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Advanced Lithium-Ion Battery Model for Power System Performance Analysis

Author

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  • Szymon Potrykus

    (Faculty of Electrical and Control Engineering, Gdańsk University of Technology, 80-233 Gdańsk, Poland
    Department of Chemical Engineering, Instituto de Tecnología Química y Medioambiental, University of Castilla-La Mancha, 13071 Ciudad Real, Spain)

  • Filip Kutt

    (Faculty of Electrical and Control Engineering, Gdańsk University of Technology, 80-233 Gdańsk, Poland)

  • Janusz Nieznański

    (Faculty of Electrical and Control Engineering, Gdańsk University of Technology, 80-233 Gdańsk, Poland)

  • Francisco Jesús Fernández Morales

    (Department of Chemical Engineering, Instituto de Tecnología Química y Medioambiental, University of Castilla-La Mancha, 13071 Ciudad Real, Spain)

Abstract

The paper describes a novel approach in battery storage system modelling. Different types of lithium-ion batteries exhibit differences in performance due to the battery anode and cathode materials being the determining factors in the storage system performance. Because of this, the influence of model parameters on the model accuracy can be different for different battery types. These models are used in battery management system development for increasing the accuracy of SoC and SoH estimation. The model proposed in this work is based on Tremblay model of the lithium-ion battery. The novelty of the model lies in the approach used for parameter estimation as a function of battery physical properties. To make the model perform more accurately, the diffusion resistance dependency on the battery current and the Peukert effect were also included in the model. The proposed battery model was validated using laboratory measurements with a LG JP 1.5 lithium-ion battery. Additionally, the proposed model incorporates the influence of the battery charge and discharge current level on battery performance.

Suggested Citation

  • Szymon Potrykus & Filip Kutt & Janusz Nieznański & Francisco Jesús Fernández Morales, 2020. "Advanced Lithium-Ion Battery Model for Power System Performance Analysis," Energies, MDPI, vol. 13(10), pages 1-15, May.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:10:p:2411-:d:357077
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    References listed on IDEAS

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    1. Ines Baccouche & Sabeur Jemmali & Bilal Manai & Noshin Omar & Najoua Essoukri Ben Amara, 2017. "Improved OCV Model of a Li-Ion NMC Battery for Online SOC Estimation Using the Extended Kalman Filter," Energies, MDPI, vol. 10(6), pages 1-22, May.
    2. Schimpe, Michael & Naumann, Maik & Truong, Nam & Hesse, Holger C. & Santhanagopalan, Shriram & Saxon, Aron & Jossen, Andreas, 2018. "Energy efficiency evaluation of a stationary lithium-ion battery container storage system via electro-thermal modeling and detailed component analysis," Applied Energy, Elsevier, vol. 210(C), pages 211-229.
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    Cited by:

    1. Josef Stetina & Michael Bohm & Michal Brezina, 2021. "Small Cogeneration Unit with Heat and Electricity Storage," Energies, MDPI, vol. 14(8), pages 1-13, April.
    2. Namala Narasimhulu & R. S. R. Krishnam Naidu & Przemysław Falkowski-Gilski & Parameshachari Bidare Divakarachari & Upendra Roy, 2022. "Energy Management for PV Powered Hybrid Storage System in Electric Vehicles Using Artificial Neural Network and Aquila Optimizer Algorithm," Energies, MDPI, vol. 15(22), pages 1-21, November.
    3. Nicola Campagna & Vincenzo Castiglia & Rosario Miceli & Rosa Anna Mastromauro & Ciro Spataro & Marco Trapanese & Fabio Viola, 2020. "Battery Models for Battery Powered Applications: A Comparative Study," Energies, MDPI, vol. 13(16), pages 1-26, August.
    4. Dariusz Karkosiński & Wojciech Aleksander Rosiński & Piotr Deinrych & Szymon Potrykus, 2021. "Onboard Energy Storage and Power Management Systems for All-Electric Cargo Vessel Concept," Energies, MDPI, vol. 14(4), pages 1-16, February.
    5. Muhammad Waseem & Jingyuan Huang & Chak-Nam Wong & C. K. M. Lee, 2023. "Data-Driven GWO-BRNN-Based SOH Estimation of Lithium-Ion Batteries in EVs for Their Prognostics and Health Management," Mathematics, MDPI, vol. 11(20), pages 1-27, October.
    6. Shaheer Ansari & Afida Ayob & Molla Shahadat Hossain Lipu & Aini Hussain & Mohamad Hanif Md Saad, 2021. "Multi-Channel Profile Based Artificial Neural Network Approach for Remaining Useful Life Prediction of Electric Vehicle Lithium-Ion Batteries," Energies, MDPI, vol. 14(22), pages 1-22, November.
    7. Szymon Potrykus & Luis Fernando León-Fernández & Janusz Nieznański & Dariusz Karkosiński & Francisco Jesus Fernandez-Morales, 2021. "The Influence of External Load on the Performance of Microbial Fuel Cells," Energies, MDPI, vol. 14(3), pages 1-11, January.
    8. Szymon Potrykus & Sara Mateo & Janusz Nieznański & Francisco Jesús Fernández-Morales, 2020. "The Influent Effects of Flow Rate Profile on the Performance of Microbial Fuel Cells Model," Energies, MDPI, vol. 13(18), pages 1-15, September.

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    Keywords

    lithium-ion batteries; battery modelling;

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