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Temperature Dependent Parameter Estimation of Electrical Vehicle Batteries

Author

Listed:
  • Anna I. Pózna

    (Department of Electrical Engineering and Information Systems, University of Pannonia, Egyetem Street 10, H-8200 Veszprém, Hungary)

  • Katalin M. Hangos

    (Department of Electrical Engineering and Information Systems, University of Pannonia, Egyetem Street 10, H-8200 Veszprém, Hungary
    Institute for Computer Science and Control, Systems and Control Laboratory, Kende Street 13-17, H-1111 Budapest, Hungary)

  • Attila Magyar

    (Department of Electrical Engineering and Information Systems, University of Pannonia, Egyetem Street 10, H-8200 Veszprém, Hungary)

Abstract

Parameter estimation of electrical vehicle batteries in the presence of temperature effect is addressed in this work. A simple parametric temperature dependent battery model is used for this purpose where the temperature dependence is described by static relationships. A two-step method is used that includes a parameter estimation step of the key parameters at different temperatures followed by a static optimization step that determines the temperature coefficients of the corresponding parameters. It was found that the temperature dependent parameter characteristics can be reliably estimated from charging profiles only. The proposed method can be used as a computationally effective way of determining the key battery parameters at a given temperature from their actual estimated values and from their previously determined static temperature dependence. The proposed parameter estimation method was verified by simulation experiments on a more complex battery model that also describes the detailed dynamic thermal behavior of the battery.

Suggested Citation

  • Anna I. Pózna & Katalin M. Hangos & Attila Magyar, 2019. "Temperature Dependent Parameter Estimation of Electrical Vehicle Batteries," Energies, MDPI, vol. 12(19), pages 1-18, September.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:19:p:3755-:d:272497
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    References listed on IDEAS

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    1. Manoj Mathew & Stefan Janhunen & Mahir Rashid & Frank Long & Michael Fowler, 2018. "Comparative Analysis of Lithium-Ion Battery Resistance Estimation Techniques for Battery Management Systems," Energies, MDPI, vol. 11(6), pages 1-15, June.
    2. Ruifeng Zhang & Bizhong Xia & Baohua Li & Libo Cao & Yongzhi Lai & Weiwei Zheng & Huawen Wang & Wei Wang & Mingwang Wang, 2018. "A Study on the Open Circuit Voltage and State of Charge Characterization of High Capacity Lithium-Ion Battery Under Different Temperature," Energies, MDPI, vol. 11(9), pages 1-17, September.
    3. Wang, Qian & Jiang, Bin & Li, Bo & Yan, Yuying, 2016. "A critical review of thermal management models and solutions of lithium-ion batteries for the development of pure electric vehicles," Renewable and Sustainable Energy Reviews, Elsevier, vol. 64(C), pages 106-128.
    4. Fei Feng & Rengui Lu & Guo Wei & Chunbo Zhu, 2015. "Online Estimation of Model Parameters and State of Charge of LiFePO 4 Batteries Using a Novel Open-Circuit Voltage at Various Ambient Temperatures," Energies, MDPI, vol. 8(4), pages 1-27, April.
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    Cited by:

    1. György Károlyi & Anna I. Pózna & Katalin M. Hangos & Attila Magyar, 2022. "An Optimized Fuzzy Controlled Charging System for Lithium-Ion Batteries Using a Genetic Algorithm," Energies, MDPI, vol. 15(2), pages 1-23, January.
    2. Arunodaya Raj Mishra & Dinesh Kumar Tripathi & Fausto Cavallaro & Pratibha Rani & Santosh K. Nigam & Abbas Mardani, 2022. "Assessment of Battery Energy Storage Systems Using the Intuitionistic Fuzzy Removal Effects of Criteria and the Measurement of Alternatives and Ranking Based on Compromise Solution Method," Energies, MDPI, vol. 15(20), pages 1-23, October.

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