IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v15y2022i19p7204-d930343.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/15/19/7204/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/15/19/7204/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. 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.
    2. 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).
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Chen, Zheng & Zhao, Hongqian & Shu, Xing & Zhang, Yuanjian & Shen, Jiangwei & Liu, Yonggang, 2021. "Synthetic state of charge estimation for lithium-ion batteries based on long short-term memory network modeling and adaptive H-Infinity filter," Energy, Elsevier, vol. 228(C).
    2. Bizhong Xia & Guanyong Zhang & Huiyuan Chen & Yuheng Li & Zhuojun Yu & Yunchao Chen, 2022. "Verification Platform of SOC Estimation Algorithm for Lithium-Ion Batteries of Electric Vehicles," Energies, MDPI, vol. 15(9), pages 1-20, April.
    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. Panpan Hu & W. F. Tang & C. H. Li & Shu-Lun Mak & C. Y. Li & C. C. Lee, 2023. "Joint State of Charge (SOC) and State of Health (SOH) Estimation for Lithium-Ion Batteries Packs of Electric Vehicles Based on NSSR-LSTM Neural Network," Energies, MDPI, vol. 16(14), pages 1-19, July.
    5. Song, Dengwei & Cheng, Yujie & Zhou, An & Lu, Chen & Chong, Jin & Ma, Jian, 2024. "Remaining useful life prediction and cycle life test optimization for multiple-formula battery: A method based on multi-source transfer learning," Reliability Engineering and System Safety, Elsevier, vol. 249(C).
    6. Zhu, Yunlong & Dong, Zhe & Cheng, Zhonghua & Huang, Xiaojin & Dong, Yujie & Zhang, Zuoyi, 2023. "Neural network extended state-observer for energy system monitoring," Energy, Elsevier, vol. 263(PA).
    7. Meng, Huixing & Geng, Mengyao & Han, Te, 2023. "Long short-term memory network with Bayesian optimization for health prognostics of lithium-ion batteries based on partial incremental capacity analysis," Reliability Engineering and System Safety, Elsevier, vol. 236(C).
    8. Zhang, Jiarui & Wang, Chao & Li, Jinzhong & Xie, Yuguang & Mao, Lei & Hu, Zhiyong, 2023. "A Bayesian method for capacity degradation prediction of lithium-ion battery considering both within and cross group heterogeneity," Applied Energy, Elsevier, vol. 351(C).
    9. Sneha Sundaresan & Bharath Chandra Devabattini & Pradeep Kumar & Krishna R. Pattipati & Balakumar Balasingam, 2022. "Tabular Open Circuit Voltage Modelling of Li-Ion Batteries for Robust SOC Estimation," Energies, MDPI, vol. 15(23), pages 1-23, December.
    10. Li, Shuangqi & He, Hongwen & Zhao, Pengfei & Cheng, Shuang, 2022. "Health-Conscious vehicle battery state estimation based on deep transfer learning," Applied Energy, Elsevier, vol. 316(C).
    11. Wang, Cong & Chen, Yunxia & Zhang, Qingyuan & Zhu, Jiaxiao, 2023. "Dynamic early recognition of abnormal lithium-ion batteries before capacity drops using self-adaptive quantum clustering," Applied Energy, Elsevier, vol. 336(C).
    12. 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.
    13. Jong-Hyun Lee & In-Soo Lee, 2021. "Lithium Battery SOH Monitoring and an SOC Estimation Algorithm Based on the SOH Result," Energies, MDPI, vol. 14(15), pages 1-16, July.
    14. Yang Guo & Ziguang Lu, 2022. "A Robust Algorithm for State-of-Charge Estimation under Model Uncertainty and Voltage Sensor Bias," Energies, MDPI, vol. 15(4), pages 1-18, February.
    15. Tom Verstraten & Md Sazzad Hosen & Maitane Berecibar & Bram Vanderborght, 2023. "Selecting Suitable Battery Technologies for Untethered Robot," Energies, MDPI, vol. 16(13), pages 1-21, June.
    16. Li, Shuangqi & He, Hongwen & Zhao, Pengfei & Cheng, Shuang, 2022. "Data cleaning and restoring method for vehicle battery big data platform," Applied Energy, Elsevier, vol. 320(C).
    17. Chien-Chun Huang & Yu-Chen Liu & Chia-Ching Lin & Chih-Yu Ni & Huang-Jen Chiu, 2020. "Stacked Buck Converter: Current Ripple Elimination Effect and Transient Response," Energies, MDPI, vol. 14(1), pages 1-25, December.
    18. Jingyeong Park & Jeonghyeon Choi & Hyeondeok Jo & Daisuke Kodaira & Sekyung Han & Moses Amoasi Acquah, 2022. "Life Evaluation of Battery Energy System for Frequency Regulation Using Wear Density Function," Energies, MDPI, vol. 15(21), pages 1-16, October.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:15:y:2022:i:19:p:7204-:d:930343. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.