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A state of charge estimation method for lithium-ion batteries based on fractional order adaptive extended kalman filter

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  • Zhu, Qiao
  • Xu, Mengen
  • Liu, Weiqun
  • Zheng, Mengqian

Abstract

This paper focuses on the state of charge (SOC) estimation of a lithium-ion battery based on a fractional-order adaptive extended Kalman filter (FO-AEKF). First, a fractional order model (FOM) is introduced to describe the physical behavior of the battery which is superior than the integral-order model (IOM), because there are diffused and decentralized characteristics in battery inner parameters. Then, the parameters of the FOM are identified by a genetic algorithm which can realize optimal parameter identification. After that, the FO-AEKF algorithm is developed, which combines the advantages of the FOM and the adaptive strategy. Consequently, the FO-AEKF can quickly track the unknown and time-invariant (or slow time-varying) noise variance and then improve the accuracy of SOC estimation. Finally, two types of lithium-ion batteries and two dynamic operation conditions are given to show the efficiency of the FO-AEKF by comparing with the extended Kalman filter (EKF) for FOM and the adaptive extended Kalman filter (AEKF) for IOM.

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  • Zhu, Qiao & Xu, Mengen & Liu, Weiqun & Zheng, Mengqian, 2019. "A state of charge estimation method for lithium-ion batteries based on fractional order adaptive extended kalman filter," Energy, Elsevier, vol. 187(C).
    • Handle: RePEc:eee:energy:v:187:y:2019:i:c:s036054421931552x
    DOI: 10.1016/j.energy.2019.115880
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    1. Yixing Chen & Deqing Huang & Qiao Zhu & Weiqun Liu & Congzhi Liu & Neng Xiong, 2017. "A New State of Charge Estimation Algorithm for Lithium-Ion Batteries Based on the Fractional Unscented Kalman Filter," Energies, MDPI, vol. 10(9), pages 1-19, September.
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    3. 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).
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    5. Ingvild B. Espedal & Asanthi Jinasena & Odne S. Burheim & Jacob J. Lamb, 2021. "Current Trends for State-of-Charge (SoC) Estimation in Lithium-Ion Battery Electric Vehicles," Energies, MDPI, vol. 14(11), pages 1-24, June.
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    7. 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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    9. 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.
    10. Vichard, L. & Ravey, A. & Venet, P. & Harel, F. & Pelissier, S. & Hissel, D., 2021. "A method to estimate battery SOH indicators based on vehicle operating data only," Energy, Elsevier, vol. 225(C).
    11. Abdelghani Djeddi & Djalel Dib & Ahmad Taher Azar & Salem Abdelmalek, 2019. "Fractional Order Unknown Inputs Fuzzy Observer for Takagi–Sugeno Systems with Unmeasurable Premise Variables," Mathematics, MDPI, vol. 7(10), pages 1-16, October.
    12. Qi Wang & Tian Gao & Xingcan Li, 2022. "SOC Estimation of Lithium-Ion Battery Based on Equivalent Circuit Model with Variable Parameters," Energies, MDPI, vol. 15(16), pages 1-15, August.
    13. Lei Pei & Cheng Yu & Tiansi Wang & Jiawei Yang & Wanlin Wang, 2024. "A Training-Free Estimation Method for the State of Charge and State of Health of Series Battery Packs under Various Load Profiles," Energies, MDPI, vol. 17(8), pages 1-20, April.
    14. Zhao, Xinze & Sun, Bingxiang & Zhang, Weige & He, Xitian & Ma, Shichang & Zhang, Junwei & Liu, Xiaopeng, 2024. "Error theory study on EKF-based SOC and effective error estimation strategy for Li-ion batteries," Applied Energy, Elsevier, vol. 353(PA).
    15. Edgar D. Silva-Vera & Jesus E. Valdez-Resendiz & Gerardo Escobar & Daniel Guillen & Julio C. Rosas-Caro & Jose M. Sosa, 2024. "Data-Driven Modeling and Open-Circuit Voltage Estimation of Lithium-Ion Batteries," Mathematics, MDPI, vol. 12(18), pages 1-16, September.
    16. Hou, Jie & Liu, Jiawei & Chen, Fengwei & Li, Penghua & Zhang, Tao & Jiang, Jincheng & Chen, Xiaolei, 2023. "Robust lithium-ion state-of-charge and battery parameters joint estimation based on an enhanced adaptive unscented Kalman filter," Energy, Elsevier, vol. 271(C).
    17. Renxin, Xiao & Yi, Yang & Xianguang, Jia & Nan, Pan, 2023. "Collaborative estimations of state of energy and maximum available energy of lithium-ion batteries with optimized time windows considering instantaneous energy efficiencies," Energy, Elsevier, vol. 274(C).
    18. 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).
    19. Yunjin Ao & Yong-Chao Liu & Salah Laghrouche & Denis Candusso, 2024. "Dynamic Fractional-Order Model of Proton Exchange Membrane Fuel Cell System for Sustainability Improvement," Sustainability, MDPI, vol. 16(7), pages 1-16, April.
    20. Wang, Yujie & Tian, Jiaqiang & Sun, Zhendong & Wang, Li & Xu, Ruilong & Li, Mince & Chen, Zonghai, 2020. "A comprehensive review of battery modeling and state estimation approaches for advanced battery management systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 131(C).
    21. Fan Zhang & Lele Yin & Jianqiang Kang, 2021. "Enhancing Stability and Robustness of State-of-Charge Estimation for Lithium-Ion Batteries by Using Improved Adaptive Kalman Filter Algorithms," Energies, MDPI, vol. 14(19), pages 1-18, October.
    22. Maheshwari, A. & Nageswari, S., 2022. "Real-time state of charge estimation for electric vehicle power batteries using optimized filter," Energy, Elsevier, vol. 254(PB).
    23. Jun Yuan & Zhili Qin & Haikun Huang & Xingdong Gan & Shuguang Li & Baihai Li, 2023. "State of Health Estimation and Remaining Useful Life Prediction for a Lithium-Ion Battery with a Two-Layer Stacking Regressor," Energies, MDPI, vol. 16(5), pages 1-15, February.

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