IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v12y2019i21p4036-d279576.html
   My bibliography  Save this article

State-Of-Charge Estimation for Lithium-Ion Battery Using Improved DUKF Based on State-Parameter Separation

Author

Listed:
  • Chuan-Xiang Yu

    (State Key Laboratory of Power Transmission Equipment & System Security and New Technology, Chong Qing University, Chongqing 400030, China)

  • Yan-Min Xie

    (State Key Laboratory of Power Transmission Equipment & System Security and New Technology, Chong Qing University, Chongqing 400030, China)

  • Zhao-Yu Sang

    (State Key Laboratory of Power Transmission Equipment & System Security and New Technology, Chong Qing University, Chongqing 400030, China)

  • Shi-Ya Yang

    (State Key Laboratory of Power Transmission Equipment & System Security and New Technology, Chong Qing University, Chongqing 400030, China)

  • Rui Huang

    (State Key Laboratory of Power Transmission Equipment & System Security and New Technology, Chong Qing University, Chongqing 400030, China)

Abstract

State-of-charge estimation and on-line model modification of lithium-ion batteries are more urgently required because of the great impact of the model accuracy on the algorithm performance. This study aims to propose an improved DUKF based on the state-parameter separation. Its characteristics include: (1) State-Of-Charge (SoC) is treated as the only state variable to eliminate the strong correlation between state and parameters. (2) Two filters are ranked to run the parameter modification only when the state estimation has converged. First, the double polarization (DP) model of battery is established, and the parameters of the model are identified at both the pulse discharge and long discharge recovery under Hybrid Pulse Power Characterization (HPPC) test. Second, the implementation of the proposed algorithm is described. Third, combined with the identification results, the study elaborates that it is unreliable to use the predicted voltage error of closed-loop algorithm as the criterion to measure the accuracy of the model, while the output voltage obtained by the open-loop model with dynamic parameters can reflect the real situation. Finally, comparative experiments are designed under HPPC and DST conditions. Results show that the proposed state-parameter separated IAUKF-UKF has higher SoC estimation accuracy and better stability than traditional DUKF.

Suggested Citation

  • Chuan-Xiang Yu & Yan-Min Xie & Zhao-Yu Sang & Shi-Ya Yang & Rui Huang, 2019. "State-Of-Charge Estimation for Lithium-Ion Battery Using Improved DUKF Based on State-Parameter Separation," Energies, MDPI, vol. 12(21), pages 1-19, October.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:21:p:4036-:d:279576
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/12/21/4036/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/12/21/4036/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Yigeng Huangfu & Jiani Xu & Dongdong Zhao & Yuntian Liu & Fei Gao, 2018. "A Novel Battery State of Charge Estimation Method Based on a Super-Twisting Sliding Mode Observer," Energies, MDPI, vol. 11(5), pages 1-21, May.
    2. Hannan, M.A. & Lipu, M.S.H. & Hussain, A. & Mohamed, A., 2017. "A review of lithium-ion battery state of charge estimation and management system in electric vehicle applications: Challenges and recommendations," Renewable and Sustainable Energy Reviews, Elsevier, vol. 78(C), pages 834-854.
    3. Xiong, Rui & Yu, Quanqing & Wang, Le Yi & Lin, Cheng, 2017. "A novel method to obtain the open circuit voltage for the state of charge of lithium ion batteries in electric vehicles by using H infinity filter," Applied Energy, Elsevier, vol. 207(C), pages 346-353.
    4. 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.
    5. Zhang, Cheng & Allafi, Walid & Dinh, Quang & Ascencio, Pedro & Marco, James, 2018. "Online estimation of battery equivalent circuit model parameters and state of charge using decoupled least squares technique," Energy, Elsevier, vol. 142(C), pages 678-688.
    6. Zhang, Xu & Wang, Yujie & Yang, Duo & Chen, Zonghai, 2016. "An on-line estimation of battery pack parameters and state-of-charge using dual filters based on pack model," Energy, Elsevier, vol. 115(P1), pages 219-229.
    7. 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.
    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. Bian, Chong & He, Huoliang & Yang, Shunkun, 2020. "Stacked bidirectional long short-term memory networks for state-of-charge estimation of lithium-ion batteries," Energy, Elsevier, vol. 191(C).
    2. Liu, Gengfeng & Zhang, Xiangwen & Liu, Zhiming, 2022. "State of health estimation of power batteries based on multi-feature fusion models using stacking algorithm," Energy, Elsevier, vol. 259(C).
    3. Gelareh Javid & Djaffar Ould Abdeslam & Michel Basset, 2021. "Adaptive Online State of Charge Estimation of EVs Lithium-Ion Batteries with Deep Recurrent Neural Networks," Energies, MDPI, vol. 14(3), pages 1-14, February.
    4. Guo, Yuanjun & Yang, Zhile & Liu, Kailong & Zhang, Yanhui & Feng, Wei, 2021. "A compact and optimized neural network approach for battery state-of-charge estimation of energy storage system," Energy, Elsevier, vol. 219(C).
    5. 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.

