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 language | English |
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Article number | 106821 |
Number of pages | 11 |
Journal | iScience |
Volume | 26 |
Issue number | 6 |
Early online date | 30 May 2023 |
DOIs | |
Publication status | Published - 16 Jun 2023 |
Externally published | Yes |
Bibliographical note
Publisher Copyright:© 2023
Funding
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. 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 ).
Keywords
- Computational materials science
- Electrochemical energy storage
- Machine learning