IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v9y2021i15p1733-d599404.html
   My bibliography  Save this article

Lithium-Ion Battery SOC Estimation Based on Adaptive Forgetting Factor Least Squares Online Identification and Unscented Kalman Filter

Author

Listed:
  • Hao Wang

    (College of Automobile and Traffic Engineering, Nanjing Forestry University, Nanjing 210037, China)

  • Yanping Zheng

    (College of Automobile and Traffic Engineering, Nanjing Forestry University, Nanjing 210037, China)

  • Yang Yu

    (College of Automobile and Traffic Engineering, Nanjing Forestry University, Nanjing 210037, China)

Abstract

In order to improve the estimation accuracy of the battery state of charge (SOC) based on the equivalent circuit model, a lithium-ion battery SOC estimation method based on adaptive forgetting factor least squares and unscented Kalman filtering is proposed. The Thevenin equivalent circuit model of the battery is established. Through the simulated annealing optimization algorithm, the forgetting factor is adaptively changed in real-time according to the model demand, and the SOC estimation is realized by combining the least-squares online identification of the adaptive forgetting factor and the unscented Kalman filter. The results show that the terminal voltage error identified by the adaptive forgetting factor least-squares online identification is extremely small; that is, the model parameter identification accuracy is high, and the joint algorithm with the unscented Kalman filter can also achieve a high-precision estimation of SOC.

Suggested Citation

  • Hao Wang & Yanping Zheng & Yang Yu, 2021. "Lithium-Ion Battery SOC Estimation Based on Adaptive Forgetting Factor Least Squares Online Identification and Unscented Kalman Filter," Mathematics, MDPI, vol. 9(15), pages 1-12, July.
    • Handle: RePEc:gam:jmathe:v:9:y:2021:i:15:p:1733-:d:599404
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/9/15/1733/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2227-7390/9/15/1733/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Jiandong Duan & Peng Wang & Wentao Ma & Xinyu Qiu & Xuan Tian & Shuai Fang, 2020. "State of Charge Estimation of Lithium Battery Based on Improved Correntropy Extended Kalman Filter," Energies, MDPI, vol. 13(16), pages 1-18, August.
    2. Ye, Min & Guo, Hui & Cao, Binggang, 2017. "A model-based adaptive state of charge estimator for a lithium-ion battery using an improved adaptive particle filter," Applied Energy, Elsevier, vol. 190(C), pages 740-748.
    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. Yuting Chen & Zhun Cheng & Yu Qian, 2022. "Research on Wet Clutch Switching Quality in the Shifting Stage of an Agricultural Tractor Transmission System," Agriculture, MDPI, vol. 12(8), pages 1-16, August.
    2. Zhun Cheng & Yuting Chen & Wenjie Li & Pengfei Zhou & Junhao Liu & Li Li & Wenjuan Chang & Yu Qian, 2022. "Optimization Design Based on I-GA and Simulation Test Verification of 5-Stage Hydraulic Mechanical Continuously Variable Transmission Used for Tractor," Agriculture, MDPI, vol. 12(6), pages 1-13, June.

