IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v16y2023i6p2710-d1097067.html
   My bibliography  Save this article

Comparison of Kalman Filters for State Estimation Based on Computational Complexity of Li-Ion Cells

Author

Listed:
  • Areeb Khalid

    (Department of Electrical Engineering, University of Engineering and Technology, Lahore 54890, Pakistan)

  • Syed Abdul Rahman Kashif

    (Department of Electrical Engineering, University of Engineering and Technology, Lahore 54890, Pakistan)

  • Noor Ul Ain

    (Department of Electrical Engineering, University of Engineering and Technology, Lahore 54890, Pakistan)

  • Muhammad Awais

    (National Transmission and Dispatch Company, Lahore 54890, Pakistan)

  • Majid Ali Smieee

    (Center for Research on Microgrids (CROM), AAU Energy, Aalborg University, 9220 Aalborg, Denmark)

  • Jorge El Mariachet Carreño

    (Center for Research on Microgrids (CROM), AAU Energy, Aalborg University, 9220 Aalborg, Denmark)

  • Juan C. Vasquez

    (Center for Research on Microgrids (CROM), AAU Energy, Aalborg University, 9220 Aalborg, Denmark)

  • Josep M. Guerrero

    (Center for Research on Microgrids (CROM), AAU Energy, Aalborg University, 9220 Aalborg, Denmark)

  • Baseem Khan

    (Department of Electrical and Computer Engineering, Hawassa University, Hawassa 1530, Ethiopia)

Abstract

Over the last few decades, lithium-ion batteries have grown in importance for the use of many portable devices and vehicular applications. It has been seen that their life expectancy is much more effective if the required conditions are met. In one of the required conditions, accurately estimating the battery’s state of charge (SOC) is one of the important factors. The purpose of this research paper is to implement the probabilistic filter algorithms for SOC estimation; however, there are challenges associated with that. Generally, for the battery to be effective the Bayesian estimation algorithms are required, which are recursively updating the probability density function of the system states. To address the challenges associated with SOC estimation, the research paper goes further into the functions of the extended Kalman filter (EKF) and sigma point Kalman filter (SPKF). The function of both of these filters will be able to provide an accurate estimation. Further studies are required for these filters’ performance, robustness, and computational complexity. For example, some filters might be accurate, might not be robust, and/or not implementable on a simple microcontroller in a vehicle’s battery management system (BMS). A comparison is made between the EKF and SPKF by running simulations in MATLAB. It is found that the SPKF has an obvious advantage over the EKF in state estimation. Within the SPKF, the sub-filter, the central difference Kalman filter (CDKF), can be considered as an alternative to the EKF for state estimation in battery management systems for electric vehicles. However, there are implications to this which include the compromise of computational complexity in which a more sophisticated micro-controller is required.

Suggested Citation

  • Areeb Khalid & Syed Abdul Rahman Kashif & Noor Ul Ain & Muhammad Awais & Majid Ali Smieee & Jorge El Mariachet Carreño & Juan C. Vasquez & Josep M. Guerrero & Baseem Khan, 2023. "Comparison of Kalman Filters for State Estimation Based on Computational Complexity of Li-Ion Cells," Energies, MDPI, vol. 16(6), pages 1-20, March.
  • Handle: RePEc:gam:jeners:v:16:y:2023:i:6:p:2710-:d:1097067
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/16/6/2710/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1996-1073/16/6/2710/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Cui, Zhenhua & Kang, Le & Li, Liwei & Wang, Licheng & Wang, Kai, 2022. "A combined state-of-charge estimation method for lithium-ion battery using an improved BGRU network and UKF," Energy, Elsevier, vol. 259(C).
    2. 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.
    3. 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).
    4. Quan Ouyang & Rui Ma & Zhaoxiang Wu & Guotuan Xu & Zhisheng Wang, 2020. "Adaptive Square-Root Unscented Kalman Filter-Based State-of-Charge Estimation for Lithium-Ion Batteries with Model Parameter Online Identification," Energies, MDPI, vol. 13(18), pages 1-14, September.
    5. He, Lin & Wang, Yangyang & Wei, Yujiang & Wang, Mingwei & Hu, Xiaosong & Shi, Qin, 2022. "An adaptive central difference Kalman filter approach for state of charge estimation by fractional order model of lithium-ion battery," Energy, Elsevier, vol. 244(PA).
    Full references (including those not matched with items on IDEAS)

