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Improved Digital Twin of Li-Ion Battery Based on Generic MATLAB Model

Author

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  • Juraj Bilansky

    (Department of Electrical Engineering and Mechatronics, Faculty of Electrical Engineering and Informatics, Technical University of Kosice, Letna 9, 042 00 Kosice, Slovakia)

  • Milan Lacko

    (Department of Electrical Engineering and Mechatronics, Faculty of Electrical Engineering and Informatics, Technical University of Kosice, Letna 9, 042 00 Kosice, Slovakia)

  • Marek Pastor

    (Department of Electrical Engineering and Mechatronics, Faculty of Electrical Engineering and Informatics, Technical University of Kosice, Letna 9, 042 00 Kosice, Slovakia)

  • Adrian Marcinek

    (Department of Electrical Engineering and Mechatronics, Faculty of Electrical Engineering and Informatics, Technical University of Kosice, Letna 9, 042 00 Kosice, Slovakia)

  • Frantisek Durovsky

    (Department of Electrical Engineering and Mechatronics, Faculty of Electrical Engineering and Informatics, Technical University of Kosice, Letna 9, 042 00 Kosice, Slovakia)

Abstract

The paper describes the digital twin of a Li-ion battery cell based on the MATLAB/Simulink generic model. The digital twin is based on measured data for constant current/constant voltage charging and discharging cycles with State of Health (SoH) up to 79%, also including fast charging. Mathematical equations used for the digital twin are obtained by 3D data fitting of measured SoH, battery capacity, and battery cell current. The input to the proposed digital twin is only the measured battery cell current, and its output includes State of Charge (SoC), SoH, and battery cell voltage. The designed digital twin is tested and compared with MATLAB/Simulink generic model and battery cell measurements for constant discharging current and dynamically generated discharging current profile. The results show significant improvement in the generic MATLAB/Simulink model.

Suggested Citation

  • Juraj Bilansky & Milan Lacko & Marek Pastor & Adrian Marcinek & Frantisek Durovsky, 2023. "Improved Digital Twin of Li-Ion Battery Based on Generic MATLAB Model," Energies, MDPI, vol. 16(3), pages 1-21, January.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:3:p:1194-:d:1043420
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    References listed on IDEAS

    as
    1. Jianfang Jia & Jianyu Liang & Yuanhao Shi & Jie Wen & Xiaoqiong Pang & Jianchao Zeng, 2020. "SOH and RUL Prediction of Lithium-Ion Batteries Based on Gaussian Process Regression with Indirect Health Indicators," Energies, MDPI, vol. 13(2), pages 1-20, January.
    2. Deng, Zhongwei & Hu, Xiaosong & Lin, Xianke & Che, Yunhong & Xu, Le & Guo, Wenchao, 2020. "Data-driven state of charge estimation for lithium-ion battery packs based on Gaussian process regression," Energy, Elsevier, vol. 205(C).
    3. Omar, Noshin & Monem, Mohamed Abdel & Firouz, Yousef & Salminen, Justin & Smekens, Jelle & Hegazy, Omar & Gaulous, Hamid & Mulder, Grietus & Van den Bossche, Peter & Coosemans, Thierry & Van Mierlo, J, 2014. "Lithium iron phosphate based battery – Assessment of the aging parameters and development of cycle life model," Applied Energy, Elsevier, vol. 113(C), pages 1575-1585.
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