IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v135y2014icp81-87.html
   My bibliography  Save this article

A method for joint estimation of state-of-charge and available energy of LiFePO4 batteries

Author

Listed:
  • Wang, Yujie
  • Zhang, Chenbin
  • Chen, Zonghai

Abstract

The state-of-charge (SOC) is a critical index in battery management system (BMS) for electric vehicles (EVs). However in the energy storage systems, the available energy also acts as a significant role. Through the estimating result of state-of-energy (SOE), we can further estimate how long the battery is going to last if we apply a low power demand, a high power demand, or even a dynamic power demand. Unlike the SOC, the SOE is not only the integral of current but also the integral of voltage which include the nonlinearity of Li-ion batteries. Since there are accumulated errors caused by current or voltage measurement noise, a joint estimator based on particle filter is proposed for the estimation of both SOC and SOE. Validation experiments are carried out based on IFP1865140-type batteries under both constant and dynamic current conditions. To further verify the robustness of the proposed method, experiments are performed under dynamic temperatures. The experiment results have verified that accurate and robust SOC and SOE estimation results can be obtained by the proposed method.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:appene:v:135:y:2014:i:c:p:81-87
    DOI: 10.1016/j.apenergy.2014.08.081
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0306261914008927
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.apenergy.2014.08.081?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. He, Yao & Liu, XingTao & Zhang, ChenBin & Chen, ZongHai, 2013. "A new model for State-of-Charge (SOC) estimation for high-power Li-ion batteries," Applied Energy, Elsevier, vol. 101(C), pages 808-814.
    2. 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.
    3. 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.
    4. Liu, Xingtao & Chen, Zonghai & Zhang, Chenbin & Wu, Ji, 2014. "A novel temperature-compensated model for power Li-ion batteries with dual-particle-filter state of charge estimation," Applied Energy, Elsevier, vol. 123(C), pages 263-272.
    5. Ng, Kong Soon & Moo, Chin-Sien & Chen, Yi-Ping & Hsieh, Yao-Ching, 2009. "Enhanced coulomb counting method for estimating state-of-charge and state-of-health of lithium-ion batteries," Applied Energy, Elsevier, vol. 86(9), pages 1506-1511, September.
    6. Xing, Yinjiao & He, Wei & Pecht, Michael & Tsui, Kwok Leung, 2014. "State of charge estimation of lithium-ion batteries using the open-circuit voltage at various ambient temperatures," Applied Energy, Elsevier, vol. 113(C), pages 106-115.
    7. Hu, Chao & Youn, Byeng D. & Chung, Jaesik, 2012. "A multiscale framework with extended Kalman filter for lithium-ion battery SOC and capacity estimation," Applied Energy, Elsevier, vol. 92(C), pages 694-704.
    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. Wang, Yujie & Zhang, Chenbin & Chen, Zonghai, 2015. "A method for state-of-charge estimation of Li-ion batteries based on multi-model switching strategy," Applied Energy, Elsevier, vol. 137(C), pages 427-434.
    2. 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.
    3. Avvari, G.V. & Pattipati, B. & Balasingam, B. & Pattipati, K.R. & Bar-Shalom, Y., 2015. "Experimental set-up and procedures to test and validate battery fuel gauge algorithms," Applied Energy, Elsevier, vol. 160(C), pages 404-418.
    4. Xiangyu Cui & Zhu Jing & Maji Luo & Yazhou Guo & Huimin Qiao, 2018. "A New Method for State of Charge Estimation of Lithium-Ion Batteries Using Square Root Cubature Kalman Filter," Energies, MDPI, vol. 11(1), pages 1-21, January.
