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Adaptive state-of-charge estimation of lithium-ion batteries based on square-root unscented Kalman filter

Author

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  • Chen, Liping
  • Wu, Xiaobo
  • Lopes, António M.
  • Yin, Lisheng
  • Li, Penghua

Abstract

This paper proposes a state of charge (SOC) estimation method for lithium-ion batteries. Firstly, a fractional second-order RC circuit model of the battery is established. Then, a particle swarm optimization algorithm with a linear differential decline strategy is adopted to identify the model parameters, and the accuracy of the parameterized model is verified. Finally, an adaptive fractional-order square root unscented Kalman filter (AFSR-UKF) is developed, which is able to update the noise information in real time and to overcome divergence caused by inappropriate noise covariance matrices. The effectiveness of the SOC estimation method based on the AFSR-UKF is verified under a variety of operational conditions in the perspective of the root-mean-squared error, which is shown to be below 1.0%, including at extreme temperatures, revealing good accuracy and robustness.

Suggested Citation

  • Chen, Liping & Wu, Xiaobo & Lopes, António M. & Yin, Lisheng & Li, Penghua, 2022. "Adaptive state-of-charge estimation of lithium-ion batteries based on square-root unscented Kalman filter," Energy, Elsevier, vol. 252(C).
    • Handle: RePEc:eee:energy:v:252:y:2022:i:c:s0360544222008751
    DOI: 10.1016/j.energy.2022.123972
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    2. Takyi-Aninakwa, Paul & Wang, Shunli & Zhang, Hongying & Yang, Xiaoyong & Fernandez, Carlos, 2022. "An optimized long short-term memory-weighted fading extended Kalman filtering model with wide temperature adaptation for the state of charge estimation of lithium-ion batteries," Applied Energy, Elsevier, vol. 326(C).
    3. 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).
    4. Xie, Yanxin & Wang, Shunli & Zhang, Gexiang & Fan, Yongcun & Fernandez, Carlos & Blaabjerg, Frede, 2023. "Optimized multi-hidden layer long short-term memory modeling and suboptimal fading extended Kalman filtering strategies for the synthetic state of charge estimation of lithium-ion batteries," Applied Energy, Elsevier, vol. 336(C).
    5. Fan, Kesen & Wan, Yiming & Wang, Zhuo & Jiang, Kai, 2023. "Time-efficient identification of lithium-ion battery temperature-dependent OCV-SOC curve using multi-output Gaussian process," Energy, Elsevier, vol. 268(C).
    6. Zhan, Mingjing & Wu, Baigong & Xu, Guoqi & Li, Wenjuan & Liang, Darong & Zhang, Xiao, 2023. "Application of adaptive extended Kalman algorithm based on strong tracking fading factor in Stat-of-Charge estimation of lithium-ion battery," Energy, Elsevier, vol. 284(C).
    7. 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.
    8. Guo, Shanshan & Ma, Liang, 2023. "A comparative study of different deep learning algorithms for lithium-ion batteries on state-of-charge estimation," Energy, Elsevier, vol. 263(PC).

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