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Online Internal Temperature Estimation for Lithium-Ion Batteries Based on Kalman Filter

Author

Listed:
  • Jinlei Sun

    (School of Electrical Engineering and Automation, Harbin Institute of Technology, Harbin 150001, China)

  • Guo Wei

    (School of Electrical Engineering and Automation, Harbin Institute of Technology, Harbin 150001, China)

  • Lei Pei

    (School of Electrical Engineering and Automation, Harbin Institute of Technology, Harbin 150001, China)

  • Rengui Lu

    (School of Electrical Engineering and Automation, Harbin Institute of Technology, Harbin 150001, China)

  • Kai Song

    (School of Electrical Engineering and Automation, Harbin Institute of Technology, Harbin 150001, China)

  • Chao Wu

    (School of Electrical Engineering and Automation, Harbin Institute of Technology, Harbin 150001, China)

  • Chunbo Zhu

    (School of Electrical Engineering and Automation, Harbin Institute of Technology, Harbin 150001, China)

Abstract

The battery internal temperature estimation is important for the thermal safety in applications, because the internal temperature is hard to measure directly. In this work, an online internal temperature estimation method based on a simplified thermal model using a Kalman filter is proposed. As an improvement, the influences of entropy change and overpotential on heat generation are analyzed quantitatively. The model parameters are identified through a current pulse test. The charge/discharge experiments under different current rates are carried out on the same battery to verify the estimation results. The internal and surface temperatures are measured with thermocouples for result validation and model construction. The accuracy of the estimated result is validated with a maximum estimation error of around 1 K.

Suggested Citation

  • Jinlei Sun & Guo Wei & Lei Pei & Rengui Lu & Kai Song & Chao Wu & Chunbo Zhu, 2015. "Online Internal Temperature Estimation for Lithium-Ion Batteries Based on Kalman Filter," Energies, MDPI, vol. 8(5), pages 1-16, May.
    • Handle: RePEc:gam:jeners:v:8:y:2015:i:5:p:4400-4415:d:49637
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    References listed on IDEAS

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    1. Pei, Lei & Zhu, Chunbo & Wang, Tiansi & Lu, Rengui & Chan, C.C., 2014. "Online peak power prediction based on a parameter and state estimator for lithium-ion batteries in electric vehicles," Energy, Elsevier, vol. 66(C), pages 766-778.
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    Cited by:

    1. Xiaogang Wu & Zhe Chen & Zhiyang Wang, 2017. "Analysis of Low Temperature Preheating Effect Based on Battery Temperature-Rise Model," Energies, MDPI, vol. 10(8), pages 1-15, August.
    2. Raijmakers, L.H.J. & Danilov, D.L. & Eichel, R.-A. & Notten, P.H.L., 2019. "A review on various temperature-indication methods for Li-ion batteries," Applied Energy, Elsevier, vol. 240(C), pages 918-945.
    3. Hong, Jichao & Wang, Zhenpo & Chen, Wen & Yao, Yongtao, 2019. "Synchronous multi-parameter prediction of battery systems on electric vehicles using long short-term memory networks," Applied Energy, Elsevier, vol. 254(C).
    4. Huang, Deyang & Chen, Ziqiang & Zhou, Shiyao, 2022. "Self-powered heating strategy for lithium-ion battery pack applied in extremely cold climates," Energy, Elsevier, vol. 239(PB).
    5. 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.
    6. 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.
    7. Xiaogang Wu & Siyu Lv & Jizhong Chen, 2017. "Determination of the Optimum Heat Transfer Coefficient and Temperature Rise Analysis for a Lithium-Ion Battery under the Conditions of Harbin City Bus Driving Cycles," Energies, MDPI, vol. 10(11), pages 1-17, October.
    8. Saw, Lip Huat & Ye, Yonghuang & Yew, Ming Chian & Chong, Wen Tong & Yew, Ming Kun & Ng, Tan Ching, 2017. "Computational fluid dynamics simulation on open cell aluminium foams for Li-ion battery cooling system," Applied Energy, Elsevier, vol. 204(C), pages 1489-1499.
    9. Hua Zhang & Lei Pei & Jinlei Sun & Kai Song & Rengui Lu & Yongping Zhao & Chunbo Zhu & Tiansi Wang, 2016. "Online Diagnosis for the Capacity Fade Fault of a Parallel-Connected Lithium Ion Battery Group," Energies, MDPI, vol. 9(5), pages 1-18, May.

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