    5. Wang, Yujie & Tian, Jiaqiang & Sun, Zhendong & Wang, Li & Xu, Ruilong & Li, Mince & Chen, Zonghai, 2020. "A comprehensive review of battery modeling and state estimation approaches for advanced battery management systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 131(C).
    6. Wei, Jingwen & Dong, Guangzhong & Chen, Zonghai & Kang, Yu, 2017. "System state estimation and optimal energy control framework for multicell lithium-ion battery system," Applied Energy, Elsevier, vol. 187(C), pages 37-49.
    7. Yong Tian & Bizhong Xia & Mingwang Wang & Wei Sun & Zhihui Xu, 2014. "Comparison Study on Two Model-Based Adaptive Algorithms for SOC Estimation of Lithium-Ion Batteries in Electric Vehicles," Energies, MDPI, vol. 7(12), pages 1-19, December.
    8. Yang, Fangfang & Xing, Yinjiao & Wang, Dong & Tsui, Kwok-Leung, 2016. "A comparative study of three model-based algorithms for estimating state-of-charge of lithium-ion batteries under a new combined dynamic loading profile," Applied Energy, Elsevier, vol. 164(C), pages 387-399.
    9. Wang, Yujie & Zhang, Chenbin & Chen, Zonghai, 2017. "On-line battery state-of-charge estimation based on an integrated estimator," Applied Energy, Elsevier, vol. 185(P2), pages 2026-2032.
    10. Ng, Selina S.Y. & Xing, Yinjiao & Tsui, Kwok L., 2014. "A naive Bayes model for robust remaining useful life prediction of lithium-ion battery," Applied Energy, Elsevier, vol. 118(C), pages 114-123.
    11. 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.
    12. Cuma, Mehmet Ugras & Koroglu, Tahsin, 2015. "A comprehensive review on estimation strategies used in hybrid and battery electric vehicles," Renewable and Sustainable Energy Reviews, Elsevier, vol. 42(C), pages 517-531.
    13. Linhui Zhao & Guohuang Ji & Zhiyuan Liu, 2017. "Design and Experiment of Nonlinear Observer with Adaptive Gains for Battery State of Charge Estimation," Energies, MDPI, vol. 10(12), pages 1-20, December.
    14. Shifei Yuan & Hongjie Wu & Xuerui Ma & Chengliang Yin, 2015. "Stability Analysis for Li-Ion Battery Model Parameters and State of Charge Estimation by Measurement Uncertainty Consideration," Energies, MDPI, vol. 8(8), pages 1-23, July.
    15. Liu, Xingtao & Chen, Zonghai & Zhang, Chenbin & Wu, Ji, 2014. "A novel temperature-compensated model for power Li-ion batteries with dual-particle-filter state of charge estimation," Applied Energy, Elsevier, vol. 123(C), pages 263-272.
    16. Dong, Guangzhong & Wei, Jingwen & Zhang, Chenbin & Chen, Zonghai, 2016. "Online state of charge estimation and open circuit voltage hysteresis modeling of LiFePO4 battery using invariant imbedding method," Applied Energy, Elsevier, vol. 162(C), pages 163-171.
    17. Ingvild B. Espedal & Asanthi Jinasena & Odne S. Burheim & Jacob J. Lamb, 2021. "Current Trends for State-of-Charge (SoC) Estimation in Lithium-Ion Battery Electric Vehicles," Energies, MDPI, vol. 14(11), pages 1-24, June.
    18. Shichun Yang & Cheng Deng & Yulong Zhang & Yongling He, 2017. "State of Charge Estimation for Lithium-Ion Battery with a Temperature-Compensated Model," Energies, MDPI, vol. 10(10), pages 1-14, October.
    19. Yong Tian & Chaoren Chen & Bizhong Xia & Wei Sun & Zhihui Xu & Weiwei Zheng, 2014. "An Adaptive Gain Nonlinear Observer for State of Charge Estimation of Lithium-Ion Batteries in Electric Vehicles," Energies, MDPI, vol. 7(9), pages 1-18, September.
    20. Zheng, Fangdan & Xing, Yinjiao & Jiang, Jiuchun & Sun, Bingxiang & Kim, Jonghoon & Pecht, Michael, 2016. "Influence of different open circuit voltage tests on state of charge online estimation for lithium-ion batteries," Applied Energy, Elsevier, vol. 183(C), pages 513-525.

    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:eee:appene:v:135:y:2014:i:c:p:81-87. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/405891/description#description .

    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.