IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v13y2020i21p5691-d437877.html
   My bibliography  Save this article

Lithium-Ion Battery SoC Equilibrium: An Artificial Potential Field-Based Method

Author

Listed:
  • Hongtao Liao

    (School of Automation, Central South University, Changsha 410083, China)

  • Fu Jiang

    (School of Computer Science and Engineering, Central South University, Changsha 410083, China)

  • Cheng Jin

    (School of Automation, Central South University, Changsha 410083, China)

  • Yue Wu

    (School of Automation, Central South University, Changsha 410083, China)

  • Heng Li

    (School of Computer Science and Engineering, Central South University, Changsha 410083, China)

  • Yongjie Liu

    (School of Automation, Central South University, Changsha 410083, China)

  • Zhiwu Huang

    (School of Automation, Central South University, Changsha 410083, China)

  • Jun Peng

    (School of Computer Science and Engineering, Central South University, Changsha 410083, China)

Abstract

Battery balance methods are the key technology to ensure the safe and efficient operation of the energy storage systems. Nevertheless, convenient balance methods experience slow convergence and difficult to adapt to quick charging applications. To solve the problem, in this paper, an artificial potential field-based lithium-ion battery balance method is proposed. Firstly, a cyber-physical model of the battery equalization system is proposed, in which the physical layer models the circuit components and the cyber layer represents the communication topology between the batteries. Then the virtual force function is established by artificial potential field to attract the voltage and state-of-charge of each cell to nominal values. With a feedback control law, the charging current of the battery is reasonably distributed to realize the rapid balance among batteries. The experimental results verify the effectiveness and superiority of the proposed method.

Suggested Citation

  • Hongtao Liao & Fu Jiang & Cheng Jin & Yue Wu & Heng Li & Yongjie Liu & Zhiwu Huang & Jun Peng, 2020. "Lithium-Ion Battery SoC Equilibrium: An Artificial Potential Field-Based Method," Energies, MDPI, vol. 13(21), pages 1-15, October.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:21:p:5691-:d:437877
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/13/21/5691/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/13/21/5691/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Zhong, Liang & Zhang, Chenbin & He, Yao & Chen, Zonghai, 2014. "A method for the estimation of the battery pack state of charge based on in-pack cells uniformity analysis," Applied Energy, Elsevier, vol. 113(C), pages 558-564.
    2. Herrera, Victor & Milo, Aitor & Gaztañaga, Haizea & Etxeberria-Otadui, Ion & Villarreal, Igor & Camblong, Haritza, 2016. "Adaptive energy management strategy and optimal sizing applied on a battery-supercapacitor based tramway," Applied Energy, Elsevier, vol. 169(C), pages 831-845.
    3. Wu, Yue & Huang, Zhiwu & Liao, Hongtao & Chen, Bin & Zhang, Xiaoyong & Zhou, Yanhui & Liu, Yongjie & Li, Heng & Peng, Jun, 2020. "Adaptive power allocation using artificial potential field with compensator for hybrid energy storage systems in electric vehicles," Applied Energy, Elsevier, vol. 257(C).
    4. Mahmoudzadeh Andwari, Amin & Pesiridis, Apostolos & Rajoo, Srithar & Martinez-Botas, Ricardo & Esfahanian, Vahid, 2017. "A review of Battery Electric Vehicle technology and readiness levels," Renewable and Sustainable Energy Reviews, Elsevier, vol. 78(C), pages 414-430.
    5. Long Cheng & Wei Wang & Shaoyuan Wei & Hongtao Lin & Zhidong Jia, 2018. "An Improved Energy Management Strategy for Hybrid Energy Storage System in Light Rail Vehicles," Energies, MDPI, vol. 11(2), pages 1-15, February.
    Full references (including those not matched with items on IDEAS)

