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Lithium-ion Battery Electrothermal Model, Parameter Estimation, and Simulation Environment

Author

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  • Simone Orcioni

    (Dipartimento di Ingegneria dell’Informazione, Università Politecnica delle Marche, Ancona 60131, Italy)

  • Luca Buccolini

    (Dipartimento di Ingegneria dell’Informazione, Università Politecnica delle Marche, Ancona 60131, Italy)

  • Adriana Ricci

    (Dipartimento di Ingegneria dell’Informazione, Università Politecnica delle Marche, Ancona 60131, Italy)

  • Massimo Conti

    (Dipartimento di Ingegneria dell’Informazione, Università Politecnica delle Marche, Ancona 60131, Italy)

Abstract

The market for lithium-ion batteries is growing exponentially. The performance of battery cells is growing due to improving production technology, but market request is growing even more rapidly. Modeling and characterization of single cells and an efficient simulation environment is fundamental for the development of an efficient battery management system. The present work is devoted to defining a novel lumped electrothermal circuit of a single battery cell, the extraction procedure of the parameters of the single cell from experiments, and a simulation environment in SystemC-WMS for the simulation of a battery pack. The electrothermal model of the cell was validated against experimental measurements obtained in a climatic chamber. The model is then used to simulate a 48-cell battery, allowing statistical variations among parameters. The different behaviors of the cells in terms of state of charge, current, voltage, or heat flow rate can be observed in the results of the simulation environment.

Suggested Citation

  • Simone Orcioni & Luca Buccolini & Adriana Ricci & Massimo Conti, 2017. "Lithium-ion Battery Electrothermal Model, Parameter Estimation, and Simulation Environment," Energies, MDPI, vol. 10(3), pages 1-20, March.
    • Handle: RePEc:gam:jeners:v:10:y:2017:i:3:p:375-:d:93234
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    References listed on IDEAS

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    1. Ng, Kong Soon & Moo, Chin-Sien & Chen, Yi-Ping & Hsieh, Yao-Ching, 2009. "Enhanced coulomb counting method for estimating state-of-charge and state-of-health of lithium-ion batteries," Applied Energy, Elsevier, vol. 86(9), pages 1506-1511, September.
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    Cited by:

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    2. Ramin Sakipour & Hamdi Abdi, 2020. "Optimizing Battery Energy Storage System Data in the Presence of Wind Power Plants: A Comparative Study on Evolutionary Algorithms," Sustainability, MDPI, vol. 12(24), pages 1-21, December.

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