Analysis of SoC Estimation for Master-Slave BMS Configuration

Bibaswan Bose; Vandana; Rahul Soni; A. Garg

doi:10.1109/ICAC3N56670.2022.10074062

Analysis of SoC Estimation for Master-Slave BMS Configuration

Bibaswan Bose^*
, Vandana
, Rahul Soni
, A. Garg

^*Corresponding author for this work

Indian Institute of Technology Delhi
Evi Technologies Private Limited

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

3 Citations (Scopus)

Abstract

The ability to precisely estimate the State of Charge (SoC) of a Lithium-ion battery is critical. BMS plays an important role in ensuring a safe and dependable operation. Due to inefficiency or a tempered algorithm, an inefficient BMS may display the incorrect SoC. As a result, a MasterSlave BMS configuration architecture has been proposed for identifying and correcting SoC estimation error. Following a comparative study on SoC estimation, a hybrid model was chosen for accurate SoC estimation in Master BMS. Six estimation methods were used in conjunction with a dynamic vehicle model. For further improvement, the data-driven methods (Neural Network and Nonlinear Auto-Regression Moving Average -2) are combined.

Original language	English
Title of host publication	Proceedings - 2022 4th International Conference on Advances in Computing, Communication Control and Networking, ICAC3N 2022
Editors	Vishnu Sharma, Vishnu Sharma, Manjeet Singh, Manjeet Singh, Jaya Sinha
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	1317-1322
Number of pages	6
ISBN (Electronic)	9781665474368
DOIs	https://doi.org/10.1109/ICAC3N56670.2022.10074062
Publication status	Published - 2022
Externally published	Yes
Event	4th International Conference on Advances in Computing, Communication Control and Networking, ICAC3N 2022 - Greater Noida, India Duration: 16 Dec 2022 → 17 Dec 2022

Publication series

Name	Proceedings - 2022 4th International Conference on Advances in Computing, Communication Control and Networking, ICAC3N 2022

Conference

Conference	4th International Conference on Advances in Computing, Communication Control and Networking, ICAC3N 2022
Country/Territory	India
City	Greater Noida
Period	16/12/22 → 17/12/22

UN SDGs

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

SDG 7 Affordable and Clean Energy

Keywords

Extended Kalman Filter (EKF)
Neural Network
Non-linear Auto-Regressive Moving Average (NARMA L-2)
Open Circuit Voltage (OCV)
State of Charge (SoC)
State of Health (SoH)
Unscented Kalman Filter (UKF)

Access to Document

10.1109/ICAC3N56670.2022.10074062

Cite this

Bose, B., Vandana, Soni, R., & Garg, A. (2022). Analysis of SoC Estimation for Master-Slave BMS Configuration. In V. Sharma, V. Sharma, M. Singh, M. Singh, & J. Sinha (Eds.), Proceedings - 2022 4th International Conference on Advances in Computing, Communication Control and Networking, ICAC3N 2022 (pp. 1317-1322). (Proceedings - 2022 4th International Conference on Advances in Computing, Communication Control and Networking, ICAC3N 2022). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICAC3N56670.2022.10074062

Bose, Bibaswan ; Vandana ; Soni, Rahul et al. / Analysis of SoC Estimation for Master-Slave BMS Configuration. Proceedings - 2022 4th International Conference on Advances in Computing, Communication Control and Networking, ICAC3N 2022. editor / Vishnu Sharma ; Vishnu Sharma ; Manjeet Singh ; Manjeet Singh ; Jaya Sinha. Institute of Electrical and Electronics Engineers Inc., 2022. pp. 1317-1322 (Proceedings - 2022 4th International Conference on Advances in Computing, Communication Control and Networking, ICAC3N 2022).

@inproceedings{4ea5a8bc54074cd7b22fcc29945903b8,

title = "Analysis of SoC Estimation for Master-Slave BMS Configuration",

abstract = "The ability to precisely estimate the State of Charge (SoC) of a Lithium-ion battery is critical. BMS plays an important role in ensuring a safe and dependable operation. Due to inefficiency or a tempered algorithm, an inefficient BMS may display the incorrect SoC. As a result, a MasterSlave BMS configuration architecture has been proposed for identifying and correcting SoC estimation error. Following a comparative study on SoC estimation, a hybrid model was chosen for accurate SoC estimation in Master BMS. Six estimation methods were used in conjunction with a dynamic vehicle model. For further improvement, the data-driven methods (Neural Network and Nonlinear Auto-Regression Moving Average -2) are combined.",

keywords = "Extended Kalman Filter (EKF), Neural Network, Non-linear Auto-Regressive Moving Average (NARMA L-2), Open Circuit Voltage (OCV), State of Charge (SoC), State of Health (SoH), Unscented Kalman Filter (UKF)",

author = "Bibaswan Bose and Vandana and Rahul Soni and A. Garg",

note = "Publisher Copyright: {\textcopyright} 2022 IEEE.; 4th International Conference on Advances in Computing, Communication Control and Networking, ICAC3N 2022 ; Conference date: 16-12-2022 Through 17-12-2022",

year = "2022",

doi = "10.1109/ICAC3N56670.2022.10074062",

language = "English",

series = "Proceedings - 2022 4th International Conference on Advances in Computing, Communication Control and Networking, ICAC3N 2022",

