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Core Temperature Estimation for a Lithium ion 18650 Cell

Author

Listed:
  • Sumukh Surya

    (e-PowerTrain, KPIT, Bangalore 560103, India)

  • Vinicius Marcis

    (Advanced Storage Systems and Electric Transportation (ASSET) Laboratory, 11110 Artesia Blvd, Cerritos, CA 90703, USA)

  • Sheldon Williamson

    (Department of Electrical, Computer, and Software Engineering, University of Ontario Institute of Technology, Oshawa, ON L1H 7K4, Canada)

Abstract

This paper deals with the estimation of core temperature (T c ) of a Lithium (Li) ion battery using measured ambient and surface temperatures. The temperatures were measured using thermocouples placed at appropriate locations. A second order thermal model was considered for the core temperature (T c ) estimation. A set of coupled linear ordinary differential equations (ODEs) were obtained by applying Kirchhoff’s current and voltage laws to the thermal model. The coupled ODEs were redefined in the discrete state space representation. The thermal model did not account for small changes in surface temperature (T s ). MATLAB/Simulink were used for modelling a Kalman filter with appropriate process and measurement noise levels. It was found that the temperatures closely followed the current patterns. For high currents, T c dominated the surface temperature by about 3 K. T c estimation plays a very important role in designing an effective thermal management and maintaining the state of health (SOH) during fast discharges under limits. Most of the battery management system (BMS) applications required T s as the input to the controller. Hence, an inverse calculation for estimating T s from known T c was carried out and found to be reasonably accurate. It was found that the thermal parameter C s played a major role in the accuracy of T s prediction and must have low values to minimize errors.

Suggested Citation

  • Sumukh Surya & Vinicius Marcis & Sheldon Williamson, 2020. "Core Temperature Estimation for a Lithium ion 18650 Cell," Energies, MDPI, vol. 14(1), pages 1-21, December.
    • Handle: RePEc:gam:jeners:v:14:y:2020:i:1:p:87-:d:468547
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    Citations

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    Cited by:

    1. Sumukh Surya & Akash Samanta & Vinicius Marcis & Sheldon Williamson, 2022. "Smart Core and Surface Temperature Estimation Techniques for Health-Conscious Lithium-Ion Battery Management Systems: A Model-to-Model Comparison," Energies, MDPI, vol. 15(2), pages 1-21, January.
    2. Akash Samanta & Sheldon S. Williamson, 2021. "A Comprehensive Review of Lithium-Ion Cell Temperature Estimation Techniques Applicable to Health-Conscious Fast Charging and Smart Battery Management Systems," Energies, MDPI, vol. 14(18), pages 1-25, September.
    3. Sumukh Surya & Vidya Rao & Sheldon S. Williamson, 2021. "Comprehensive Review on Smart Techniques for Estimation of State of Health for Battery Management System Application," Energies, MDPI, vol. 14(15), pages 1-22, July.
    4. Thomas Imre Cyrille Buidin & Florin Mariasiu, 2021. "Battery Thermal Management Systems: Current Status and Design Approach of Cooling Technologies," Energies, MDPI, vol. 14(16), pages 1-32, August.

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