    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. 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).
    2. Chen, Lin & Yu, Wentao & Cheng, Guoyang & Wang, Jierui, 2023. "State-of-charge estimation of lithium-ion batteries based on fractional-order modeling and adaptive square-root cubature Kalman filter," Energy, Elsevier, vol. 271(C).
    3. Chen, Junxiong & Zhang, Yu & Wu, Ji & Cheng, Weisong & Zhu, Qiao, 2023. "SOC estimation for lithium-ion battery using the LSTM-RNN with extended input and constrained output," Energy, Elsevier, vol. 262(PA).
    4. Tang, Aihua & Huang, Yukun & Liu, Shangmei & Yu, Quanqing & Shen, Weixiang & Xiong, Rui, 2023. "A novel lithium-ion battery state of charge estimation method based on the fusion of neural network and equivalent circuit models," Applied Energy, Elsevier, vol. 348(C).
    5. Van Quan Dao & Minh-Chau Dinh & Chang Soon Kim & Minwon Park & Chil-Hoon Doh & Jeong Hyo Bae & Myung-Kwan Lee & Jianyong Liu & Zhiguo Bai, 2021. "Design of an Effective State of Charge Estimation Method for a Lithium-Ion Battery Pack Using Extended Kalman Filter and Artificial Neural Network," Energies, MDPI, vol. 14(9), pages 1-20, May.
    6. Li, Li & Ling, Lei & Xie, Yajun & Zhou, Wencai & Wang, Tianbo & Zhang, Lanchun & Bei, Shaoyi & Zheng, Keqing & Xu, Qiang, 2023. "Comparative study of thermal management systems with different cooling structures for cylindrical battery modules: Side-cooling vs. terminal-cooling," Energy, Elsevier, vol. 274(C).
    7. 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.
    8. Yang, Bowen & Wang, Dafang & Sun, Xu & Chen, Shiqin & Wang, Xingcheng, 2023. "Offline order recognition for state estimation of Lithium-ion battery using fractional order model," Applied Energy, Elsevier, vol. 341(C).
    9. Zhu, Yunlong & Dong, Zhe & Cheng, Zhonghua & Huang, Xiaojin & Dong, Yujie & Zhang, Zuoyi, 2023. "Neural network extended state-observer for energy system monitoring," Energy, Elsevier, vol. 263(PA).
    10. He, Lin & Wang, Yangyang & Wei, Yujiang & Wang, Mingwei & Hu, Xiaosong & Shi, Qin, 2022. "An adaptive central difference Kalman filter approach for state of charge estimation by fractional order model of lithium-ion battery," Energy, Elsevier, vol. 244(PA).
    11. Julan Chen & Guangheng Qi & Kai Wang, 2023. "Synergizing Machine Learning and the Aviation Sector in Lithium-Ion Battery Applications: A Review," Energies, MDPI, vol. 16(17), pages 1-22, August.
    12. Cui, Zhenhua & Kang, Le & Li, Liwei & Wang, Licheng & Wang, Kai, 2022. "A hybrid neural network model with improved input for state of charge estimation of lithium-ion battery at low temperatures," Renewable Energy, Elsevier, vol. 198(C), pages 1328-1340.
    13. He, Lin & Hu, Xingwen & Yin, Guangwei & Shao, Xingguo & Liu, Jichao & Shi, Qin, 2023. "A voltage dynamics model of lithium-ion battery for state-of-charge estimation by proportional-integral observer," Applied Energy, Elsevier, vol. 351(C).
    14. Dezhi Li & Dongfang Yang & Liwei Li & Licheng Wang & Kai Wang, 2022. "Electrochemical Impedance Spectroscopy Based on the State of Health Estimation for Lithium-Ion Batteries," Energies, MDPI, vol. 15(18), pages 1-26, September.
    15. Molla Shahadat Hossain Lipu & Tahia F. Karim & Shaheer Ansari & Md. Sazal Miah & Md. Siddikur Rahman & Sheikh T. Meraj & Rajvikram Madurai Elavarasan & Raghavendra Rajan Vijayaraghavan, 2022. "Intelligent SOX Estimation for Automotive Battery Management Systems: State-of-the-Art Deep Learning Approaches, Open Issues, and Future Research Opportunities," Energies, MDPI, vol. 16(1), pages 1-31, December.
    16. Li, Alan G. & Wang, Weizhong & West, Alan C. & Preindl, Matthias, 2022. "Health and performance diagnostics in Li-ion batteries with pulse-injection-aided machine learning," Applied Energy, Elsevier, vol. 315(C).
    17. García, Antonio & Monsalve-Serrano, Javier & Ponce-Mora, Alberto & Fogué-Robles, Álvaro, 2023. "Development of a calibration methodology for fitting the response of a lithium-ion cell P2D model using real driving cycles," Energy, Elsevier, vol. 271(C).
    18. Peng Guo & Xiaobo Wu & António M. Lopes & Anyu Cheng & Yang Xu & Liping Chen, 2022. "Parameter Identification for Lithium-Ion Battery Based on Hybrid Genetic–Fractional Beetle Swarm Optimization Method," Mathematics, MDPI, vol. 10(17), pages 1-11, August.
    19. Ming Zhang & Dongfang Yang & Jiaxuan Du & Hanlei Sun & Liwei Li & Licheng Wang & Kai Wang, 2023. "A Review of SOH Prediction of Li-Ion Batteries Based on Data-Driven Algorithms," Energies, MDPI, vol. 16(7), pages 1-28, March.
    20. Lai, Xin & Yao, Yi & Tang, Xiaopeng & Zheng, Yuejiu & Zhou, Yuanqiang & Sun, Yuedong & Gao, Furong, 2023. "Voltage profile reconstruction and state of health estimation for lithium-ion batteries under dynamic working conditions," Energy, Elsevier, vol. 282(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:16:y:2023:i:6:p:2710-:d:1097067. 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.