    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. Mihai Machedon-Pisu & Paul Nicolae Borza, 2019. "Are Personal Electric Vehicles Sustainable? A Hybrid E-Bike Case Study," Sustainability, MDPI, vol. 12(1), pages 1-24, December.
    2. Ying Yang & Weige Zhang & Shaoyuan Wei & Zhenpo Wang, 2020. "Optimal Sizing of On-Board Energy Storage Systems and Stationary Charging Infrastructures for a Catenary-Free Tram," Energies, MDPI, vol. 13(23), pages 1-21, November.
    3. Hao Sun & Bo Jiang & Heze You & Bojian Yang & Xueyuan Wang & Xuezhe Wei & Haifeng Dai, 2021. "Quantitative Analysis of Degradation Modes of Lithium-Ion Battery under Different Operating Conditions," Energies, MDPI, vol. 14(2), pages 1-19, January.
    4. Thorne, Rebecca Jayne & Hovi, Inger Beate & Figenbaum, Erik & Pinchasik, Daniel Ruben & Amundsen, Astrid Helene & Hagman, Rolf, 2021. "Facilitating adoption of electric buses through policy: Learnings from a trial in Norway," Energy Policy, Elsevier, vol. 155(C).
    5. Tan, R.R. & Aviso, K.B. & Ng, D.K.S., 2019. "Optimization models for financing innovations in green energy technologies," Renewable and Sustainable Energy Reviews, Elsevier, vol. 113(C), pages 1-1.
    6. Hu, Lin & Hu, Xiaosong & Che, Yunhong & Feng, Fei & Lin, Xianke & Zhang, Zhiyong, 2020. "Reliable state of charge estimation of battery packs using fuzzy adaptive federated filtering," Applied Energy, Elsevier, vol. 262(C).
    7. Wang, Bin & Xu, Jun & Cao, Binggang & Ning, Bo, 2017. "Adaptive mode switch strategy based on simulated annealing optimization of a multi-mode hybrid energy storage system for electric vehicles," Applied Energy, Elsevier, vol. 194(C), pages 596-608.
    8. Li, Xiaoyu & Xu, Jianhua & Hong, Jianxun & Tian, Jindong & Tian, Yong, 2021. "State of energy estimation for a series-connected lithium-ion battery pack based on an adaptive weighted strategy," Energy, Elsevier, vol. 214(C).
    9. Yang, Jibin & Xu, Xiaohui & Peng, Yiqiang & Zhang, Jiye & Song, Pengyun, 2019. "Modeling and optimal energy management strategy for a catenary-battery-ultracapacitor based hybrid tramway," Energy, Elsevier, vol. 183(C), pages 1123-1135.
    10. Mattia Ricco & Jinhao Meng & Tudor Gherman & Gabriele Grandi & Remus Teodorescu, 2019. "Smart Battery Pack for Electric Vehicles Based on Active Balancing with Wireless Communication Feedback," Energies, MDPI, vol. 12(20), pages 1-15, October.
    11. Muhammad Khalid, 2019. "A Review on the Selected Applications of Battery-Supercapacitor Hybrid Energy Storage Systems for Microgrids," Energies, MDPI, vol. 12(23), pages 1-34, November.
    12. He, Hongwen & Xiong, Rui & Peng, Jiankun, 2016. "Real-time estimation of battery state-of-charge with unscented Kalman filter and RTOS μCOS-II platform," Applied Energy, Elsevier, vol. 162(C), pages 1410-1418.
    13. Qin, Yudi & Du, Jiuyu & Lu, Languang & Gao, Ming & Haase, Frank & Li, Jianqiu & Ouyang, Minggao, 2020. "A rapid lithium-ion battery heating method based on bidirectional pulsed current: Heating effect and impact on battery life," Applied Energy, Elsevier, vol. 280(C).
    14. Weina Qu & Hongli Sun & Yan Ge, 2021. "The effects of trait anxiety and the big five personality traits on self-driving car acceptance," Transportation, Springer, vol. 48(5), pages 2663-2679, October.
    15. Wang, Yujie & Zhang, Chenbin & Chen, Zonghai, 2014. "A method for joint estimation of state-of-charge and available energy of LiFePO4 batteries," Applied Energy, Elsevier, vol. 135(C), pages 81-87.
    16. Xingyue Jiang & Jianjun Hu & Meixia Jia & Yong Zheng, 2018. "Parameter Matching and Instantaneous Power Allocation for the Hybrid Energy Storage System of Pure Electric Vehicles," Energies, MDPI, vol. 11(8), pages 1-18, July.
    17. Santos, María Emma, 2019. "Non-monetary indicators to monitor SDG targets 1.2 and 1.4: standards, availability, comparability and quality," Estudios Estadísticos 44452, Naciones Unidas Comisión Económica para América Latina y el Caribe (CEPAL).
    18. Danijel Pavković & Mihael Cipek & Zdenko Kljaić & Tomislav Josip Mlinarić & Mario Hrgetić & Davor Zorc, 2018. "Damping Optimum-Based Design of Control Strategy Suitable for Battery/Ultracapacitor Electric Vehicles," Energies, MDPI, vol. 11(10), pages 1-26, October.
    19. Jiajun Liu & Tianxu Jin & Li Liu & Yajue Chen & Kun Yuan, 2017. "Multi-Objective Optimization of a Hybrid ESS Based on Optimal Energy Management Strategy for LHDs," Sustainability, MDPI, vol. 9(10), pages 1-18, October.
    20. Rahman, Syed & Khan, Irfan Ahmed & Khan, Ashraf Ali & Mallik, Ayan & Nadeem, Muhammad Faisal, 2022. "Comprehensive review & impact analysis of integrating projected electric vehicle charging load to the existing low voltage distribution system," Renewable and Sustainable Energy Reviews, Elsevier, vol. 153(C).

    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:13:y:2020:i:21:p:5691-:d:437877. 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.