IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v176y2019icp874-887.html
   My bibliography  Save this article

Frequency and time domain modelling and online state of charge monitoring for ultracapacitors

Author

Listed:
  • Tian, Jinpeng
  • Xiong, Rui
  • Shen, Weixiang
  • Wang, Ju

Abstract

Ultracapacitors are common power sources for new energy vehicles with the advantage of providing high current and power. Modelling and state of charge (SOC) estimation of ultracapacitors are still a research focus. In this study, a fractional order model (FOM), which is derived from the analysis of electrochemical impedance spectroscopy, is established for ultracapacitors and validated in frequency and time domains. After analyzing characteristics of an ultracapacitor and its open circuit voltage, the established FOM is combined with a fractional order Unscented Kalman filter to realize SOC estimation for ultracapacitors. Results show that the proposed method can estimate SOC more accurately than traditional SOC methods. Furthermore, an online joint estimation method for SOC and FOM parameters is proposed and validated by a hardware-in-the-loop platform, indicating that the FOM can be used for onboard applications.

Suggested Citation

  • Tian, Jinpeng & Xiong, Rui & Shen, Weixiang & Wang, Ju, 2019. "Frequency and time domain modelling and online state of charge monitoring for ultracapacitors," Energy, Elsevier, vol. 176(C), pages 874-887.
  • Handle: RePEc:eee:energy:v:176:y:2019:i:c:p:874-887
    DOI: 10.1016/j.energy.2019.04.034
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544219306577
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.energy.2019.04.034?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. Wang, Ju & Xiong, Rui & Li, Linlin & Fang, Yu, 2018. "A comparative analysis and validation for double-filters-based state of charge estimators using battery-in-the-loop approach," Applied Energy, Elsevier, vol. 229(C), pages 648-659.
    2. Li, Yanwen & Wang, Chao & Gong, Jinfeng, 2016. "A combination Kalman filter approach for State of Charge estimation of lithium-ion battery considering model uncertainty," Energy, Elsevier, vol. 109(C), pages 933-946.
    3. Wang, Chun & He, Hongwen & Zhang, Yongzhi & Mu, Hao, 2017. "A comparative study on the applicability of ultracapacitor models for electric vehicles under different temperatures," Applied Energy, Elsevier, vol. 196(C), pages 268-278.
    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. Dong, Cheng & Yang, Haowei & Yang, Zhe & Cao, Wenjian & Miao, Zhengwei & Ren, Lei & Guo, Yacong & Huang, Chenguang & Tu, Huan & Wei, Yanpeng, 2024. "Protective performance of shear thickening gel modified epoxy sealant on lithium-ion batteries under mechanical abuse," Energy, Elsevier, vol. 296(C).
    2. Wang, Chun & Yang, Ruixin & Yu, Quanqing, 2019. "Wavelet transform based energy management strategies for plug-in hybrid electric vehicles considering temperature uncertainty," Applied Energy, Elsevier, vol. 256(C).

