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Computationally Efficient State-of-Charge Estimation in Li-Ion Batteries Using Enhanced Dual-Kalman Filter

Author

Listed:
  • Ali Wadi

    (Department of Mechanical Engineering, American University of Sharjah, Sharjah P.O. Box 26666, United Arab Emirates)

  • Mamoun Abdel-Hafez

    (Department of Mechanical Engineering, American University of Sharjah, Sharjah P.O. Box 26666, United Arab Emirates)

  • Ala A. Hussein

    (Department of Electrical Engineering, Prince Mohammad Bin Fahd University, Khobar 31952, Saudi Arabia
    Florida Solar Energy Center, University of Central Florida, Orlando, FL 32922-5703, USA)

Abstract

This paper proposes a state-of-charge estimation technique to meet highly dynamic power requirements in electric vehicles. When the power going in/out the battery is highly dynamic, the statistics of the measurement noise are expected to deviate and maybe change over time from the expected laboratory specified values. Therefore, we propose to integrate adaptive noise identification with the dual-Kalman filter to obtain a robust and computationally-efficient estimation. The proposed technique is verified at the pack and cell levels using a 3.6 V lithium-ion battery cell and a 12.8 V lithium-ion battery pack. Standardized electric vehicle tests are conducted and used to validate the proposed technique, such as dynamic stress test, urban dynamometer driving schedule, and constant-current discharge tests at different temperatures. Results demonstrate a sustained improvement in the estimation accuracy and a high robustness due to immunity to changes in the statistics of the process and measurement noise sequences using the proposed technique.

Suggested Citation

  • Ali Wadi & Mamoun Abdel-Hafez & Ala A. Hussein, 2022. "Computationally Efficient State-of-Charge Estimation in Li-Ion Batteries Using Enhanced Dual-Kalman Filter," Energies, MDPI, vol. 15(10), pages 1-15, May.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:10:p:3717-:d:818869
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    References listed on IDEAS

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    1. Wei He & Michael Pecht & David Flynn & Fateme Dinmohammadi, 2018. "A Physics-Based Electrochemical Model for Lithium-Ion Battery State-of-Charge Estimation Solved by an Optimised Projection-Based Method and Moving-Window Filtering," Energies, MDPI, vol. 11(8), pages 1-23, August.
    2. Bizhong Xia & Haiqing Wang & Yong Tian & Mingwang Wang & Wei Sun & Zhihui Xu, 2015. "State of Charge Estimation of Lithium-Ion Batteries Using an Adaptive Cubature Kalman Filter," Energies, MDPI, vol. 8(6), pages 1-21, June.
    3. Li, Shi & Pischinger, Stefan & He, Chaoyi & Liang, Liliuyuan & Stapelbroek, Michael, 2018. "A comparative study of model-based capacity estimation algorithms in dual estimation frameworks for lithium-ion batteries under an accelerated aging test," Applied Energy, Elsevier, vol. 212(C), pages 1522-1536.
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    Cited by:

    1. Xinyue Liu & Yang Gao & Kyamra Marma & Yu Miao & Lin Liu, 2024. "Advances in the Study of Techniques to Determine the Lithium-Ion Battery’s State of Charge," Energies, MDPI, vol. 17(7), pages 1-16, March.
    2. Salvatore Micari & Giuseppe Napoli, 2024. "Electric Vehicles for a Flexible Energy System: Challenges and Opportunities," Energies, MDPI, vol. 17(22), pages 1-26, November.

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