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Smart Core and Surface Temperature Estimation Techniques for Health-Conscious Lithium-Ion Battery Management Systems: A Model-to-Model Comparison

Author

Listed:
  • Sumukh Surya

    (Robert Bosch Engineering and Business Solutions, Bangalore 560100, India)

  • Akash Samanta

    (Department of Electrical, Computer and Software Engineering, Ontario Tech University, Oshawa, ON L1G 0C5, Canada)

  • Vinicius Marcis

    (Department of Electrical, Computer and Software Engineering, Ontario Tech University, Oshawa, ON L1G 0C5, Canada)

  • Sheldon Williamson

    (Department of Electrical, Computer and Software Engineering, Ontario Tech University, Oshawa, ON L1G 0C5, Canada)

Abstract

Estimation of core temperature is one of the crucial functionalities of the lithium-ion Battery Management System (BMS) towards providing effective thermal management, fault detection and operational safety. It is impractical to measure the core temperature of each cell using physical sensors, while at the same time implementing a complex core temperature estimation strategy in onboard low-cost BMS is also challenging due to high computational cost and the cost of implementation. Typically, a temperature estimation scheme consists of a heat generation model and a heat transfer model. Several researchers have already proposed ranges of thermal models with different levels of accuracy and complexity. Broadly, there are first-order and second-order heat resistor–capacitor-based thermal models of lithium-ion batteries (LIBs) for core and surface temperature estimation. This paper deals with a detailed comparative study between these two models using extensive laboratory test data and simulation study. The aim was to determine whether it is worth investing towards developing a second-order thermal model instead of a first-order model with respect to prediction accuracy considering the modeling complexity and experiments required. Both the thermal models along with the parameter estimation scheme were modeled and simulated in a MATLAB/Simulink environment. Models were validated using laboratory test data of a cylindrical 18,650 LIB cell. Further, a Kalman filter with appropriate process and measurement noise levels was used to estimate the core temperature in terms of measured surface and ambient temperatures. Results from the first-order model and second-order models were analyzed for comparison purposes.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:2:p:623-:d:726106
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    References listed on IDEAS

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    1. Jiangong Zhu & Zechang Sun & Xuezhe Wei & Haifeng Dai, 2017. "Battery Internal Temperature Estimation for LiFePO 4 Battery Based on Impedance Phase Shift under Operating Conditions," Energies, MDPI, vol. 10(1), pages 1-17, January.
    2. 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.
    3. Van Quan Dao & Minh-Chau Dinh & Chang Soon Kim & Minwon Park & Chil-Hoon Doh & Jeong Hyo Bae & Myung-Kwan Lee & Jianyong Liu & Zhiguo Bai, 2021. "Design of an Effective State of Charge Estimation Method for a Lithium-Ion Battery Pack Using Extended Kalman Filter and Artificial Neural Network," Energies, MDPI, vol. 14(9), pages 1-20, May.
    4. Lingxi Kong & Chuan Li & Jiuchun Jiang & Michael G. Pecht, 2018. "Li-Ion Battery Fire Hazards and Safety Strategies," Energies, MDPI, vol. 11(9), pages 1-11, August.
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