Predicting battery impedance spectra from 10-second pulse tests under 10 Hz sampling rate

Xiaopeng TANG, Xin LAI, Qi LIU*, Yuejiu ZHENG, Yuanqiang ZHOU, Yunjie MA, Furong GAO*

*Corresponding author for this work

Research output: Journal PublicationsJournal Article (refereed)peer-review

9 Citations (Scopus)

Abstract

Onboard measuring the electrochemical impedance spectroscopy (EIS) for lithium-ion batteries is a long-standing issue that limits the technologies such as portable electronics and electric vehicles. Challenges arise from not only the high sampling rate required by the Shannon Sampling Theorem but also the sophisticated real-life battery-using profiles. We here propose a fast and accurate EIS predicting system by combining the fractional-order electric circuit model—a highly nonlinear model with clear physical meanings—with a median-filtered neural network machine learning. Over 1000 load profiles collected under different state-of-charge and state-of-health are utilized for verification, and the root-mean-squared-error of our predictions could be bounded by 1.1 mΩ and 2.1 mΩ when using dynamic profiles last for 3 min and 10 s, respectively. Our method allows using size-varying input data sampled at a rate down to 10 Hz and unlocks opportunities to detect the battery's internal electrochemical characteristics onboard via low-cost embedded sensors.

Original languageEnglish
Article number106821
Number of pages11
JournaliScience
Volume26
Issue number6
Early online date30 May 2023
DOIs
Publication statusPublished - 16 Jun 2023
Externally publishedYes

Bibliographical note

Funding Information:
The first author would like to thank the continuing support from the Guangzhou HKUST Fok Ying Tung Research Institute during Hong Kong’s unrest and the outbreak of the Covid-19.

Funding Information:
This work is supported, in part, by the Hong Kong RGC Postdoctoral Fellowship Scheme (PDFS2122-6S06), Hong Kong Research Grant Council under grant 16208520 and 11220322 , the National Natural Science Foundation of China (51977131, 52277223), the Natural Science Foundation of Shanghai (19ZR1435800), Guangdong Scientific and Technological Project (2019A050516002), and the Foshan-HKUST Projects Program (FSUST20-FYTRI12F).

Publisher Copyright:
© 2023

Keywords

  • Computational materials science
  • Electrochemical energy storage
  • Machine learning

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