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Online Parameter Identification and Joint Estimation of the State of Charge and the State of Health of Lithium-Ion Batteries Considering the Degree of Polarization

Author

Listed:
  • Bizhong Xia

    (Graduate School at Shenzhen, Tsinghua University, Shenzhen 518055, Guangdong, China)

  • Guanghao Chen

    (Graduate School at Shenzhen, Tsinghua University, Shenzhen 518055, Guangdong, China)

  • Jie Zhou

    (Graduate School at Shenzhen, Tsinghua University, Shenzhen 518055, Guangdong, China)

  • Yadi Yang

    (Graduate School at Shenzhen, Tsinghua University, Shenzhen 518055, Guangdong, China)

  • Rui Huang

    (Graduate School at Shenzhen, Tsinghua University, Shenzhen 518055, Guangdong, China)

  • Wei Wang

    (Sunwoda Electronic Co. Ltd., Shenzhen 518108, Guangdong, China)

  • Yongzhi Lai

    (Sunwoda Electronic Co. Ltd., Shenzhen 518108, Guangdong, China)

  • Mingwang Wang

    (Sunwoda Electronic Co. Ltd., Shenzhen 518108, Guangdong, China)

  • Huawen Wang

    (Sunwoda Electronic Co. Ltd., Shenzhen 518108, Guangdong, China)

Abstract

The state of charge (SOC) and the state of health (SOH) are the two most important indexes of batteries. However, they are not measurable with transducers and must be estimated with mathematical algorithms. A precise model and accurate available battery capacity are crucial to the estimation results. An improved speed adaptive velocity particle swarm optimization algorithm (SAVPSO) based on the Thevenin model is used for online parameter identification, which is used with an unscented Kalman filter (UKF) to estimate the SOC. In order to achieve the cyclic update of the SOH, the concept of degree of polarization (DOP) is proposed. The cyclic update of available capacity is thus obtainable to conversely promote the estimation accuracy of the SOC. The estimation experiments in the whole aging process of batteries show that the proposed method can enhance the SOC estimation accuracy in the full battery life cycle with the cyclic update of the SOH, even in cases of operating aged batteries and under complex operating conditions.

Suggested Citation

  • Bizhong Xia & Guanghao Chen & Jie Zhou & Yadi Yang & Rui Huang & Wei Wang & Yongzhi Lai & Mingwang Wang & Huawen Wang, 2019. "Online Parameter Identification and Joint Estimation of the State of Charge and the State of Health of Lithium-Ion Batteries Considering the Degree of Polarization," Energies, MDPI, vol. 12(15), pages 1-20, July.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:15:p:2939-:d:253219
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    References listed on IDEAS

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    Cited by:

    1. Nickolay I. Shchurov & Sergey I. Dedov & Boris V. Malozyomov & Alexander A. Shtang & Nikita V. Martyushev & Roman V. Klyuev & Sergey N. Andriashin, 2021. "Degradation of Lithium-Ion Batteries in an Electric Transport Complex," Energies, MDPI, vol. 14(23), pages 1-33, December.
    2. Bizhong Xia & Guanyong Zhang & Huiyuan Chen & Yuheng Li & Zhuojun Yu & Yunchao Chen, 2022. "Verification Platform of SOC Estimation Algorithm for Lithium-Ion Batteries of Electric Vehicles," Energies, MDPI, vol. 15(9), pages 1-20, April.

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