JACIII Vol.24 No.7 pp. 855-863
doi: 10.20965/jaciii.2020.p0855


Estimation of SOC Based on LSTM-RNN and Design of Intelligent Equalization Charging System

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

*School of Automation, Beijing Institute of Technology
No.5 Zhongguancun South Street, Haidian District, Beijing 100081, China

**College of Physics and Energy, Fujian Normal University
No.8 Xuefu South Road, Shangjie, Minhou, Fuzhou, Fujian 350117, China

Corresponding author

October 17, 2020
October 27, 2020
December 20, 2020
intelligent equalization charging, auxiliary constant current source, LSTM-RNN

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.

Cite this article as:
X. Chen, K. Hirota, Y. Dai, and Z. Jia, “Estimation of SOC Based on LSTM-RNN and Design of Intelligent Equalization Charging System,” J. Adv. Comput. Intell. Intell. Inform., Vol.24 No.7, pp. 855-863, 2020.
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Last updated on Apr. 22, 2024