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Thermal Modeling and Prediction of The Lithium-ion Battery Based on Driving Behavior

Author

Listed:
  • Tingting Wang

    (School of Mechanical and Power Engineering, Zhengzhou University, Zhengzhou 450001, China)

  • Xin Liu

    (School of Mechanical and Power Engineering, Zhengzhou University, Zhengzhou 450001, China)

  • Dongchen Qin

    (School of Mechanical and Power Engineering, Zhengzhou University, Zhengzhou 450001, China)

  • Yuechen Duan

    (School of Mechanical and Power Engineering, Zhengzhou University, Zhengzhou 450001, China)

Abstract

Real-time monitoring of the battery thermal status is important to ensure the effectiveness of battery thermal management system (BTMS), which can effectively avoid thermal runaway. In the study of BTMS, driver behavior is one of the factors affecting the performance of the battery thermal status, and it is often neglected in battery temperature studies. Therefore, it is necessary to predict the dynamic heat generation of the battery in actual driving cycles. In this work, a thermal equivalent circuit model (TECM) and an artificial neural network (ANN) thermal model based on the driving data, which can predict the thermal behavior of the battery in real-world driving cycles, are proposed and established by MATLAB/Simulink tool. Driving behaviors analysis of different drivers are simulated by PI control as input, and battery temperature is used as output response. The results show that aggressive driving behavior leads to an increase in battery temperature of nearly 1.2 K per second, and the average prediction error of TECM model and ANN model is 0.13 K and 0.11 K, respectively. This indicates that both models can accurately estimate the real-time battery temperature. However, the computational speed of the ANN thermal model is only 0.2 s, which is more efficient for battery thermal management.

Suggested Citation

  • Tingting Wang & Xin Liu & Dongchen Qin & Yuechen Duan, 2022. "Thermal Modeling and Prediction of The Lithium-ion Battery Based on Driving Behavior," Energies, MDPI, vol. 15(23), pages 1-22, November.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:23:p:9088-:d:989359
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    References listed on IDEAS

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    1. Deng, Zhongwei & Yang, Lin & Cai, Yishan & Deng, Hao & Sun, Liu, 2016. "Online available capacity prediction and state of charge estimation based on advanced data-driven algorithms for lithium iron phosphate battery," Energy, Elsevier, vol. 112(C), pages 469-480.
    2. Theodoros Kalogiannis & Md Sazzad Hosen & Mohsen Akbarzadeh Sokkeh & Shovon Goutam & Joris Jaguemont & Lu Jin & Geng Qiao & Maitane Berecibar & Joeri Van Mierlo, 2019. "Comparative Study on Parameter Identification Methods for Dual-Polarization Lithium-Ion Equivalent Circuit Model," Energies, MDPI, vol. 12(21), pages 1-35, October.
    3. Li, Zhenhe & Khajepour, Amir & Song, Jinchun, 2019. "A comprehensive review of the key technologies for pure electric vehicles," Energy, Elsevier, vol. 182(C), pages 824-839.
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    Cited by:

    1. Hao Fan & Lan Wang & Wei Chen & Bin Liu & Pengxin Wang, 2023. "A J-Type Air-Cooled Battery Thermal Management System Design and Optimization Based on the Electro-Thermal Coupled Model," Energies, MDPI, vol. 16(16), pages 1-19, August.

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