Lithium-ion battery capacity estimation based on incremental capacity analysis and deep convolutional neural network

Sibo Zeng, Sheng Chen*, Babakalli Alkali

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

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Abstract

Accurate estimation of Li-ion battery capacity is critical for a battery management system (BMS). This paper proposes an innovative method which combines a convolutional neural network and incremental capacity analysis (ICA). In the present approach, the voltage and temperature, which significantly affect the ICA curve during the discharging process, are adopted as the inputs for CNN. Rather than extracting feature parameters of an IC curve, as is carried out in the available research, the present method uses the whole ICA curve as the input to avoid complicated feature extraction and correlation analysis. The results show that the maximum error of capacity estimation is less than 4.7%, the rectified mean squared error is less than 1.3% for each battery, and the overall RMSE is below 1.12%.
Original languageEnglish
Article number1272
Number of pages14
JournalEnergies
Volume17
Issue number6
Early online date7 Mar 2024
DOIs
Publication statusPublished - Mar 2024

Keywords

  • lithium-ion battery
  • capacity estimation
  • incremental capacity analysis
  • gaussian regression
  • convolutional neural network

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