Data Analytics for Core Temperature Estimation in Battery Management System for Electric and Hybrid Vehicles

Ahilya Chhetri; Sumukh Surya; S. Mohan Krishna; Vidya Rao

doi:10.1109/ICEPE57949.2023.10201536

Data Analytics for Core Temperature Estimation in Battery Management System for Electric and Hybrid Vehicles

Ahilya Chhetri
, Sumukh Surya
, S. Mohan Krishna
, Vidya Rao^*

^*Corresponding author for this work

Manipal Institute of Technology, Manipal

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

1 Citation (Scopus)

Abstract

Compared to conventional means of transport thatuse fossil fuels, electric vehicles are known to reduce pollution levels as they take power from renewable sources of energy stored in energy storage devices like batteries and fuel cells. Core temperature estimation of batteriesis extremely important inbattery management systems for preventing thermal runaway and ensuring safe operation. In this study, core temperature is estimated using a Kalman filter for two different battery chemistries viz; lithium polymer and lithium iron phosphate using a second-order thermal model. Further, a linear regression model is applied to verify the prediction over the trained and tested dataset. The lithium iron phosphate prediction curvehad a fit of approximately 82-83%, whilelithium polymerhad a fit of 72-82% during the charging and discharging of OCV-SOC variation.

Original language	English
Title of host publication	5th International Conference on Energy, Power, and Environment
Subtitle of host publication	Towards Flexible Green Energy Technologies, ICEPE 2023
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9798350313123
DOIs	https://doi.org/10.1109/ICEPE57949.2023.10201536
Publication status	Published - 2023
Event	5th International Conference on Energy, Power, and Environment, ICEPE 2023 - Shillong, India Duration: 15-06-2023 → 17-06-2023

Publication series

Name	5th International Conference on Energy, Power, and Environment: Towards Flexible Green Energy Technologies, ICEPE 2023

Conference

Conference	5th International Conference on Energy, Power, and Environment, ICEPE 2023
Country/Territory	India
City	Shillong
Period	15-06-23 → 17-06-23

UN SDGs

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

SDG 7 Affordable and Clean Energy

All Science Journal Classification (ASJC) codes

Energy Engineering and Power Technology
Renewable Energy, Sustainability and the Environment
Electrical and Electronic Engineering
Safety, Risk, Reliability and Quality

Access to Document

10.1109/ICEPE57949.2023.10201536

Cite this

Chhetri, A., Surya, S., Mohan Krishna, S., & Rao, V. (2023). Data Analytics for Core Temperature Estimation in Battery Management System for Electric and Hybrid Vehicles. In 5th International Conference on Energy, Power, and Environment: Towards Flexible Green Energy Technologies, ICEPE 2023 (5th International Conference on Energy, Power, and Environment: Towards Flexible Green Energy Technologies, ICEPE 2023). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICEPE57949.2023.10201536

Chhetri, Ahilya ; Surya, Sumukh ; Mohan Krishna, S. et al. / Data Analytics for Core Temperature Estimation in Battery Management System for Electric and Hybrid Vehicles. 5th International Conference on Energy, Power, and Environment: Towards Flexible Green Energy Technologies, ICEPE 2023. Institute of Electrical and Electronics Engineers Inc., 2023. (5th International Conference on Energy, Power, and Environment: Towards Flexible Green Energy Technologies, ICEPE 2023).

@inproceedings{5db994a899dc4db4996cf372bab3eb69,

title = "Data Analytics for Core Temperature Estimation in Battery Management System for Electric and Hybrid Vehicles",

abstract = "Compared to conventional means of transport thatuse fossil fuels, electric vehicles are known to reduce pollution levels as they take power from renewable sources of energy stored in energy storage devices like batteries and fuel cells. Core temperature estimation of batteriesis extremely important inbattery management systems for preventing thermal runaway and ensuring safe operation. In this study, core temperature is estimated using a Kalman filter for two different battery chemistries viz; lithium polymer and lithium iron phosphate using a second-order thermal model. Further, a linear regression model is applied to verify the prediction over the trained and tested dataset. The lithium iron phosphate prediction curvehad a fit of approximately 82-83\%, whilelithium polymerhad a fit of 72-82\% during the charging and discharging of OCV-SOC variation.",

author = "Ahilya Chhetri and Sumukh Surya and \{Mohan Krishna\}, S. and Vidya Rao",

note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 5th International Conference on Energy, Power, and Environment, ICEPE 2023 ; Conference date: 15-06-2023 Through 17-06-2023",

year = "2023",

doi = "10.1109/ICEPE57949.2023.10201536",

language = "English",

series = "5th International Conference on Energy, Power, and Environment: Towards Flexible Green Energy Technologies, ICEPE 2023",

