Capacity and state‐of‐charge (SOC) estimation for lithium‐ion cells based on charging time differences curves

Yuejiu Zheng, Kang Pang, Chaojie Xu, Teng Su, Bhagat Rohit, Yue Guo, Xuebing Han

Research output: Contribution to journalArticlepeer-review

Abstract

The battery pack of electric vehicles (EV) is generally composed of multiple cells in series. Due to the inconsistency between the cells in the production process and use stage, the capacity and state-of-charge (SOC) of the cells will be different. We propose an online estimation method based on the charging curve similarity principle in this paper. The proposed method uses a series of charging time differences (CTD) during the charge. By analyzing the CTD curve, the capacity and SOC difference can be achieved. The first-order resistance circuit model is used for the series charging curve simulation. Further experimental verification is conducted using two groups of four cells in series. In simulations and experiments, the error of the proposed capacity estimation method and the initial SOC error are less than 1%. Finally, the robustness of the proposed method is verified using EV cloud data. The results demonstrate that the proposed method has good robustness at the level of EV cloud data.
Original languageEnglish
Pages (from-to)18757-18767
Number of pages11
JournalInternational Journal of Energy Research
Volume46
Issue number13
Early online date10 Aug 2022
DOIs
Publication statusPublished - 25 Oct 2022

Bibliographical note

Funding Information:
National Natural Science Foundation of China, Grant/Award Number: 51877138; Shanghai Science and Technology Development Foundation, Grant/Award Number: 19QA1406200 Funding information

Funding Information:
This research is supported by National Natural Science Foundation of China (NSFC) under the Grant number of 51877138 and Shanghai Science and Technology Development Foundation 19QA1406200.

Publisher Copyright:
© 2022 John Wiley & Sons Ltd.

Funder

National Natural Science Foundation of China, Grant/Award Number: 51877138; Shanghai Science and Technology Development Foundation, Grant/Award Number: 19QA1406200

Keywords

  • Energy Engineering and Power Technology
  • Fuel Technology
  • Nuclear Energy and Engineering
  • Renewable Energy, Sustainability and the Environment

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