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

pages = "1317--1322",

editor = "Vishnu Sharma and Vishnu Sharma and Manjeet Singh and Manjeet Singh and Jaya Sinha",

booktitle = "Proceedings - 2022 4th International Conference on Advances in Computing, Communication Control and Networking, ICAC3N 2022",

}

Bose, B, Vandana, Soni, R & Garg, A 2022, Analysis of SoC Estimation for Master-Slave BMS Configuration. in V Sharma, V Sharma, M Singh, M Singh & J Sinha (eds), Proceedings - 2022 4th International Conference on Advances in Computing, Communication Control and Networking, ICAC3N 2022. Proceedings - 2022 4th International Conference on Advances in Computing, Communication Control and Networking, ICAC3N 2022, Institute of Electrical and Electronics Engineers Inc., pp. 1317-1322, 4th International Conference on Advances in Computing, Communication Control and Networking, ICAC3N 2022, Greater Noida, India, 16/12/22. https://doi.org/10.1109/ICAC3N56670.2022.10074062

Analysis of SoC Estimation for Master-Slave BMS Configuration. / Bose, Bibaswan; Vandana; Soni, Rahul et al.
Proceedings - 2022 4th International Conference on Advances in Computing, Communication Control and Networking, ICAC3N 2022. ed. / Vishnu Sharma; Vishnu Sharma; Manjeet Singh; Manjeet Singh; Jaya Sinha. Institute of Electrical and Electronics Engineers Inc., 2022. p. 1317-1322 (Proceedings - 2022 4th International Conference on Advances in Computing, Communication Control and Networking, ICAC3N 2022).

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

TY - GEN

T1 - Analysis of SoC Estimation for Master-Slave BMS Configuration

AU - Bose, Bibaswan

AU - Vandana,

AU - Soni, Rahul

AU - Garg, A.

PY - 2022

Y1 - 2022

N2 - The ability to precisely estimate the State of Charge (SoC) of a Lithium-ion battery is critical. BMS plays an important role in ensuring a safe and dependable operation. Due to inefficiency or a tempered algorithm, an inefficient BMS may display the incorrect SoC. As a result, a MasterSlave BMS configuration architecture has been proposed for identifying and correcting SoC estimation error. Following a comparative study on SoC estimation, a hybrid model was chosen for accurate SoC estimation in Master BMS. Six estimation methods were used in conjunction with a dynamic vehicle model. For further improvement, the data-driven methods (Neural Network and Nonlinear Auto-Regression Moving Average -2) are combined.

AB - The ability to precisely estimate the State of Charge (SoC) of a Lithium-ion battery is critical. BMS plays an important role in ensuring a safe and dependable operation. Due to inefficiency or a tempered algorithm, an inefficient BMS may display the incorrect SoC. As a result, a MasterSlave BMS configuration architecture has been proposed for identifying and correcting SoC estimation error. Following a comparative study on SoC estimation, a hybrid model was chosen for accurate SoC estimation in Master BMS. Six estimation methods were used in conjunction with a dynamic vehicle model. For further improvement, the data-driven methods (Neural Network and Nonlinear Auto-Regression Moving Average -2) are combined.

KW - Extended Kalman Filter (EKF)

KW - Neural Network

KW - Non-linear Auto-Regressive Moving Average (NARMA L-2)

KW - Open Circuit Voltage (OCV)

KW - State of Charge (SoC)

KW - State of Health (SoH)

KW - Unscented Kalman Filter (UKF)

UR - https://www.scopus.com/pages/publications/85152195626

U2 - 10.1109/ICAC3N56670.2022.10074062

DO - 10.1109/ICAC3N56670.2022.10074062

M3 - Conference Proceeding

AN - SCOPUS:85152195626

T3 - Proceedings - 2022 4th International Conference on Advances in Computing, Communication Control and Networking, ICAC3N 2022

SP - 1317

EP - 1322

BT - Proceedings - 2022 4th International Conference on Advances in Computing, Communication Control and Networking, ICAC3N 2022

A2 - Sharma, Vishnu

A2 - Singh, Manjeet

A2 - Sinha, Jaya

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 4th International Conference on Advances in Computing, Communication Control and Networking, ICAC3N 2022

Y2 - 16 December 2022 through 17 December 2022

ER -

Bose B, Vandana, Soni R, Garg A. Analysis of SoC Estimation for Master-Slave BMS Configuration. In Sharma V, Sharma V, Singh M, Singh M, Sinha J, editors, Proceedings - 2022 4th International Conference on Advances in Computing, Communication Control and Networking, ICAC3N 2022. Institute of Electrical and Electronics Engineers Inc. 2022. p. 1317-1322. (Proceedings - 2022 4th International Conference on Advances in Computing, Communication Control and Networking, ICAC3N 2022). doi: 10.1109/ICAC3N56670.2022.10074062

Analysis of SoC Estimation for Master-Slave BMS Configuration

Abstract

Publication series

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UN SDGs

Keywords

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