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System state estimation and optimal energy control framework for multicell lithium-ion battery system

Author

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  • Wei, Jingwen
  • Dong, Guangzhong
  • Chen, Zonghai
  • Kang, Yu

Abstract

Cell variations caused by the inevitable inconsistency during manufacture and use of battery cells have significant impacts on battery capacity, security and durability for battery energy storage systems. Thus, the battery equalization systems are essentially required to reduce variations of in-pack cells and increase battery pack capability. In order to protect all in-pack cells from damaging, estimate battery state and reduce variations, a system state estimation and energy optimal control framework for multicell lithium-ion battery system is proposed. The state-of-charge (SOC) values of all in-pack cells are firstly estimated using a dual-scale extended Kalman filtering (EKF) to improve estimation accuracy and reduce computation simultaneously. These estimated SOC values provide specific details of battery system, which cannot only be used to protect cells from over-charging/over-discharging, but also be employed to design state-feedback controller for battery equalization system. A two-stage hybrid state-feedback and output-feedback equalization algorithm is proposed. The state-feedback controller is firstly employed for coarse-grained adjustment to reduce equalization time cost with large current. However, due to the inevitable SOC estimation errors, the output-feedback controller is then used for fine-grained adjustment with trickle current. Experimental results show that the proposed framework can provide an effectively estimation and energy control for multicell battery systems. Finally, the implementation of the proposed method is further discussed for the real applications.

Suggested Citation

  • Wei, Jingwen & Dong, Guangzhong & Chen, Zonghai & Kang, Yu, 2017. "System state estimation and optimal energy control framework for multicell lithium-ion battery system," Applied Energy, Elsevier, vol. 187(C), pages 37-49.
    • Handle: RePEc:eee:appene:v:187:y:2017:i:c:p:37-49
    DOI: 10.1016/j.apenergy.2016.11.057
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    Cited by:

    1. Zhang, Caiping & Jiang, Yan & Jiang, Jiuchun & Cheng, Gong & Diao, Weiping & Zhang, Weige, 2017. "Study on battery pack consistency evolutions and equilibrium diagnosis for serial- connected lithium-ion batteries," Applied Energy, Elsevier, vol. 207(C), pages 510-519.
    2. Adnan Ashraf & Basit Ali & Mothanna S. A. Alsunjury & Hakime Goren & Halise Kilicoglu & Faysal Hardan & Pietro Tricoli, 2024. "Review of Cell-Balancing Schemes for Electric Vehicle Battery Management Systems," Energies, MDPI, vol. 17(6), pages 1-18, March.
    3. Xiudong Cui & Weixiang Shen & Yunlei Zhang & Cungang Hu, 2017. "A Novel Active Online State of Charge Based Balancing Approach for Lithium-Ion Battery Packs during Fast Charging Process in Electric Vehicles," Energies, MDPI, vol. 10(11), pages 1-17, November.
    4. Tian, Jiaqiang & Fan, Yuan & Pan, Tianhong & Zhang, Xu & Yin, Jianning & Zhang, Qingping, 2024. "A critical review on inconsistency mechanism, evaluation methods and improvement measures for lithium-ion battery energy storage systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 189(PB).
    5. Linhui Zhao & Guohuang Ji & Zhiyuan Liu, 2017. "Design and Experiment of Nonlinear Observer with Adaptive Gains for Battery State of Charge Estimation," Energies, MDPI, vol. 10(12), pages 1-20, December.
    6. Diao, Weiping & Xue, Nan & Bhattacharjee, Vikram & Jiang, Jiuchun & Karabasoglu, Orkun & Pecht, Michael, 2018. "Active battery cell equalization based on residual available energy maximization," Applied Energy, Elsevier, vol. 210(C), pages 690-698.
    7. Jian Yang & Jaewook Jung & Samira Ghorbanpour & Sekyung Han, 2022. "Data–Driven Fault Diagnosis and Cause Analysis of Battery Pack with Real Data," Energies, MDPI, vol. 15(5), pages 1-19, February.

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