IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v15y2022i3p963-d736744.html
   My bibliography  Save this article

Extended Rauch–Tung–Striebel Smoother for the State of Charge Estimation of Lithium-Ion Batteries Based on an Enhanced Circuit Model

Author

Listed:
  • Yinfeng Jiang

    (School of Mechatronic Engineering and Automation, Shanghai University, Shanghai 200444, China)

  • Wenxiang Song

    (School of Mechatronic Engineering and Automation, Shanghai University, Shanghai 200444, China)

  • Hao Zhu

    (HNU College of Mechanical and Vehicle Engineering, Hunan University, Changsha 410082, China)

  • Yun Zhu

    (Hunan Hong Xun Yi’An New Energy and Technology, Co., Ltd., Zhuzhou 412007, China)

  • Yongzhi Du

    (Hunan Hong Xun Yi’An New Energy and Technology, Co., Ltd., Zhuzhou 412007, China)

  • Huichun Yin

    (Hunan Hong Xun Yi’An New Energy and Technology, Co., Ltd., Zhuzhou 412007, China)

Abstract

The state of charge (SOC) of a lithium battery system is critical since it indicates the remaining operating hours, full charge time, and peak power of the battery. This paper recommends an extended Rauch–Tung–Striebel smoother (ERTSS) for estimating SOC. It is implemented based on an improved equivalent circuit model with hysteresis voltage. The smoothing step of ERTSS will reduce the estimation error further. Additionally, the genetic algorithm (GA) is employed for searching the optimal ERTSS’s smoothing time interval. Various dynamic cell tests are conducted to verify the model’s accuracy and error estimation deviation. The test results demonstrate that ERTSS’s SOC estimation error is limited to 4 % with an initial error between −25 ∘ C and 45 ∘ C and that the root mean square error (RMSE) of ERTSS’s SOC estimation is approximately 5% lower than that of extended Kalman filter (EKF). The ERTSS improves the SOC estimation accuracy at all operating temperatures of batteries.

