IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v10y2017i3p390-d93505.html
   My bibliography  Save this article

Online Reliable Peak Charge/Discharge Power Estimation of Series-Connected Lithium-Ion Battery Packs

Author

Listed:
  • Bo Jiang

    (National Fuel Cell Vehicle & Powertrain System Research & Engineering Center, No. 4800, Caoan Road, Shanghai 201804, China
    School of Automotive Studies, Tongji University, No. 4800, Caoan Road, Shanghai 201804, China)

  • Haifeng Dai

    (National Fuel Cell Vehicle & Powertrain System Research & Engineering Center, No. 4800, Caoan Road, Shanghai 201804, China
    School of Automotive Studies, Tongji University, No. 4800, Caoan Road, Shanghai 201804, China)

  • Xuezhe Wei

    (National Fuel Cell Vehicle & Powertrain System Research & Engineering Center, No. 4800, Caoan Road, Shanghai 201804, China
    School of Automotive Studies, Tongji University, No. 4800, Caoan Road, Shanghai 201804, China)

  • Letao Zhu

    (National Fuel Cell Vehicle & Powertrain System Research & Engineering Center, No. 4800, Caoan Road, Shanghai 201804, China
    School of Automotive Studies, Tongji University, No. 4800, Caoan Road, Shanghai 201804, China)

  • Zechang Sun

    (National Fuel Cell Vehicle & Powertrain System Research & Engineering Center, No. 4800, Caoan Road, Shanghai 201804, China
    School of Automotive Studies, Tongji University, No. 4800, Caoan Road, Shanghai 201804, China)

Abstract

The accurate peak power estimation of a battery pack is essential to the power-train control of electric vehicles (EVs). It helps to evaluate the maximum charge and discharge capability of the battery system, and thus to optimally control the power-train system to meet the requirement of acceleration, gradient climbing and regenerative braking while achieving a high energy efficiency. A novel online peak power estimation method for series-connected lithium-ion battery packs is proposed, which considers the influence of cell difference on the peak power of the battery packs. A new parameter identification algorithm based on adaptive ratio vectors is designed to online identify the parameters of each individual cell in a series-connected battery pack. The ratio vectors reflecting cell difference are deduced strictly based on the analysis of battery characteristics. Based on the online parameter identification, the peak power estimation considering cell difference is further developed. Some validation experiments in different battery aging conditions and with different current profiles have been implemented to verify the proposed method. The results indicate that the ratio vector-based identification algorithm can achieve the same accuracy as the repetitive RLS (recursive least squares) based identification while evidently reducing the computation cost, and the proposed peak power estimation method is more effective and reliable for series-connected battery packs due to the consideration of cell difference.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:10:y:2017:i:3:p:390-:d:93505
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/10/3/390/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/10/3/390/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Castaings, Ali & Lhomme, Walter & Trigui, Rochdi & Bouscayrol, Alain, 2016. "Comparison of energy management strategies of a battery/supercapacitors system for electric vehicle under real-time constraints," Applied Energy, Elsevier, vol. 163(C), pages 190-200.
    2. Dai, Haifeng & Wei, Xuezhe & Sun, Zechang & Wang, Jiayuan & Gu, Weijun, 2012. "Online cell SOC estimation of Li-ion battery packs using a dual time-scale Kalman filtering for EV applications," Applied Energy, Elsevier, vol. 95(C), pages 227-237.
    3. 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.
    4. Sun, Fengchun & Xiong, Rui & He, Hongwen & Li, Weiqing & Aussems, Johan Eric Emmanuel, 2012. "Model-based dynamic multi-parameter method for peak power estimation of lithium–ion batteries," Applied Energy, Elsevier, vol. 96(C), pages 378-386.
    5. 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.
    6. 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.
    7. Lim, KaiChin & Bastawrous, Hany Ayad & Duong, Van-Huan & See, Khay Wai & Zhang, Peng & Dou, Shi Xue, 2016. "Fading Kalman filter-based real-time state of charge estimation in LiFePO4 battery-powered electric vehicles," Applied Energy, Elsevier, vol. 169(C), pages 40-48.
    8. Wei, Zhongbao & Lim, Tuti Mariana & Skyllas-Kazacos, Maria & Wai, Nyunt & Tseng, King Jet, 2016. "Online state of charge and model parameter co-estimation based on a novel multi-timescale estimator for vanadium redox flow battery," Applied Energy, Elsevier, vol. 172(C), pages 169-179.
    9. Sun, Fengchun & Xiong, Rui & He, Hongwen, 2016. "A systematic state-of-charge estimation framework for multi-cell battery pack in electric vehicles using bias correction technique," Applied Energy, Elsevier, vol. 162(C), pages 1399-1409.
    10. 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. Zhu, Jiangong & Knapp, Michael & Darma, Mariyam Susana Dewi & Fang, Qiaohua & Wang, Xueyuan & Dai, Haifeng & Wei, Xuezhe & Ehrenberg, Helmut, 2019. "An improved electro-thermal battery model complemented by current dependent parameters for vehicular low temperature application," Applied Energy, Elsevier, vol. 248(C), pages 149-161.
    2. Tae-Won Noh & Junghoon Ahn & Byoung Kuk Lee, 2022. "Online Cell Screening Algorithm for Maximum Peak Current Estimation of a Lithium-Ion Battery Pack for Electric Vehicles," Energies, MDPI, vol. 15(4), pages 1-14, February.
    3. Rui Xiong & Hailong Li & Xuan Zhou, 2017. "Advanced Energy Storage Technologies and Their Applications (AESA2017)," Energies, MDPI, vol. 10(9), pages 1-3, September.
    4. Abraham Alem Kebede & Md Sazzad Hosen & Theodoros Kalogiannis & Henok Ayele Behabtu & Towfik Jemal & Joeri Van Mierlo & Thierry Coosemans & Maitane Berecibar, 2022. "Model Development for State-of-Power Estimation of Large-Capacity Nickel-Manganese-Cobalt Oxide-Based Lithium-Ion Cell Validated Using a Real-Life Profile," Energies, MDPI, vol. 15(18), pages 1-15, September.

