State-of-Charge Estimation of Lithium-ion Batteries Based on Deep Neural Network

Chao Lyu; Yitong Han; Qi Guo; Lixin Wang; Yankong Song

doi:10.1109/PHM-Shanghai49105.2020.9280940

State-of-Charge Estimation of Lithium-ion Batteries Based on Deep Neural Network

Chao Lyu
, Yitong Han
, Qi Guo
, Lixin Wang
, Yankong Song

School of Electrical Engineering and Automation, Harbin Institute of Technology

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

9 Scopus citations

Abstract

The State of Charge (SOC) of the battery is one of the core functions of the battery management system (BMS). Accurate estimation of SOC is essential for the reliability and safety of battery systems. The model-based SOC estimation method requires the establishment of an accurate battery model and the use of complex adaptive filtering algorithms, which is difficult to implement in engineering. Lithium-ion batteries generate a large amount of data such as voltage, current and temperature during the working process, which combines pure data-driven deep learning algorithms with lithium-ion batteries SOC estimation. This paper is based on the deep neural network to estimate the SOC of lithium-ion batteries, using public data sets and model simulation data sets to compare the effectiveness of Gated Recurrent Unit Neural Network (GRU), Long Short-Term Memory Network (LSTM) and Recurrent Neural Network (RNN) on SOC estimation. It verifies that GRU is superior to LSTM and RNN in terms of network performance and estimation accuracy.

Original language	English
Title of host publication	2020 Global Reliability and Prognostics and Health Management, PHM-Shanghai 2020
Editors	Wei Guo, Steven Li, Qiang Miao
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781728159454
DOIs	https://doi.org/10.1109/PHM-Shanghai49105.2020.9280940
State	Published - 16 Oct 2020
Externally published	Yes
Event	2020 Global Reliability and Prognostics and Health Management, PHM-Shanghai 2020 - Shanghai, China Duration: 16 Oct 2020 → 18 Oct 2020

Publication series

Name	2020 Global Reliability and Prognostics and Health Management, PHM-Shanghai 2020

Conference

Conference	2020 Global Reliability and Prognostics and Health Management, PHM-Shanghai 2020
Country/Territory	China
City	Shanghai
Period	16/10/20 → 18/10/20

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 7 Affordable and Clean Energy

Keywords

Lithium-ion batteries
SOC estimation
deep neural network

Access to Document

10.1109/PHM-Shanghai49105.2020.9280940

Cite this

Lyu, C., Han, Y., Guo, Q., Wang, L., & Song, Y. (2020). State-of-Charge Estimation of Lithium-ion Batteries Based on Deep Neural Network. In W. Guo, S. Li, & Q. Miao (Eds.), 2020 Global Reliability and Prognostics and Health Management, PHM-Shanghai 2020 Article 9280940 (2020 Global Reliability and Prognostics and Health Management, PHM-Shanghai 2020). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/PHM-Shanghai49105.2020.9280940

Lyu, Chao ; Han, Yitong ; Guo, Qi et al. / State-of-Charge Estimation of Lithium-ion Batteries Based on Deep Neural Network. 2020 Global Reliability and Prognostics and Health Management, PHM-Shanghai 2020. editor / Wei Guo ; Steven Li ; Qiang Miao. Institute of Electrical and Electronics Engineers Inc., 2020. (2020 Global Reliability and Prognostics and Health Management, PHM-Shanghai 2020).

@inproceedings{fc144b404088474aa88fa5ac03aa5b2f,

title = "State-of-Charge Estimation of Lithium-ion Batteries Based on Deep Neural Network",

abstract = "The State of Charge (SOC) of the battery is one of the core functions of the battery management system (BMS). Accurate estimation of SOC is essential for the reliability and safety of battery systems. The model-based SOC estimation method requires the establishment of an accurate battery model and the use of complex adaptive filtering algorithms, which is difficult to implement in engineering. Lithium-ion batteries generate a large amount of data such as voltage, current and temperature during the working process, which combines pure data-driven deep learning algorithms with lithium-ion batteries SOC estimation. This paper is based on the deep neural network to estimate the SOC of lithium-ion batteries, using public data sets and model simulation data sets to compare the effectiveness of Gated Recurrent Unit Neural Network (GRU), Long Short-Term Memory Network (LSTM) and Recurrent Neural Network (RNN) on SOC estimation. It verifies that GRU is superior to LSTM and RNN in terms of network performance and estimation accuracy.",

keywords = "Lithium-ion batteries, SOC estimation, deep neural network",

author = "Chao Lyu and Yitong Han and Qi Guo and Lixin Wang and Yankong Song",

note = "Publisher Copyright: {\textcopyright} 2020 IEEE.; 2020 Global Reliability and Prognostics and Health Management, PHM-Shanghai 2020 ; Conference date: 16-10-2020 Through 18-10-2020",

year = "2020",

month = oct,

day = "16",

doi = "10.1109/PHM-Shanghai49105.2020.9280940",

language = "英语",

series = "2020 Global Reliability and Prognostics and Health Management, PHM-Shanghai 2020",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

