IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v8y2015i8p7703-7728d53313.html
   My bibliography  Save this article

A Novel Grouping Method for Lithium Iron Phosphate Batteries Based on a Fractional Joint Kalman Filter and a New Modified K-Means Clustering Algorithm

Author

Listed:
  • Xiaoyu Li

    (School of Electrical Engineering and Automation, Harbin Institute of Technology, Harbin 150001, China)

  • Kai Song

    (School of Electrical Engineering and Automation, Harbin Institute of Technology, Harbin 150001, China)

  • Guo Wei

    (School of Electrical Engineering and Automation, Harbin Institute of Technology, Harbin 150001, China)

  • Rengui Lu

    (School of Electrical Engineering and Automation, Harbin Institute of Technology, Harbin 150001, China)

  • Chunbo Zhu

    (School of Electrical Engineering and Automation, Harbin Institute of Technology, Harbin 150001, China)

Abstract

This paper presents a novel grouping method for lithium iron phosphate batteries. In this method, a simplified electrochemical impedance spectroscopy (EIS) model is utilized to describe the battery characteristics. Dynamic stress test (DST) and fractional joint Kalman filter (FJKF) are used to extract battery model parameters. In order to realize equal-number grouping of batteries, a new modified K-means clustering algorithm is proposed. Two rules are designed to equalize the numbers of elements in each group and exchange samples among groups. In this paper, the principles of battery model selection, physical meaning and identification method of model parameters, data preprocessing and equal-number clustering method for battery grouping are comprehensively described. Additionally, experiments for battery grouping and method validation are designed. This method is meaningful to application involving the grouping of fresh batteries for electric vehicles (EVs) and screening of aged batteries for recycling.

Suggested Citation

  • Xiaoyu Li & Kai Song & Guo Wei & Rengui Lu & Chunbo Zhu, 2015. "A Novel Grouping Method for Lithium Iron Phosphate Batteries Based on a Fractional Joint Kalman Filter and a New Modified K-Means Clustering Algorithm," Energies, MDPI, vol. 8(8), pages 1-26, July.
    • Handle: RePEc:gam:jeners:v:8:y:2015:i:8:p:7703-7728:d:53313
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/8/8/7703/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/8/8/7703/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Kim, Jonghoon & Cho, B.H., 2013. "Screening process-based modeling of the multi-cell battery string in series and parallel connections for high accuracy state-of-charge estimation," Energy, Elsevier, vol. 57(C), pages 581-599.
    2. Wang, Tao & Tseng, K.J. & Zhao, Jiyun & Wei, Zhongbao, 2014. "Thermal investigation of lithium-ion battery module with different cell arrangement structures and forced air-cooling strategies," Applied Energy, Elsevier, vol. 134(C), pages 229-238.
    3. Waag, Wladislaw & Käbitz, Stefan & Sauer, Dirk Uwe, 2013. "Experimental investigation of the lithium-ion battery impedance characteristic at various conditions and aging states and its influence on the application," Applied Energy, Elsevier, vol. 102(C), pages 885-897.
    4. Xiong, Rui & Sun, Fengchun & He, Hongwen & Nguyen, Trong Duy, 2013. "A data-driven adaptive state of charge and power capability joint estimator of lithium-ion polymer battery used in electric vehicles," Energy, Elsevier, vol. 63(C), pages 295-308.
    5. Fei Feng & Rengui Lu & Chunbo Zhu, 2014. "A Combined State of Charge Estimation Method for Lithium-Ion Batteries Used in a Wide Ambient Temperature Range," Energies, MDPI, vol. 7(5), pages 1-29, May.
    6. Pei, Lei & Zhu, Chunbo & Wang, Tiansi & Lu, Rengui & Chan, C.C., 2014. "Online peak power prediction based on a parameter and state estimator for lithium-ion batteries in electric vehicles," Energy, Elsevier, vol. 66(C), pages 766-778.
    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. Liangwen Yan & Fengfeng Qian & Wei Li, 2018. "Research on Key Parameters Operation Range of Central Air Conditioning Based on Binary K-Means and Apriori Algorithm," Energies, MDPI, vol. 12(1), pages 1-13, December.
    2. Tian, Jiaqiang & Fan, Yuan & Pan, Tianhong & Zhang, Xu & Yin, Jianning & Zhang, Qingping, 2024. "A critical review on inconsistency mechanism, evaluation methods and improvement measures for lithium-ion battery energy storage systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 189(PB).
    3. Xin Lu & Hui Li & Jun Xu & Siyuan Chen & Ning Chen, 2018. "Rapid Estimation Method for State of Charge of Lithium-Ion Battery Based on Fractional Continual Variable Order Model," Energies, MDPI, vol. 11(4), pages 1-18, March.
    4. Sergio Valdivia & Ricardo Soto & Broderick Crawford & Nicolás Caselli & Fernando Paredes & Carlos Castro & Rodrigo Olivares, 2020. "Clustering-Based Binarization Methods Applied to the Crow Search Algorithm for 0/1 Combinatorial Problems," Mathematics, MDPI, vol. 8(7), pages 1-42, July.
    5. Paul Stewart & Chris Bingham, 2016. "Electrical Power and Energy Systems for Transportation Applications," Energies, MDPI, vol. 9(7), pages 1-3, July.
    6. Hua Zhang & Lei Pei & Jinlei Sun & Kai Song & Rengui Lu & Yongping Zhao & Chunbo Zhu & Tiansi Wang, 2016. "Online Diagnosis for the Capacity Fade Fault of a Parallel-Connected Lithium Ion Battery Group," Energies, MDPI, vol. 9(5), pages 1-18, May.

