Design of Neural Network-based Boost Charging for Reducing the Charging Time of Li-ion Battery

Sue Hyang Lim, Seon Hyeog Kim, Hyeong Min Lee, Si Joong Kim, Yong June Shin

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Rapid charging of Li-ion batteries is vital for the commercialization of electric propulsion systems. But, during the fast-charging process, reduction in the battery capacity and temperature increases must be considered in real-time. Most Li-ion battery chargers follow the charging profile of an open-loop system, which has been determined based on prior knowledge. However, such a system does not reflect the temperature change of the battery and the degree of aging. Therefore, in this study, we propose a neural network-based charging profile model by applying a closed-loop system to reflect the various states of batteries; we also show two battery-state characteristics in addition to temperature. Consequently, we show battery characteristics other than those shown in the past, such as the battery voltage and temperature trends. In addition to the design of the charging current, an improvement of approximately 22 ∼ 50% based on the mean absolute error (MAE) is achieved. By considering the various characteristics, the long short-term memory performance is determined to be better when compared to the feed-forward neural network, and this performance is improved by 35% based on MAE.

Original languageEnglish
Title of host publicationProceedings - 20th IEEE International Conference on Data Mining Workshops, ICDMW 2020
EditorsGiuseppe Di Fatta, Victor Sheng, Alfredo Cuzzocrea, Carlo Zaniolo, Xindong Wu
PublisherIEEE Computer Society
Pages750-756
Number of pages7
ISBN (Electronic)9781728190129
DOIs
Publication statusPublished - 2020 Nov
Event20th IEEE International Conference on Data Mining Workshops, ICDMW 2020 - Virtual, Sorrento, Italy
Duration: 2020 Nov 172020 Nov 20

Publication series

NameIEEE International Conference on Data Mining Workshops, ICDMW
Volume2020-November
ISSN (Print)2375-9232
ISSN (Electronic)2375-9259

Conference

Conference20th IEEE International Conference on Data Mining Workshops, ICDMW 2020
CountryItaly
CityVirtual, Sorrento
Period20/11/1720/11/20

Bibliographical note

Funding Information:
This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Ministry of Science, the framework of international cooperation program managed by NRF of Korea #NRF-2017K1A4A301357. This research was also supported by Korea Electric Power Corporation (KEPCO) #R18XA05.

Publisher Copyright:
© 2020 IEEE.

All Science Journal Classification (ASJC) codes

  • Computer Science Applications
  • Software

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