IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v13y2020i17p4536-d407465.html
   My bibliography  Save this article

An Accurate State of Charge Estimation Method for Lithium Iron Phosphate Battery Using a Combination of an Unscented Kalman Filter and a Particle Filter

Author

Listed:
  • Thanh-Tung Nguyen

    (Department of Electrical Engineering, Soongsil University, Seoul 06978, Korea)

  • Abdul Basit Khan

    (Department of Electrical Engineering, Soongsil University, Seoul 06978, Korea)

  • Younghwi Ko

    (Department of Electrical Engineering, Soongsil University, Seoul 06978, Korea)

  • Woojin Choi

    (Department of Electrical Engineering, Soongsil University, Seoul 06978, Korea)

Abstract

An accurate state of charge (SOC) estimation of the battery is one of the most important techniques in battery-based power systems, such as electric vehicles (EVs) and energy storage systems (ESSs). The Kalman filter is a preferred algorithm in estimating the SOC of the battery due to the capability of including the time-varying coefficients in the model and its superior performance in the SOC estimation. However, since its performance highly depends on the measurement noise (MN) and process noise (PN) values, it is difficult to obtain highly accurate estimation results with the battery having a flat plateau OCV (open-circuit voltage) area in the SOC-OCV curve, such as the Lithium iron phosphate battery. In this paper, a new integrated estimation method is proposed by combining an unscented Kalman filter and a particle filter (UKF-PF) to estimate the SOC of the Lithium iron phosphate battery. The equivalent circuit of the battery used is composed of a series resistor and two R-C parallel circuits. Then, it is modeled by a second-order autoregressive exogenous (ARX) model, and the parameters are identified by using the recursive least square (RLS) identification method. The validity of the proposed algorithm is verified by comparing the experimental results obtained with the proposed method and the conventional methods.

Suggested Citation

  • Thanh-Tung Nguyen & Abdul Basit Khan & Younghwi Ko & Woojin Choi, 2020. "An Accurate State of Charge Estimation Method for Lithium Iron Phosphate Battery Using a Combination of an Unscented Kalman Filter and a Particle Filter," Energies, MDPI, vol. 13(17), pages 1-15, September.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:17:p:4536-:d:407465
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/13/17/4536/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/13/17/4536/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Zheng, Yuejiu & Ouyang, Minggao & Lu, Languang & Li, Jianqiu & Han, Xuebing & Xu, Liangfei & Ma, Hongbin & Dollmeyer, Thomas A. & Freyermuth, Vincent, 2013. "Cell state-of-charge inconsistency estimation for LiFePO4 battery pack in hybrid electric vehicles using mean-difference model," Applied Energy, Elsevier, vol. 111(C), pages 571-580.
    2. 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.
    3. Hongwen He & Rui Xiong & Jinxin Fan, 2011. "Evaluation of Lithium-Ion Battery Equivalent Circuit Models for State of Charge Estimation by an Experimental Approach," Energies, MDPI, vol. 4(4), pages 1-17, March.
    4. Ngoc-Tham Tran & Abdul Basit Khan & Woojin Choi, 2017. "State of Charge and State of Health Estimation of AGM VRLA Batteries by Employing a Dual Extended Kalman Filter and an ARX Model for Online Parameter Estimation," Energies, MDPI, vol. 10(1), pages 1-18, January.
    5. Xiangwei Guo & Longyun Kang & Yuan Yao & Zhizhen Huang & Wenbiao Li, 2016. "Joint Estimation of the Electric Vehicle Power Battery State of Charge Based on the Least Squares Method and the Kalman Filter Algorithm," Energies, MDPI, vol. 9(2), pages 1-16, February.
    6. Waag, Wladislaw & Käbitz, Stefan & Sauer, Dirk Uwe, 2013. "Experimental investigation of the lithium-ion battery impedance characteristic at various conditions and aging states and its influence on the application," Applied Energy, Elsevier, vol. 102(C), pages 885-897.
    7. Xiong, Rui & Sun, Fengchun & He, Hongwen & Nguyen, Trong Duy, 2013. "A data-driven adaptive state of charge and power capability joint estimator of lithium-ion polymer battery used in electric vehicles," Energy, Elsevier, vol. 63(C), pages 295-308.
    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. Xinghao Zhang & Yan Huang & Zhaowei Zhang & Huipin Lin & Yu Zeng & Mingyu Gao, 2022. "A Hybrid Method for State-of-Charge Estimation for Lithium-Ion Batteries Using a Long Short-Term Memory Network Combined with Attention and a Kalman Filter," Energies, MDPI, vol. 15(18), pages 1-26, September.