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

booktitle = "5th International Conference on Energy, Power, and Environment",

address = "United States",

}

Chhetri, A, Surya, S, Mohan Krishna, S & Rao, V 2023, Data Analytics for Core Temperature Estimation in Battery Management System for Electric and Hybrid Vehicles. in 5th International Conference on Energy, Power, and Environment: Towards Flexible Green Energy Technologies, ICEPE 2023. 5th International Conference on Energy, Power, and Environment: Towards Flexible Green Energy Technologies, ICEPE 2023, Institute of Electrical and Electronics Engineers Inc., 5th International Conference on Energy, Power, and Environment, ICEPE 2023, Shillong, India, 15-06-23. https://doi.org/10.1109/ICEPE57949.2023.10201536

Data Analytics for Core Temperature Estimation in Battery Management System for Electric and Hybrid Vehicles. / Chhetri, Ahilya; Surya, Sumukh; Mohan Krishna, S. et al.
5th International Conference on Energy, Power, and Environment: Towards Flexible Green Energy Technologies, ICEPE 2023. Institute of Electrical and Electronics Engineers Inc., 2023. (5th International Conference on Energy, Power, and Environment: Towards Flexible Green Energy Technologies, ICEPE 2023).

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

TY - GEN

T1 - Data Analytics for Core Temperature Estimation in Battery Management System for Electric and Hybrid Vehicles

AU - Chhetri, Ahilya

AU - Surya, Sumukh

AU - Mohan Krishna, S.

AU - Rao, Vidya

PY - 2023

Y1 - 2023

N2 - Compared to conventional means of transport thatuse fossil fuels, electric vehicles are known to reduce pollution levels as they take power from renewable sources of energy stored in energy storage devices like batteries and fuel cells. Core temperature estimation of batteriesis extremely important inbattery management systems for preventing thermal runaway and ensuring safe operation. In this study, core temperature is estimated using a Kalman filter for two different battery chemistries viz; lithium polymer and lithium iron phosphate using a second-order thermal model. Further, a linear regression model is applied to verify the prediction over the trained and tested dataset. The lithium iron phosphate prediction curvehad a fit of approximately 82-83%, whilelithium polymerhad a fit of 72-82% during the charging and discharging of OCV-SOC variation.

AB - Compared to conventional means of transport thatuse fossil fuels, electric vehicles are known to reduce pollution levels as they take power from renewable sources of energy stored in energy storage devices like batteries and fuel cells. Core temperature estimation of batteriesis extremely important inbattery management systems for preventing thermal runaway and ensuring safe operation. In this study, core temperature is estimated using a Kalman filter for two different battery chemistries viz; lithium polymer and lithium iron phosphate using a second-order thermal model. Further, a linear regression model is applied to verify the prediction over the trained and tested dataset. The lithium iron phosphate prediction curvehad a fit of approximately 82-83%, whilelithium polymerhad a fit of 72-82% during the charging and discharging of OCV-SOC variation.

UR - https://www.scopus.com/pages/publications/85168674276

UR - https://www.scopus.com/pages/publications/85168674276#tab=citedBy

U2 - 10.1109/ICEPE57949.2023.10201536

DO - 10.1109/ICEPE57949.2023.10201536

M3 - Conference contribution

AN - SCOPUS:85168674276

T3 - 5th International Conference on Energy, Power, and Environment: Towards Flexible Green Energy Technologies, ICEPE 2023

BT - 5th International Conference on Energy, Power, and Environment

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 5th International Conference on Energy, Power, and Environment, ICEPE 2023

Y2 - 15 June 2023 through 17 June 2023

ER -

Chhetri A, Surya S, Mohan Krishna S, Rao V. Data Analytics for Core Temperature Estimation in Battery Management System for Electric and Hybrid Vehicles. In 5th International Conference on Energy, Power, and Environment: Towards Flexible Green Energy Technologies, ICEPE 2023. Institute of Electrical and Electronics Engineers Inc. 2023. (5th International Conference on Energy, Power, and Environment: Towards Flexible Green Energy Technologies, ICEPE 2023). doi: 10.1109/ICEPE57949.2023.10201536

Data Analytics for Core Temperature Estimation in Battery Management System for Electric and Hybrid Vehicles

Abstract

Publication series

Conference

UN SDGs

All Science Journal Classification (ASJC) codes

Access to Document

Other files and links

Fingerprint

Cite this