    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. Fei Feng & Rengui Lu & Guo Wei & Chunbo Zhu, 2015. "Online Estimation of Model Parameters and State of Charge of LiFePO 4 Batteries Using a Novel Open-Circuit Voltage at Various Ambient Temperatures," Energies, MDPI, vol. 8(4), pages 1-27, April.
    2. Feng, Xuning & Weng, Caihao & Ouyang, Minggao & Sun, Jing, 2016. "Online internal short circuit detection for a large format lithium ion battery," Applied Energy, Elsevier, vol. 161(C), pages 168-180.
    3. Shen, Yanqing, 2014. "Hybrid unscented particle filter based state-of-charge determination for lead-acid batteries," Energy, Elsevier, vol. 74(C), pages 795-803.
    4. Zheng, Fangdan & Jiang, Jiuchun & Sun, Bingxiang & Zhang, Weige & Pecht, Michael, 2016. "Temperature dependent power capability estimation of lithium-ion batteries for hybrid electric vehicles," Energy, Elsevier, vol. 113(C), pages 64-75.
    5. Bo Jiang & Haifeng Dai & Xuezhe Wei & Letao Zhu & Zechang Sun, 2017. "Online Reliable Peak Charge/Discharge Power Estimation of Series-Connected Lithium-Ion Battery Packs," Energies, MDPI, vol. 10(3), pages 1-22, March.
    6. Thanh-Tung Nguyen & Abdul Basit Khan & Younghwi Ko & Woojin Choi, 2020. "An Accurate State of Charge Estimation Method for Lithium Iron Phosphate Battery Using a Combination of an Unscented Kalman Filter and a Particle Filter," Energies, MDPI, vol. 13(17), pages 1-15, September.
    7. Qiaohua Fang & Xuezhe Wei & Haifeng Dai, 2019. "A Remaining Discharge Energy Prediction Method for Lithium-Ion Battery Pack Considering SOC and Parameter Inconsistency," Energies, MDPI, vol. 12(6), pages 1-24, March.
    8. Singh, Karanjot & Tjahjowidodo, Tegoeh & Boulon, Loïc & Feroskhan, Mir, 2022. "Framework for measurement of battery state-of-health (resistance) integrating overpotential effects and entropy changes using energy equilibrium," Energy, Elsevier, vol. 239(PA).
    9. Xingtao Liu & Chaoyi Zheng & Ji Wu & Jinhao Meng & Daniel-Ioan Stroe & Jiajia Chen, 2020. "An Improved State of Charge and State of Power Estimation Method Based on Genetic Particle Filter for Lithium-ion Batteries," Energies, MDPI, vol. 13(2), pages 1-16, January.
    10. Raijmakers, L.H.J. & Danilov, D.L. & Eichel, R.-A. & Notten, P.H.L., 2019. "A review on various temperature-indication methods for Li-ion batteries," Applied Energy, Elsevier, vol. 240(C), pages 918-945.