Suggested Citation

  • Yinfeng Jiang & Wenxiang Song & Hao Zhu & Yun Zhu & Yongzhi Du & Huichun Yin, 2022. "Extended Rauch–Tung–Striebel Smoother for the State of Charge Estimation of Lithium-Ion Batteries Based on an Enhanced Circuit Model," Energies, MDPI, vol. 15(3), pages 1-17, January.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:3:p:963-:d:736744
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/15/3/963/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/15/3/963/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Sewon Park & Seo Yeong Jeong & Tae Kyung Lee & Min Woo Park & Hyeong Yong Lim & Jaekyung Sung & Jaephil Cho & Sang Kyu Kwak & Sung You Hong & Nam-Soon Choi, 2021. "Replacing conventional battery electrolyte additives with dioxolone derivatives for high-energy-density lithium-ion batteries," Nature Communications, Nature, vol. 12(1), pages 1-12, December.
    2. Quanqing Yu & Changjiang Wan & Junfu Li & Lixin E & Xin Zhang & Yonghe Huang & Tao Liu, 2021. "An Open Circuit Voltage Model Fusion Method for State of Charge Estimation of Lithium-Ion Batteries," Energies, MDPI, vol. 14(7), pages 1-22, March.
    3. Lin Su & Guangxu Zhou & Dairong Hu & Yuan Liu & Yunhai Zhu, 2021. "Research on the State of Charge of Lithium-Ion Battery Based on the Fractional Order Model," Energies, MDPI, vol. 14(19), pages 1-23, October.
    4. Longxing Wu & Kai Liu & Hui Pang & Jiamin Jin, 2021. "Online SOC Estimation Based on Simplified Electrochemical Model for Lithium-Ion Batteries Considering Current Bias," Energies, MDPI, vol. 14(17), pages 1-12, August.
    5. 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.
    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. Suwei Zhai & Wenyun Li & Cheng Wang & Yundi Chu, 2022. "A Novel Data-Driven Estimation Method for State-of-Charge Estimation of Li-Ion Batteries," Energies, MDPI, vol. 15(9), pages 1-17, 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. Stefano Leonori & Luca Baldini & Antonello Rizzi & Fabio Massimo Frattale Mascioli, 2021. "A Physically Inspired Equivalent Neural Network Circuit Model for SoC Estimation of Electrochemical Cells," Energies, MDPI, vol. 14(21), pages 1-29, November.
    2. Takyi-Aninakwa, Paul & Wang, Shunli & Zhang, Hongying & Yang, Xiao & Fernandez, Carlos, 2023. "A hybrid probabilistic correction model for the state of charge estimation of lithium-ion batteries considering dynamic currents and temperatures," Energy, Elsevier, vol. 273(C).
    3. Prarthana Pillai & Sneha Sundaresan & Pradeep Kumar & Krishna R. Pattipati & Balakumar Balasingam, 2022. "Open-Circuit Voltage Models for Battery Management Systems: A Review," Energies, MDPI, vol. 15(18), pages 1-25, September.
    4. Gul, Eid & Baldinelli, Giorgio & Bartocci, Pietro & Bianchi, Francesco & Domenghini, Piergiovanni & Cotana, Franco & Wang, Jinwen, 2022. "A techno-economic analysis of a solar PV and DC battery storage system for a community energy sharing," Energy, Elsevier, vol. 244(PB).
    5. Xin Lai & Ming Yuan & Xiaopeng Tang & Yi Yao & Jiahui Weng & Furong Gao & Weiguo Ma & Yuejiu Zheng, 2022. "Co-Estimation of State-of-Charge and State-of-Health for Lithium-Ion Batteries Considering Temperature and Ageing," Energies, MDPI, vol. 15(19), pages 1-20, October.
    6. Gu, Xinyu & See, K.W. & Li, Penghua & Shan, Kangheng & Wang, Yunpeng & Zhao, Liang & Lim, Kai Chin & Zhang, Neng, 2023. "A novel state-of-health estimation for the lithium-ion battery using a convolutional neural network and transformer model," Energy, Elsevier, vol. 262(PB).
    7. Amiri, Mahshid N. & Håkansson, Anne & Burheim, Odne S. & Lamb, Jacob J., 2024. "Lithium-ion battery digitalization: Combining physics-based models and machine learning," Renewable and Sustainable Energy Reviews, Elsevier, vol. 200(C).
    8. Lai, Rucong & Wang, Jie & Tian, Yong & Tian, Jindong, 2024. "FedCBE: A federated-learning-based collaborative battery estimation system with non-IID data," Applied Energy, Elsevier, vol. 368(C).
    9. Saleh Mohammed Shahriar & Erphan A. Bhuiyan & Md. Nahiduzzaman & Mominul Ahsan & Julfikar Haider, 2022. "State of Charge Estimation for Electric Vehicle Battery Management Systems Using the Hybrid Recurrent Learning Approach with Explainable Artificial Intelligence," Energies, MDPI, vol. 15(21), pages 1-26, October.
    10. Tadeusz Białoń & Roman Niestrój & Wojciech Korski, 2023. "PSO-Based Identification of the Li-Ion Battery Cell Parameters," Energies, MDPI, vol. 16(10), pages 1-22, May.
    11. Luo, Guiling & Li, Xiaowei & Chen, Linlin & Gu, Jun & Huang, Yuhong & Sun, Jing & Liu, Haiyan & Chao, Yanhong & Zhu, Wenshuai & Liu, Zhichang, 2023. "Electrochemical recovery lithium from brine via taming surface wettability of regeneration spent batteries cathode materials," Applied Energy, Elsevier, vol. 337(C).
    12. Wu, Ji & Fang, Leichao & Dong, Guangzhong & Lin, Mingqiang, 2023. "State of health estimation of lithium-ion battery with improved radial basis function neural network," Energy, Elsevier, vol. 262(PB).
    13. Yu Feng & Xiaochun Lu, 2021. "Construction Planning and Operation of Battery Swapping Stations for Electric Vehicles: A Literature Review," Energies, MDPI, vol. 14(24), pages 1-19, December.
    14. E, Jiaqiang & Zhang, Bin & Zeng, Yan & Wen, Ming & Wei, Kexiang & Huang, Zhonghua & Chen, Jingwei & Zhu, Hao & Deng, Yuanwang, 2022. "Effects analysis on active equalization control of lithium-ion batteries based on intelligent estimation of the state-of-charge," Energy, Elsevier, vol. 238(PB).
    15. Korkmaz, Mehmet, 2024. "A novel approach for improving the performance of deep learning-based state of charge estimation of lithium-ion batteries: Choosy SoC Estimator (ChoSoCE)," Energy, Elsevier, vol. 294(C).
    16. Ivan Radaš & Nicole Pilat & Daren Gnjatović & Viktor Šunde & Željko Ban, 2022. "Estimating the State of Charge of Lithium-Ion Batteries Based on the Transfer Function of the Voltage Response to the Current Pulse," Energies, MDPI, vol. 15(18), pages 1-14, September.
    17. Buchicchio, Emanuele & De Angelis, Alessio & Santoni, Francesco & Carbone, Paolo & Bianconi, Francesco & Smeraldi, Fabrizio, 2023. "Battery SOC estimation from EIS data based on machine learning and equivalent circuit model," Energy, Elsevier, vol. 283(C).
    18. Shi, Haotian & Wang, Shunli & Fernandez, Carlos & Yu, Chunmei & Xu, Wenhua & Dablu, Bobobee Etse & Wang, Liping, 2022. "Improved multi-time scale lumped thermoelectric coupling modeling and parameter dispersion evaluation of lithium-ion batteries," Applied Energy, Elsevier, vol. 324(C).
    19. Yi-Fan Tian & Shuang-Jie Tan & Chunpeng Yang & Yu-Ming Zhao & Di-Xin Xu & Zhuo-Ya Lu & Ge Li & Jin-Yi Li & Xu-Sheng Zhang & Chao-Hui Zhang & Jilin Tang & Yao Zhao & Fuyi Wang & Rui Wen & Quan Xu & Yu-, 2023. "Tailoring chemical composition of solid electrolyte interphase by selective dissolution for long-life micron-sized silicon anode," Nature Communications, Nature, vol. 14(1), pages 1-10, December.
    20. Tang, Aihua & Huang, Yukun & Xu, Yuchen & Hu, Yuanzhi & Yan, Fuwu & Tan, Yong & Jin, Xin & Yu, Quanqing, 2024. "Data-physics-driven estimation of battery state of charge and capacity," Energy, Elsevier, vol. 294(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:15:y:2022:i:3:p:963-:d:736744. 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.