Improved covariance matching—electrical equivalent modeling for accurate internal state characterization of packing lithium-ion batteries

Shun-Li Wang*, Yongcun Fan, Chunmei Yu, Siyu Jin, Carlos Fernandez, Daniel-Ioan Stroe

*Corresponding author for this work

Research output: Contribution to journalJournal articleResearchpeer-review

5 Citations (Scopus)

Abstract

As for the cell-to-cell inconsistency of packing lithium-ion batteries, accurate equivalent modeling plays a significant role in the working characteristic monitoring and improving the safety protection quality under complex working conditions. In this work, a novel covariance matching - electrical equivalent circuit modeling method is proposed to realize the adaptive working state characterization by considering the internal reaction features, and an improved adaptive weighting factor correction - differential Kalman filtering model is constructed for the iterative calculation process. A new parameter named state-of-balance is introduced to describe the cell-to-cell variation mathematically by forming an effective influence correction strategy. An adaptive covariance matching method is investigated to update and transmit the noise matrix for high-power energy supply conditions, in which the weighting factor correction is conducted by considering the coupling relationship to improve the prediction accuracy. Experimental tests are conducted to verify the estimation effect, in which the closed-circuit voltage responds well corresponding to the battery state variation. The maximum closed-circuit voltage traction error is 1.80% and the maximum SOC estimation error for packing lithium-ion batteries is 1.114% for the long-term experimental tests with the MAE value of 0.00481 and RMSE value of 5.44085E-5. The improved covariance matching - electrical equivalent circuit modeling method provides a theoretical foundation for the reliable application of lithium-ion batteries.
Original languageEnglish
JournalInternational Journal of Energy Research
Volume46
Issue number3
Pages (from-to)3602-3620
Number of pages19
ISSN0363-907X
DOIs
Publication statusPublished - 10 Mar 2022

Bibliographical note

Funding Information:
The work was supported by the National Natural Science Foundation of China (No. 62173281, 61801407), Sichuan science and technology program (No. 2019YFG0427), China Scholarship Council (No. 201908515099), and Fund of Robot Technology Used for Special Environment Key Laboratory of Sichuan Province (No. 18kftk03).

Funding Information:
China Scholarship Council, Grant/Award Number: 201908515099; Fund of Robot Technology Used for Special Environment Key Laboratory of Sichuan Province, Grant/Award Number: 18kftk03; National Natural Science Foundation of China, Grant/Award Numbers: 62173281, 61801407; Sichuan science and technology program, Grant/Award Number: 2019YFG0427 Funding information

Publisher Copyright:
© 2021 John Wiley & Sons Ltd.

Keywords

  • adaptive covariance matching
  • cell-to-cell variation
  • electrical equivalent circuit modeling
  • packing lithium-ion batteries
  • state of balance
  • weighting factor correction

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