A Novel State of Charge Estimation Method of Batteries Using Recurrent Neural Networks

DOI

10.2991/ncce-18.2018.199How to use a DOI?

Keywords

power battery, SOC, LSTM network.

Abstract

This paper, an improved recurrent neural network, long and short time memory model (LSTM) is used to estimate the SOC estimation of vehicle lithium ion battery, and the SOC estimation model of the battery based on LSTM network is established. Based on the electrochemical reaction of lithium ion batteries and the complex operating conditions of the electric vehicle, a battery model was established, and the experimental verification was carried out. The results showed that the accuracy of the SOC estimation model could meet the requirements of the SOC estimation application.

Copyright

© 2018, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Anyu Cheng
AU  - Yao Wang
PY  - 2018/05
DA  - 2018/05
TI  - A Novel State of Charge Estimation Method of Batteries Using Recurrent Neural Networks
BT  - Proceedings of the 2018 International Conference on Network, Communication, Computer Engineering (NCCE 2018)
PB  - Atlantis Press
SP  - 1175
EP  - 1181
SN  - 1951-6851
UR  - https://doi.org/10.2991/ncce-18.2018.199
DO  - 10.2991/ncce-18.2018.199
ID  - Cheng2018/05
ER  -