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
N1 - Publisher Copyright:
© 2023 IEEE.
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/inward/citedby.url?scp=85168674276&partnerID=8YFLogxK
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 -