    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. Shuaishuai Zhang & Youhong Wan & Jie Ding & Yangyang Da, 2021. "State of Charge (SOC) Estimation Based on Extended Exponential Weighted Moving Average H ∞ Filtering," Energies, MDPI, vol. 14(6), pages 1-15, March.
    2. Xiong, Rui & Duan, Yanzhou & Cao, Jiayi & Yu, Quanqing, 2018. "Battery and ultracapacitor in-the-loop approach to validate a real-time power management method for an all-climate electric vehicle," Applied Energy, Elsevier, vol. 217(C), pages 153-165.
    3. Wei Zhang & Jixin Wang & Shaofeng Du & Hongfeng Ma & Wenjun Zhao & Haojie Li, 2019. "Energy Management Strategies for Hybrid Construction Machinery: Evolution, Classification, Comparison and Future Trends," Energies, MDPI, vol. 12(10), pages 1-26, May.
    4. Yonghong Xu & Cheng Li & Xu Wang & Hongguang Zhang & Fubin Yang & Lili Ma & Yan Wang, 2022. "Joint Estimation Method with Multi-Innovation Unscented Kalman Filter Based on Fractional-Order Model for State of Charge and State of Health Estimation," Sustainability, MDPI, vol. 14(23), pages 1-25, November.
    5. Sun, Daoming & Yu, Xiaoli & Wang, Chongming & Zhang, Cheng & Huang, Rui & Zhou, Quan & Amietszajew, Taz & Bhagat, Rohit, 2021. "State of charge estimation for lithium-ion battery based on an Intelligent Adaptive Extended Kalman Filter with improved noise estimator," Energy, Elsevier, vol. 214(C).
    6. Qiao Zhu & Neng Xiong & Ming-Liang Yang & Rui-Sen Huang & Guang-Di Hu, 2017. "State of Charge Estimation for Lithium-Ion Battery Based on Nonlinear Observer: An H ∞ Method," Energies, MDPI, vol. 10(5), pages 1-19, May.
    7. Bizhong Xia & Wenhui Zheng & Ruifeng Zhang & Zizhou Lao & Zhen Sun, 2017. "A Novel Observer for Lithium-Ion Battery State of Charge Estimation in Electric Vehicles Based on a Second-Order Equivalent Circuit Model," Energies, MDPI, vol. 10(8), pages 1-20, August.
    8. Jiang, Yunfeng & Xia, Bing & Zhao, Xin & Nguyen, Truong & Mi, Chris & de Callafon, Raymond A., 2017. "Data-based fractional differential models for non-linear dynamic modeling of a lithium-ion battery," Energy, Elsevier, vol. 135(C), pages 171-181.
    9. Yao, Lei & Fang, Zhanpeng & Xiao, Yanqiu & Hou, Junjian & Fu, Zhijun, 2021. "An Intelligent Fault Diagnosis Method for Lithium Battery Systems Based on Grid Search Support Vector Machine," Energy, Elsevier, vol. 214(C).
    10. Guo, Feng & Hu, Guangdi & Xiang, Shun & Zhou, Pengkai & Hong, Ru & Xiong, Neng, 2019. "A multi-scale parameter adaptive method for state of charge and parameter estimation of lithium-ion batteries using dual Kalman filters," Energy, Elsevier, vol. 178(C), pages 79-88.
    11. Zhang, Shuzhi & Zhang, Chen & Jiang, Shiyong & Zhang, Xiongwen, 2022. "A comparative study of different adaptive extended/unscented Kalman filters for lithium-ion battery state-of-charge estimation," Energy, Elsevier, vol. 246(C).
    12. Renxin, Xiao & Yi, Yang & Xianguang, Jia & Nan, Pan, 2023. "Collaborative estimations of state of energy and maximum available energy of lithium-ion batteries with optimized time windows considering instantaneous energy efficiencies," Energy, Elsevier, vol. 274(C).
    13. Li, Xiaoyu & Wang, Zhenpo & Zhang, Lei, 2019. "Co-estimation of capacity and state-of-charge for lithium-ion batteries in electric vehicles," Energy, Elsevier, vol. 174(C), pages 33-44.
    14. Pan, Haihong & Lü, Zhiqiang & Lin, Weilong & Li, Junzi & Chen, Lin, 2017. "State of charge estimation of lithium-ion batteries using a grey extended Kalman filter and a novel open-circuit voltage model," Energy, Elsevier, vol. 138(C), pages 764-775.
    15. Liang Zhang & Shunli Wang & Daniel-Ioan Stroe & Chuanyun Zou & Carlos Fernandez & Chunmei Yu, 2020. "An Accurate Time Constant Parameter Determination Method for the Varying Condition Equivalent Circuit Model of Lithium Batteries," Energies, MDPI, vol. 13(8), pages 1-12, April.
    16. Bizhong Xia & Zizhou Lao & Ruifeng Zhang & Yong Tian & Guanghao Chen & Zhen Sun & Wei Wang & Wei Sun & Yongzhi Lai & Mingwang Wang & Huawen Wang, 2017. "Online Parameter Identification and State of Charge Estimation of Lithium-Ion Batteries Based on Forgetting Factor Recursive Least Squares and Nonlinear Kalman Filter," Energies, MDPI, vol. 11(1), pages 1-23, December.
    17. Ouyang, Nan & Zhang, Wencan & Yin, Xiuxing & Li, Xingyao & Xie, Yi & He, Hancheng & Long, Zhuoru, 2023. "A data-driven method for predicting thermal runaway propagation of battery modules considering uncertain conditions," Energy, Elsevier, vol. 273(C).
    18. Ma, Mina & Wang, Yu & Duan, Qiangling & Wu, Tangqin & Sun, Jinhua & Wang, Qingsong, 2018. "Fault detection of the connection of lithium-ion power batteries in series for electric vehicles based on statistical analysis," Energy, Elsevier, vol. 164(C), pages 745-756.
    19. Muhammad Umair Ali & Amad Zafar & Sarvar Hussain Nengroo & Sadam Hussain & Muhammad Junaid Alvi & Hee-Je Kim, 2019. "Towards a Smarter Battery Management System for Electric Vehicle Applications: A Critical Review of Lithium-Ion Battery State of Charge Estimation," Energies, MDPI, vol. 12(3), pages 1-33, January.
    20. Zhao, Xiaowei & Cai, Yishan & Yang, Lin & Deng, Zhongwei & Qiang, Jiaxi, 2017. "State of charge estimation based on a new dual-polarization-resistance model for electric vehicles," Energy, Elsevier, vol. 135(C), pages 40-52.

    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:energy:v:176:y:2019:i:c:p:874-887. 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.journals.elsevier.com/energy .

    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.