    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. Li, Kangqun & Zhou, Fei & Chen, Xing & Yang, Wen & Shen, Junjie & Song, Zebin, 2023. "State-of-charge estimation combination algorithm for lithium-ion batteries with Frobenius-norm-based QR decomposition modified adaptive cubature Kalman filter and H-infinity filter based on electro-th," Energy, Elsevier, vol. 263(PC).
    2. Wenxian Duan & Chuanxue Song & Silun Peng & Feng Xiao & Yulong Shao & Shixin Song, 2020. "An Improved Gated Recurrent Unit Network Model for State-of-Charge Estimation of Lithium-Ion Battery," Energies, MDPI, vol. 13(23), pages 1-19, December.
    3. 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).
    4. Jiang, Cong & Wang, Shunli & Wu, Bin & Fernandez, Carlos & Xiong, Xin & Coffie-Ken, James, 2021. "A state-of-charge estimation method of the power lithium-ion battery in complex conditions based on adaptive square root extended Kalman filter," Energy, Elsevier, vol. 219(C).
    5. Bizhong Xia & Zhen Sun & Ruifeng Zhang & Deyu Cui & Zizhou Lao & Wei Wang & Wei Sun & Yongzhi Lai & Mingwang Wang, 2017. "A Comparative Study of Three Improved Algorithms Based on Particle Filter Algorithms in SOC Estimation of Lithium Ion Batteries," Energies, MDPI, vol. 10(8), pages 1-14, August.
    6. 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.
    7. Jingwei Hu & Bing Lin & Mingfen Wang & Jie Zhang & Wenliang Zhang & Yu Lu, 2022. "State of Charge Centralized Estimation of Road Condition Information Based on Fuzzy Sunday Algorithm," Energies, MDPI, vol. 15(8), pages 1-15, April.
    8. Wei, Zhongbao & Hu, Jian & Li, Yang & He, Hongwen & Li, Weihan & Sauer, Dirk Uwe, 2022. "Hierarchical soft measurement of load current and state of charge for future smart lithium-ion batteries," Applied Energy, Elsevier, vol. 307(C).
    9. Bizhong Xia & Zhen Sun & Ruifeng Zhang & Zizhou Lao, 2017. "A Cubature Particle Filter Algorithm to Estimate the State of the Charge of Lithium-Ion Batteries Based on a Second-Order Equivalent Circuit Model," Energies, MDPI, vol. 10(4), pages 1-15, April.
    10. Tang, Xiaopeng & Gao, Furong & Zou, Changfu & Yao, Ke & Hu, Wengui & Wik, Torsten, 2019. "Load-responsive model switching estimation for state of charge of lithium-ion batteries," Applied Energy, Elsevier, vol. 238(C), pages 423-434.
    11. Ren, Xiaoqing & Liu, Shulin & Yu, Xiaodong & Dong, Xia, 2021. "A method for state-of-charge estimation of lithium-ion batteries based on PSO-LSTM," Energy, Elsevier, vol. 234(C).
    12. Filip Maletić & Mario Hrgetić & Joško Deur, 2020. "Dual Nonlinear Kalman Filter-Based SoC and Remaining Capacity Estimation for an Electric Scooter Li-NMC Battery Pack," Energies, MDPI, vol. 13(3), pages 1-16, January.
    13. Xu, Cheng & Zhang, E & Jiang, Kai & Wang, Kangli, 2022. "Dual fuzzy-based adaptive extended Kalman filter for state of charge estimation of liquid metal battery," Applied Energy, Elsevier, vol. 327(C).
    14. Ye Liu & Yiwei Zhong & Chaowei Tang, 2023. "Optimal Sizing of Photovoltaic/Energy Storage Hybrid Power Systems: Considering Output Characteristics and Uncertainty Factors," Energies, MDPI, vol. 16(14), pages 1-23, July.
    15. Peng, Jiankun & Luo, Jiayi & He, Hongwen & Lu, Bing, 2019. "An improved state of charge estimation method based on cubature Kalman filter for lithium-ion batteries," Applied Energy, Elsevier, vol. 253(C), pages 1-1.
    16. Wang, Yujie & Chen, Zonghai, 2020. "A framework for state-of-charge and remaining discharge time prediction using unscented particle filter," Applied Energy, Elsevier, vol. 260(C).
    17. Zeeshan Ahmad Khan & Prashant Shrivastava & Syed Muhammad Amrr & Saad Mekhilef & Abdullah A. Algethami & Mehdi Seyedmahmoudian & Alex Stojcevski, 2022. "A Comparative Study on Different Online State of Charge Estimation Algorithms for Lithium-Ion Batteries," Sustainability, MDPI, vol. 14(12), pages 1-19, June.
    18. Song, Ziyou & Hofmann, Heath & Lin, Xinfan & Han, Xuebing & Hou, Jun, 2018. "Parameter identification of lithium-ion battery pack for different applications based on Cramer-Rao bound analysis and experimental study," Applied Energy, Elsevier, vol. 231(C), pages 1307-1318.
    19. Deng Ma & Kai Gao & Yutao Mu & Ziqi Wei & Ronghua Du, 2022. "An Adaptive Tracking-Extended Kalman Filter for SOC Estimation of Batteries with Model Uncertainty and Sensor Error," Energies, MDPI, vol. 15(10), pages 1-18, May.
    20. Giuseppe Di Luca & Gabriele Di Blasio & Alfredo Gimelli & Daniela Anna Misul, 2023. "Review on Battery State Estimation and Management Solutions for Next-Generation Connected Vehicles," Energies, MDPI, vol. 17(1), pages 1-34, December.

    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:jmathe:v:9:y:2021:i:15:p:1733-:d:599404. 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.