Estimation of SOC based on LSTM-RNN and design of intelligent equalization charging system

Xi Chen*, Kaoru Hirota, Yaping Dai, Zhiyang Jia

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

4 Citations (Scopus)

Abstract

Lithium battery packs are the main driving energy source for electric vehicles. A battery pack equalization charging solution using a constant current source for variable rate charging is presented in this paper. The charging system consists of a main constant current source and independent auxiliary constant current sources. Auxiliary constant current sources are controlled by the battery management system (BMS), which can change the current rate of the corresponding single battery, and achieve full charging of each single cell in the series battery pack. At the same time, the state of charge (SOC) is regarded as time series data to establish a long short-term memory recurrent neural network (LSTM-RNN) model, and it is possible to obtain the single battery with lower capacity, so that the charging efficiency and battery pack consistency can be improved. The experimental results show that the open circuit voltage difference between the single cells is less than 50 mV after the charging of 20 strings of lithium battery packs by using this method, which achieve the purpose of equalization charging.

Original languageEnglish
Pages (from-to)855-863
Number of pages9
JournalJournal of Advanced Computational Intelligence and Intelligent Informatics
Volume24
Issue number7
DOIs
Publication statusPublished - 20 Dec 2020

Keywords

  • Auxiliary constant current source
  • Intelligent equalization charging
  • LSTM-RNN

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Chen, X., Hirota, K., Dai, Y., & Jia, Z. (2020). Estimation of SOC based on LSTM-RNN and design of intelligent equalization charging system. Journal of Advanced Computational Intelligence and Intelligent Informatics, 24(7), 855-863. https://doi.org/10.20965/JACIII.2020.P0855