    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. 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.
    2. Shrivastava, Prashant & Soon, Tey Kok & Idris, Mohd Yamani Idna Bin & Mekhilef, Saad, 2019. "Overview of model-based online state-of-charge estimation using Kalman filter family for lithium-ion batteries," Renewable and Sustainable Energy Reviews, Elsevier, vol. 113(C), pages 1-1.
    3. Kuo Yang & Yugui Tang & Zhen Zhang, 2021. "Parameter Identification and State-of-Charge Estimation for Lithium-Ion Batteries Using Separated Time Scales and Extended Kalman Filter," Energies, MDPI, vol. 14(4), pages 1-15, February.
    4. Guo, Yuanjun & Yang, Zhile & Liu, Kailong & Zhang, Yanhui & Feng, Wei, 2021. "A compact and optimized neural network approach for battery state-of-charge estimation of energy storage system," Energy, Elsevier, vol. 219(C).
    5. Adrian Chmielewski & Jakub Możaryn & Piotr Piórkowski & Krzysztof Bogdziński, 2018. "Comparison of NARX and Dual Polarization Models for Estimation of the VRLA Battery Charging/Discharging Dynamics in Pulse Cycle," Energies, MDPI, vol. 11(11), pages 1-28, November.
    6. Song, Yuchen & Liu, Datong & Liao, Haitao & Peng, Yu, 2020. "A hybrid statistical data-driven method for on-line joint state estimation of lithium-ion batteries," Applied Energy, Elsevier, vol. 261(C).
    7. Yang, Fangfang & Li, Weihua & Li, Chuan & Miao, Qiang, 2019. "State-of-charge estimation of lithium-ion batteries based on gated recurrent neural network," Energy, Elsevier, vol. 175(C), pages 66-75.
    8. Fan, Kesen & Wan, Yiming & Wang, Zhuo & Jiang, Kai, 2023. "Time-efficient identification of lithium-ion battery temperature-dependent OCV-SOC curve using multi-output Gaussian process," Energy, Elsevier, vol. 268(C).
    9. 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.
    10. Zuo, Hongyan & Zhang, Bin & Huang, Zhonghua & Wei, Kexiang & Zhu, Hong & Tan, Jiqiu, 2022. "Effect analysis on SOC values of the power lithium manganate battery during discharging process and its intelligent estimation," Energy, Elsevier, vol. 238(PB).
    11. 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).
    12. Chen, Zheng & Zhao, Hongqian & Shu, Xing & Zhang, Yuanjian & Shen, Jiangwei & Liu, Yonggang, 2021. "Synthetic state of charge estimation for lithium-ion batteries based on long short-term memory network modeling and adaptive H-Infinity filter," Energy, Elsevier, vol. 228(C).
    13. 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.
    14. Benedikt Rzepka & Simon Bischof & Thomas Blank, 2021. "Implementing an Extended Kalman Filter for SoC Estimation of a Li-Ion Battery with Hysteresis: A Step-by-Step Guide," Energies, MDPI, vol. 14(13), pages 1-17, June.
    15. 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.
    16. Theodoros Kalogiannis & Md Sazzad Hosen & Mohsen Akbarzadeh Sokkeh & Shovon Goutam & Joris Jaguemont & Lu Jin & Geng Qiao & Maitane Berecibar & Joeri Van Mierlo, 2019. "Comparative Study on Parameter Identification Methods for Dual-Polarization Lithium-Ion Equivalent Circuit Model," Energies, MDPI, vol. 12(21), pages 1-35, October.
    17. Wenhui Zheng & Bizhong Xia & Wei Wang & Yongzhi Lai & Mingwang Wang & Huawen Wang, 2019. "State of Charge Estimation for Power Lithium-Ion Battery Using a Fuzzy Logic Sliding Mode Observer," Energies, MDPI, vol. 12(13), pages 1-14, June.
    18. Hu, Xiaosong & Jiang, Haifu & Feng, Fei & Liu, Bo, 2020. "An enhanced multi-state estimation hierarchy for advanced lithium-ion battery management," Applied Energy, Elsevier, vol. 257(C).
    19. Maheshwari, A. & Nageswari, S., 2022. "Real-time state of charge estimation for electric vehicle power batteries using optimized filter," Energy, Elsevier, vol. 254(PB).
    20. Tian, Yong & Lai, Rucong & Li, Xiaoyu & Xiang, Lijuan & Tian, Jindong, 2020. "A combined method for state-of-charge estimation for lithium-ion batteries using a long short-term memory network and an adaptive cubature Kalman filter," Applied Energy, Elsevier, vol. 265(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:12:y:2019:i:21:p:4036-:d:279576. 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.