    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. Dai, Haifeng & Xu, Tianjiao & Zhu, Letao & Wei, Xuezhe & Sun, Zechang, 2016. "Adaptive model parameter identification for large capacity Li-ion batteries on separated time scales," Applied Energy, Elsevier, vol. 184(C), pages 119-131.
    2. 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.
    3. 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.
    4. 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.
    5. 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.
    6. Li, Yanwen & Wang, Chao & Gong, Jinfeng, 2017. "A multi-model probability SOC fusion estimation approach using an improved adaptive unscented Kalman filter technique," Energy, Elsevier, vol. 141(C), pages 1402-1415.
    7. Zheng, Yuejiu & Ouyang, Minggao & Lu, Languang & Li, Jianqiu & Han, Xuebing & Xu, Liangfei & Ma, Hongbin & Dollmeyer, Thomas A. & Freyermuth, Vincent, 2013. "Cell state-of-charge inconsistency estimation for LiFePO4 battery pack in hybrid electric vehicles using mean-difference model," Applied Energy, Elsevier, vol. 111(C), pages 571-580.
    8. 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.
    9. Yang, Jufeng & Xia, Bing & Huang, Wenxin & Fu, Yuhong & Mi, Chris, 2018. "Online state-of-health estimation for lithium-ion batteries using constant-voltage charging current analysis," Applied Energy, Elsevier, vol. 212(C), pages 1589-1600.
    10. 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.
    11. Esfandyari, M.J. & Esfahanian, V. & Hairi Yazdi, M.R. & Nehzati, H. & Shekoofa, O., 2019. "A new approach to consider the influence of aging state on Lithium-ion battery state of power estimation for hybrid electric vehicle," Energy, Elsevier, vol. 176(C), pages 505-520.
    12. 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.
    13. Bai, Guangxing & Wang, Pingfeng & Hu, Chao & Pecht, Michael, 2014. "A generic model-free approach for lithium-ion battery health management," Applied Energy, Elsevier, vol. 135(C), pages 247-260.
    14. 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).
    15. 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.
    16. 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.
    17. Kong, Xiangdong & Zheng, Yuejiu & Ouyang, Minggao & Li, Xiangjun & Lu, Languang & Li, Jianqiu & Zhang, Zhendong, 2017. "Signal synchronization for massive data storage in modular battery management system with controller area network," Applied Energy, Elsevier, vol. 197(C), pages 52-62.
    18. Shun Xiang & Guangdi Hu & Ruisen Huang & Feng Guo & Pengkai Zhou, 2018. "Lithium-Ion Battery Online Rapid State-of-Power Estimation under Multiple Constraints," Energies, MDPI, vol. 11(2), pages 1-20, January.
    19. Wei, Zhongbao & Meng, Shujuan & Xiong, Binyu & Ji, Dongxu & Tseng, King Jet, 2016. "Enhanced online model identification and state of charge estimation for lithium-ion battery with a FBCRLS based observer," Applied Energy, Elsevier, vol. 181(C), pages 332-341.
    20. Allafi, Walid & Uddin, Kotub & Zhang, Cheng & Mazuir Raja Ahsan Sha, Raja & Marco, James, 2017. "On-line scheme for parameter estimation of nonlinear lithium ion battery equivalent circuit models using the simplified refined instrumental variable method for a modified Wiener continuous-time model," Applied Energy, Elsevier, vol. 204(C), pages 497-508.

    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:10:y:2017:i:3:p:390-:d:93505. 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.