    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. Qiaohua Fang & Xuezhe Wei & Haifeng Dai, 2019. "A Remaining Discharge Energy Prediction Method for Lithium-Ion Battery Pack Considering SOC and Parameter Inconsistency," Energies, MDPI, vol. 12(6), pages 1-24, March.
    2. 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.
    3. Turksoy, Arzu & Teke, Ahmet & Alkaya, Alkan, 2020. "A comprehensive overview of the dc-dc converter-based battery charge balancing methods in electric vehicles," Renewable and Sustainable Energy Reviews, Elsevier, vol. 133(C).
    4. Jiale Xie & Jiachen Ma & Jun Chen, 2018. "Peukert-Equation-Based State-of-Charge Estimation for LiFePO4 Batteries Considering the Battery Thermal Evolution Effect," Energies, MDPI, vol. 11(5), pages 1-14, May.
    5. Zou, Yuan & Li, Shengbo Eben & Shao, Bing & Wang, Baojin, 2016. "State-space model with non-integer order derivatives for lithium-ion battery," Applied Energy, Elsevier, vol. 161(C), pages 330-336.
    6. Ozkurt, Celil & Camci, Fatih & Atamuradov, Vepa & Odorry, Christopher, 2016. "Integration of sampling based battery state of health estimation method in electric vehicles," Applied Energy, Elsevier, vol. 175(C), pages 356-367.
    7. 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.
    8. Yang, Ruixin & Xiong, Rui & He, Hongwen & Mu, Hao & Wang, Chun, 2017. "A novel method on estimating the degradation and state of charge of lithium-ion batteries used for electrical vehicles," Applied Energy, Elsevier, vol. 207(C), pages 336-345.
    9. 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.
    10. Wang, Qian & Jiang, Bin & Li, Bo & Yan, Yuying, 2016. "A critical review of thermal management models and solutions of lithium-ion batteries for the development of pure electric vehicles," Renewable and Sustainable Energy Reviews, Elsevier, vol. 64(C), pages 106-128.
    11. 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.
    12. Xian Zhao & Siqi Wang & Xiaoyue Wang, 2018. "Characteristics and Trends of Research on New Energy Vehicle Reliability Based on the Web of Science," Sustainability, MDPI, vol. 10(10), pages 1-25, October.
    13. Feng, Xuning & Weng, Caihao & Ouyang, Minggao & Sun, Jing, 2016. "Online internal short circuit detection for a large format lithium ion battery," Applied Energy, Elsevier, vol. 161(C), pages 168-180.
    14. 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.
    15. Sina Shojaei & Andrew McGordon & Simon Robinson & James Marco, 2017. "Improving the Performance Attributes of Plug-in Hybrid Electric Vehicles in Hot Climates through Key-Off Battery Cooling," Energies, MDPI, vol. 10(12), pages 1-28, December.
    16. 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.
    17. Chuanxue Song & Yulong Shao & Shixin Song & Cheng Chang & Fang Zhou & Silun Peng & Feng Xiao, 2017. "Energy Management of Parallel-Connected Cells in Electric Vehicles Based on Fuzzy Logic Control," Energies, MDPI, vol. 10(3), pages 1-13, March.
    18. Tanaka, T. & Ito, S. & Muramatsu, M. & Yamada, T. & Kamiko, H. & Kakimoto, N. & Inui, Y., 2015. "Accurate and versatile simulation of transient voltage profile of lithium-ion secondary battery employing internal equivalent electric circuit," Applied Energy, Elsevier, vol. 143(C), pages 200-210.
    19. Bai, Guangxing & Wang, Pingfeng & Hu, Chao & Pecht, Michael, 2014. "A generic model-free approach for lithium-ion battery health management," Applied Energy, Elsevier, vol. 135(C), pages 247-260.
    20. Chen, Haosen & Fan, Jinbao & Zhang, Mingliang & Feng, Xiaolong & Zhong, Ximing & He, Jianchao & Ai, Shigang, 2023. "Mechanism of inhomogeneous deformation and equal-stiffness design of large-format prismatic lithium-ion batteries," Applied Energy, Elsevier, vol. 332(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:13:y:2020:i:17:p:4536-:d:407465. 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.