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An electro-thermal model and its electrical parameters estimation procedure in a lithium-ion battery cell

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  • Perez Estevez, Manuel Antonio
  • Calligaro, Sandro
  • Bottesi, Omar
  • Caligiuri, Carlo
  • Renzi, Massimiliano

Abstract

In this work, an electro-thermal model of a single lithium-ion battery cell has been developed in Simulink-Simscape environment. The model is divided in two interacting parts: the electrical and the thermal one. The electrical model consists of one Resistor-Capacitor branch circuit that is coupled to a thermal model consisting of a discretized volume, representing the battery cell, built on the basis of the thermal-electrical analogy. The heat generated by the cell is estimated using a lumped heat source approach. One of the novel aspects of the work is the definition and detailed description of an automatic procedure to extract the parameters of the RC circuit from pulse discharge tests, based on a Multi-Linear Regression Model approach. The electrical and thermal performance of the model was validated with dynamic and static measured data: the mean square error of the voltage prediction in dynamic conditions is 0.00027 V2. In static conditions, the mean square error of the voltage and temperature predictions are 0.014 V2 and 2.28 °C2, respectively. This model, due to its ease of application, can be used as a tool to define new modules architecture, as well as to support the design of the battery cooling system.

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  • 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).
    • Handle: RePEc:eee:energy:v:234:y:2021:i:c:s0360544221015449
    DOI: 10.1016/j.energy.2021.121296
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    References listed on IDEAS

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    1. Mathieu, Romain & Baghdadi, Issam & Briat, Olivier & Gyan, Philippe & Vinassa, Jean-Michel, 2017. "D-optimal design of experiments applied to lithium battery for ageing model calibration," Energy, Elsevier, vol. 141(C), pages 2108-2119.
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    4. Menale, Carla & D'Annibale, Francesco & Mazzarotta, Barbara & Bubbico, Roberto, 2019. "Thermal management of lithium-ion batteries: An experimental investigation," Energy, Elsevier, vol. 182(C), pages 57-71.
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    Cited by:

    1. Solai, Elie & Guadagnini, Maxime & Beaugendre, Héloïse & Daccord, Rémi & Congedo, Pietro, 2022. "Validation of a data-driven fast numerical model to simulate the immersion cooling of a lithium-ion battery pack," Energy, Elsevier, vol. 249(C).
    2. Morali, Ugur, 2022. "A numerical and statistical implementation of a thermal model for a lithium-ion battery," Energy, Elsevier, vol. 240(C).
    3. 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.
    4. Lu, Xin & Chen, Ning & Li, Hui & Guo, Shiyu & Chen, Zengtao, 2023. "Simulation of the temperature distribution of lithium-ion battery module considering the time-delay effect of the porous electrodes," Energy, Elsevier, vol. 284(C).
    5. Li, Kangqun & Zhou, Fei & Chen, Xing & Yang, Wen & Shen, Junjie & Song, Zebin, 2023. "State-of-charge estimation combination algorithm for lithium-ion batteries with Frobenius-norm-based QR decomposition modified adaptive cubature Kalman filter and H-infinity filter based on electro-th," Energy, Elsevier, vol. 263(PC).
    6. 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.
    7. Fahmy, Hend M. & Alqahtani, Ayedh H. & Hasanien, Hany M., 2024. "Precise modeling of lithium-ion battery in industrial applications using Walrus optimization algorithm," Energy, Elsevier, vol. 294(C).

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