC estimation.

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Research Organization:: Idaho National Laboratory (INL), Idaho Falls, ID (United States)

Sponsoring Organization:: USDOE Office of Nuclear Energy (NE)

Grant/Contract Number:: AC07-05ID14517

OSTI ID:: 1470981

Alternate ID(s):: OSTI ID: 1416199; OSTI ID: 1484715

Report Number(s):: INL/JOU-17-40852-Rev001; INL/JOU-17-40852-Rev000

Journal Information:: Journal of Power Sources, Vol. 348, Issue C; ISSN 0378-7753

Publisher:: ElsevierCopyright Statement

Country of Publication:: United States

Language:: English

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Cited by: 176 works

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In Situ Measurement of Radial Temperature Distributions in Cylindrical Li-Ion Cells Zhang, Guangsheng; Cao, Lei; Ge, Shanhai Journal of The Electrochemical Society, Vol. 161, Issue 10 https://doi.org/10.1149/2.0051410jes	journal	January 2014
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Origins and accommodation of cell variations in Li-ion battery pack modeling Dubarry, Matthieu; Vuillaume, Nicolas; Liaw, Bor Yann International Journal of Energy Research, Vol. 34, Issue 2 https://doi.org/10.1002/er.1668	journal	December 2009
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A Comparative Study of Three Improved Algorithms Based on Particle Filter Algorithms in SOC Estimation of Lithium Ion Batteries Xia, Bizhong; Sun, Zhen; Zhang, Ruifeng Energies, Vol. 10, Issue 8 https://doi.org/10.3390/en10081149	journal	August 2017
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Electrochemical noise of a Li-ion battery: measurement and spectral analysis Astafev, E. A. Journal of Solid State Electrochemistry, Vol. 23, Issue 4 https://doi.org/10.1007/s10008-019-04209-5	journal	February 2019
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A Novel Intelligent Method for the State of Charge Estimation of Lithium-Ion Batteries Using a Discrete Wavelet Transform-Based Wavelet Neural Network Cui, Deyu; Xia, Bizhong; Zhang, Ruifeng Energies, Vol. 11, Issue 4 https://doi.org/10.3390/en11040995	journal	April 2018
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