editor = "Wei Guo and Steven Li and Qiang Miao",

booktitle = "2020 Global Reliability and Prognostics and Health Management, PHM-Shanghai 2020",

address = "美国",

}

Lyu, C, Han, Y, Guo, Q, Wang, L & Song, Y 2020, State-of-Charge Estimation of Lithium-ion Batteries Based on Deep Neural Network. in W Guo, S Li & Q Miao (eds), 2020 Global Reliability and Prognostics and Health Management, PHM-Shanghai 2020., 9280940, 2020 Global Reliability and Prognostics and Health Management, PHM-Shanghai 2020, Institute of Electrical and Electronics Engineers Inc., 2020 Global Reliability and Prognostics and Health Management, PHM-Shanghai 2020, Shanghai, China, 16/10/20. https://doi.org/10.1109/PHM-Shanghai49105.2020.9280940

State-of-Charge Estimation of Lithium-ion Batteries Based on Deep Neural Network. / Lyu, Chao; Han, Yitong; Guo, Qi et al.
2020 Global Reliability and Prognostics and Health Management, PHM-Shanghai 2020. ed. / Wei Guo; Steven Li; Qiang Miao. Institute of Electrical and Electronics Engineers Inc., 2020. 9280940 (2020 Global Reliability and Prognostics and Health Management, PHM-Shanghai 2020).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - State-of-Charge Estimation of Lithium-ion Batteries Based on Deep Neural Network

AU - Lyu, Chao

AU - Han, Yitong

AU - Guo, Qi

AU - Wang, Lixin

AU - Song, Yankong

PY - 2020/10/16

Y1 - 2020/10/16

N2 - The State of Charge (SOC) of the battery is one of the core functions of the battery management system (BMS). Accurate estimation of SOC is essential for the reliability and safety of battery systems. The model-based SOC estimation method requires the establishment of an accurate battery model and the use of complex adaptive filtering algorithms, which is difficult to implement in engineering. Lithium-ion batteries generate a large amount of data such as voltage, current and temperature during the working process, which combines pure data-driven deep learning algorithms with lithium-ion batteries SOC estimation. This paper is based on the deep neural network to estimate the SOC of lithium-ion batteries, using public data sets and model simulation data sets to compare the effectiveness of Gated Recurrent Unit Neural Network (GRU), Long Short-Term Memory Network (LSTM) and Recurrent Neural Network (RNN) on SOC estimation. It verifies that GRU is superior to LSTM and RNN in terms of network performance and estimation accuracy.

AB - The State of Charge (SOC) of the battery is one of the core functions of the battery management system (BMS). Accurate estimation of SOC is essential for the reliability and safety of battery systems. The model-based SOC estimation method requires the establishment of an accurate battery model and the use of complex adaptive filtering algorithms, which is difficult to implement in engineering. Lithium-ion batteries generate a large amount of data such as voltage, current and temperature during the working process, which combines pure data-driven deep learning algorithms with lithium-ion batteries SOC estimation. This paper is based on the deep neural network to estimate the SOC of lithium-ion batteries, using public data sets and model simulation data sets to compare the effectiveness of Gated Recurrent Unit Neural Network (GRU), Long Short-Term Memory Network (LSTM) and Recurrent Neural Network (RNN) on SOC estimation. It verifies that GRU is superior to LSTM and RNN in terms of network performance and estimation accuracy.

KW - Lithium-ion batteries

KW - SOC estimation

KW - deep neural network

UR - https://www.scopus.com/pages/publications/85099702374

U2 - 10.1109/PHM-Shanghai49105.2020.9280940

DO - 10.1109/PHM-Shanghai49105.2020.9280940

M3 - 会议稿件

AN - SCOPUS:85099702374

T3 - 2020 Global Reliability and Prognostics and Health Management, PHM-Shanghai 2020

BT - 2020 Global Reliability and Prognostics and Health Management, PHM-Shanghai 2020

A2 - Guo, Wei

A2 - Li, Steven

A2 - Miao, Qiang

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2020 Global Reliability and Prognostics and Health Management, PHM-Shanghai 2020

Y2 - 16 October 2020 through 18 October 2020

ER -

Lyu C, Han Y, Guo Q, Wang L, Song Y. State-of-Charge Estimation of Lithium-ion Batteries Based on Deep Neural Network. In Guo W, Li S, Miao Q, editors, 2020 Global Reliability and Prognostics and Health Management, PHM-Shanghai 2020. Institute of Electrical and Electronics Engineers Inc. 2020. 9280940. (2020 Global Reliability and Prognostics and Health Management, PHM-Shanghai 2020). doi: 10.1109/PHM-Shanghai49105.2020.9280940

State-of-Charge Estimation of Lithium-ion Batteries Based on Deep Neural Network

Abstract

Publication series

Conference

UN SDGs

Keywords

Access to Document

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