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Estimation and Comparison of SOC in Batteries Used in Electromobility Using the Thevenin Model and Coulomb Ampere Counting

Author

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  • Diego Salazar

    (Research Group on Smart Electrical Networks (GIREI), South Campus, Quito Headquarters, School of Engineering, Universidad Politecnica Salesiana, Cuenca 010105, Ecuador)

  • Marcelo Garcia

    (Research Group on Smart Electrical Networks (GIREI), South Campus, Quito Headquarters, School of Engineering, Universidad Politecnica Salesiana, Cuenca 010105, Ecuador)

Abstract

Nowadays, due to the increasing use of electric vehicles, manufacturers are making more and more innovations in the batteries used in electromobility, in order to make these vehicles more efficient and provide them with greater autonomy. This has led to the need to evaluate and compare the efficiency of different batteries used in electric vehicles to determine which one is the best to be implemented. This paper characterises, models and compares three batteries used in electromobility: lithium-ion, lead-acid, and nickel metal hydride, and determines which of these three is the most efficient based on their state of charge. The main drawback to determine the state of charge is that there are a great variety of methods and models used for this purpose; in this article, the Thévenin model and the Coulomb Count method are used to determine the state of charge of the battery. When obtaining the electrical parameters, the simulation of the same is carried out, which indicates that the most efficient battery is the Lithium-ion battery presenting the best performance of state of charge, reaching 99.05% in the charging scenario, while, in the discharge scenario, it reaches a minimum value of 40.68%; in contrast, the least efficient battery is the lead acid battery, presenting in the charging scenario a maximum value of 98.42%, and in the discharge scenario a minimum value of 10.35%, presenting a deep discharge. This indicates that the lithium-ion battery is the most efficient in both the charge and discharge scenarios, and is the best option for use in electric vehicles. In this paper, it was decided to use the Coulomb ampere counting method together with the Thévenin equivalent circuit model because it was determined that the combination of these two methods to estimate the SOC can be applied to any battery, not only applicable to electric vehicle batteries, but to battery banks, BESS systems, or any system or equipment that has batteries for its operation, while the models based on Kalman, or models based on fuzzy mathematics and neural networks, as they are often used and are applicable only to a specific battery system.

Suggested Citation

  • Diego Salazar & Marcelo Garcia, 2022. "Estimation and Comparison of SOC in Batteries Used in Electromobility Using the Thevenin Model and Coulomb Ampere Counting," Energies, MDPI, vol. 15(19), pages 1-13, September.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:19:p:7204-:d:930343
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    References listed on IDEAS

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    1. Han, Xiaojuan & Wang, Zuran & Wei, Zixuan, 2021. "A novel approach for health management online-monitoring of lithium-ion batteries based on model-data fusion," Applied Energy, Elsevier, vol. 302(C).
    2. Alejandro Gismero & Erik Schaltz & Daniel-Ioan Stroe, 2020. "Recursive State of Charge and State of Health Estimation Method for Lithium-Ion Batteries Based on Coulomb Counting and Open Circuit Voltage," Energies, MDPI, vol. 13(7), pages 1-11, April.
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    Cited by:

    1. Aihua Wu & Yan Zhou & Jingfeng Mao & Xudong Zhang & Junqiang Zheng, 2023. "An Improved Multi-Timescale AEKF–AUKF Joint Algorithm for State-of-Charge Estimation of Lithium-Ion Batteries," Energies, MDPI, vol. 16(16), pages 1-24, August.
    2. Bachir Zine & Haithem Bia & Amel Benmouna & Mohamed Becherif & Mehroze Iqbal, 2022. "Experimentally Validated Coulomb Counting Method for Battery State-of-Charge Estimation under Variable Current Profiles," Energies, MDPI, vol. 15(21), pages 1-15, November.
    3. Xingxing Wang & Peilin Ye & Shengren Liu & Yu Zhu & Yelin Deng & Yinnan Yuan & Hongjun Ni, 2023. "Research Progress of Battery Life Prediction Methods Based on Physical Model," Energies, MDPI, vol. 16(9), pages 1-20, April.

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