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Robust Electro-Thermal Modeling of Lithium-Ion Batteries for Electrified Vehicles Applications

Author

Listed:
  • Mina Naguib

    (Department of Electrical and Computer Engineering, McMaster University, Hamilton, ON L8S 4L8, Canada)

  • Aashit Rathore

    (Department of Mechanical Engineering, McMaster University, Hamilton, ON L8S 4L8, Canada)

  • Nathan Emery

    (Department of Mechanical Engineering, McMaster University, Hamilton, ON L8S 4L8, Canada)

  • Shiva Ghasemi

    (Department of Mechanical Engineering, McMaster University, Hamilton, ON L8S 4L8, Canada)

  • Ryan Ahmed

    (Department of Mechanical Engineering, McMaster University, Hamilton, ON L8S 4L8, Canada)

Abstract

Lithium-ion battery (LIBs) packs represent the most expensive and safety-critical components in any electric vehicle, requiring accurate real-time thermal management. This task falls under the battery management system (BMS), which plays a crucial role in ensuring the longevity, safety, and optimal performance of batteries. The BMS accurately monitors cell temperatures and prevents thermal runaway by leveraging multiple temperature sensors; however, adding a temperature sensor to each individual cell is not practical and increases the total cost of the EV. This paper provides three key original contributions: (1) the development and optimization of a new efficient electro-thermal battery model that accurately estimates the LIB voltage and temperature, which reduces the required number of temperature sensors; (2) the investigation of the ECM parameters’ dependency on the state of charge (SOC) at a wide range of ambient temperatures, including cold temperatures; (3) the testing and validation of the proposed electro-thermal model using real-world dynamic drive cycles and temperature ranges from −20 to 25 °C. Results indicate the effectiveness of the proposed electro-thermal model, which shows good estimation accuracy with an average error of 50 mV and 0.5 °C for the battery voltage and surface temperature estimation, respectively.

Suggested Citation

  • Mina Naguib & Aashit Rathore & Nathan Emery & Shiva Ghasemi & Ryan Ahmed, 2023. "Robust Electro-Thermal Modeling of Lithium-Ion Batteries for Electrified Vehicles Applications," Energies, MDPI, vol. 16(16), pages 1-20, August.
  • Handle: RePEc:gam:jeners:v:16:y:2023:i:16:p:5887-:d:1213644
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    References listed on IDEAS

    as
    1. Wu, Hongfei & Zhang, Xingjuan & Cao, Renfeng & Yang, Chunxin, 2021. "An investigation on electrical and thermal characteristics of cylindrical lithium-ion batteries at low temperatures," Energy, Elsevier, vol. 225(C).
    2. Xiao Yang & Long Chen & Xing Xu & Wei Wang & Qiling Xu & Yuzhen Lin & Zhiguang Zhou, 2017. "Parameter Identification of Electrochemical Model for Vehicular Lithium-Ion Battery Based on Particle Swarm Optimization," Energies, MDPI, vol. 10(11), pages 1-16, November.
    3. Wang, Qian-Kun & He, Yi-Jun & Shen, Jia-Ni & Ma, Zi-Feng & Zhong, Guo-Bin, 2017. "A unified modeling framework for lithium-ion batteries: An artificial neural network based thermal coupled equivalent circuit model approach," Energy, Elsevier, vol. 138(C), pages 118-132.
    4. Zuchang Gao & Cheng Siong Chin & Wai Lok Woo & Junbo Jia, 2017. "Integrated Equivalent Circuit and Thermal Model for Simulation of Temperature-Dependent LiFePO 4 Battery in Actual Embedded Application," Energies, MDPI, vol. 10(1), pages 1-22, January.
    5. Zhu, Jiangong & Knapp, Michael & Darma, Mariyam Susana Dewi & Fang, Qiaohua & Wang, Xueyuan & Dai, Haifeng & Wei, Xuezhe & Ehrenberg, Helmut, 2019. "An improved electro-thermal battery model complemented by current dependent parameters for vehicular low temperature application," Applied Energy, Elsevier, vol. 248(C), pages 149-161.
    6. Perez Estevez, Manuel Antonio & Calligaro, Sandro & Bottesi, Omar & Caligiuri, Carlo & Renzi, Massimiliano, 2021. "An electro-thermal model and its electrical parameters estimation procedure in a lithium-ion battery cell," Energy, Elsevier, vol. 234(C).
    7. Farmann, Alexander & Sauer, Dirk Uwe, 2018. "Comparative study of reduced order equivalent circuit models for on-board state-of-available-power prediction of lithium-ion batteries in electric vehicles," Applied Energy, Elsevier, vol. 225(C), pages 1102-1122.
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