    11. Zhang, Yuan Ci & Briat, Olivier & Boulon, Loïc & Deletage, Jean-Yves & Martin, Cyril & Coccetti, Fabio & Vinassa, Jean-Michel, 2019. "Non-isothermal Ragone plots of Li-ion cells from datasheet and galvanostatic discharge tests," Applied Energy, Elsevier, vol. 247(C), pages 703-715.
    12. Burgos-Mellado, Claudio & Orchard, Marcos E. & Kazerani, Mehrdad & Cárdenas, Roberto & Sáez, Doris, 2016. "Particle-filtering-based estimation of maximum available power state in Lithium-Ion batteries," Applied Energy, Elsevier, vol. 161(C), pages 349-363.
    13. Bai, Hongwei & Liu, Zhaoyang & Sun, Darren Delai & Chan, Siew Hwa, 2014. "Hierarchical 3D micro-/nano-V2O5 (vanadium pentoxide) spheres as cathode materials for high-energy and high-power lithium ion-batteries," Energy, Elsevier, vol. 76(C), pages 607-613.
    14. Oh, Ki-Yong & Epureanu, Bogdan I., 2016. "Characterization and modeling of the thermal mechanics of lithium-ion battery cells," Applied Energy, Elsevier, vol. 178(C), pages 633-646.
    15. Yang, Lin & Cai, Yishan & Yang, Yixin & Deng, Zhongwei, 2020. "Supervisory long-term prediction of state of available power for lithium-ion batteries in electric vehicles," Applied Energy, Elsevier, vol. 257(C).
    16. Yan, Dongxiang & Lu, Languang & Li, Zhe & Feng, Xuning & Ouyang, Minggao & Jiang, Fachao, 2016. "Durability comparison of four different types of high-power batteries in HEV and their degradation mechanism analysis," Applied Energy, Elsevier, vol. 179(C), pages 1123-1130.
    17. Liu, Guangming & Ouyang, Minggao & Lu, Languang & Li, Jianqiu & Hua, Jianfeng, 2015. "A highly accurate predictive-adaptive method for lithium-ion battery remaining discharge energy prediction in electric vehicle applications," Applied Energy, Elsevier, vol. 149(C), pages 297-314.
    18. Dai, Haifeng & Jiang, Bo & Hu, Xiaosong & Lin, Xianke & Wei, Xuezhe & Pecht, Michael, 2021. "Advanced battery management strategies for a sustainable energy future: Multilayer design concepts and research trends," Renewable and Sustainable Energy Reviews, Elsevier, vol. 138(C).
    19. Kang, Jianqiang & Yan, Fuwu & Zhang, Pei & Du, Changqing, 2014. "Comparison of comprehensive properties of Ni-MH (nickel-metal hydride) and Li-ion (lithium-ion) batteries in terms of energy efficiency," Energy, Elsevier, vol. 70(C), pages 618-625.
    20. He, HongWen & Zhang, YongZhi & Xiong, Rui & Wang, Chun, 2015. "A novel Gaussian model based battery state estimation approach: State-of-Energy," Applied Energy, Elsevier, vol. 151(C), pages 41-48.

    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:8:y:2015:i:8:p:7703-7728:d:53313. 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.