Battery impedance spectrum prediction from partial charging voltage curve by machine learning

Jia Guo, Yunhong Che*, Kjeld Pedersen, Daniel Ioan Stroe

*Corresponding author for this work

Research output: Contribution to journalJournal articleResearchpeer-review

16 Citations (Scopus)
49 Downloads (Pure)

Abstract

Electrochemical impedance spectroscopy (EIS) is an effective technique for Lithium-ion battery state of health diagnosis, and the impedance spectrum prediction by battery charging curve is expected to enable battery impedance testing during vehicle operation. However, the mechanistic relationship between charging curves and impedance spectrum remains unclear, which hinders the development as well as optimization of EIS-based prediction techniques. In this paper, we predicted the impedance spectrum by the battery charging voltage curve and optimized the input based on electrochemical mechanistic analysis and machine learning. The internal electrochemical relationships between the charging curve, incremental capacity curve, and the impedance spectrum are explored, which improves the physical interpretability for this prediction and helps define the proper partial voltage range for the input for machine learning models. Different machine learning algorithms have been adopted for the verification of the proposed framework based on the sequence-to-sequence predictions. In addition, the predictions with different partial voltage ranges, at different state of charge, and with different training data ratio are evaluated to prove the proposed method have high generalization and robustness. The experimental results show that the proper partial voltage range has high accuracy and converges to the findings of the electrochemical analysis. The predicted errors for impedance spectrum are less than 1.9 mΩ with the proper partial voltage range selected by the corelative analysis of the electrochemical reactions inside the batteries. Even with the voltage range reduced to 3.65–3.75 V, the predictions are still reliable with most RMSEs less than 4 mΩ.

Original languageEnglish
JournalJournal of Energy Chemistry
Volume79
Pages (from-to)211-221
Number of pages11
ISSN2095-4956
DOIs
Publication statusPublished - Apr 2023

Bibliographical note

Funding Information:
Jia Guo is supported by a grant from the China Scholarship Council ( 202006370035 ), and a fund from Otto Monsteds Fund (4057941073). We would also like to thank Haidi Energy Technology Co. for providing electrodes, which were used to manufacture the coin cells.

Funding Information:
Jia Guo is supported by a grant from the China Scholarship Council (202006370035), and a fund from Otto Monsteds Fund (4057941073). We would also like to thank Haidi Energy Technology Co. for providing electrodes, which were used to manufacture the coin cells.

Publisher Copyright:
© 2023 Science Press and Dalian Institute of Chemical Physics, Chinese Academy of Sciences

Keywords

  • EIS
  • Graphite anode
  • Impedance spectrum prediction
  • Lithium-ion battery
  • Machine learning

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