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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
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    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).
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