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Online Parameter Identification of Lithium-Ion Batteries Using a Novel Multiple Forgetting Factor Recursive Least Square Algorithm

Author

Listed:
  • Bizhong Xia

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

  • Rui Huang

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

  • Zizhou Lao

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

  • Ruifeng Zhang

    (Graduate School at Shenzhen, Tsinghua University, Shenzhen 518055, China
    Sunwoda Electronic Co. Ltd., Shenzhen 518108, China)

  • Yongzhi Lai

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

  • Weiwei Zheng

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

  • Huawen Wang

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

  • Wei Wang

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

  • Mingwang Wang

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

Abstract

The model parameters of the lithium-ion battery are of great importance to model-based battery state estimation methods. The fact that parameters change in different rates with operation temperature, state of charge (SOC), state of health (SOH) and other factors calls for an online parameter identification algorithm that can track different dynamic characters of the parameters. In this paper, a novel multiple forgetting factor recursive least square (MFFRLS) algorithm was proposed. Forgetting factors were assigned to each parameter, allowing the algorithm to capture the different dynamics of the parameters. Particle swarm optimization (PSO) was utilized to determine the optimal forgetting factors. A state of the art SOC estimator, known as the unscented Kalman filter (UKF), was combined with the online parameter identification to create an accurate estimation of SOC. The effectiveness of the proposed method was verified through a driving cycle under constant temperature and three different driving cycles under varied temperature. The single forgetting factor recursive least square (SFFRLS)-UKF and UKF with fixed parameter were also tested for comparison. The proposed MFFRLS-UKF method obtained an accurate estimation of SOC especially when the battery was running in an environment of changing temperature.

Suggested Citation

  • Bizhong Xia & Rui Huang & Zizhou Lao & Ruifeng Zhang & Yongzhi Lai & Weiwei Zheng & Huawen Wang & Wei Wang & Mingwang Wang, 2018. "Online Parameter Identification of Lithium-Ion Batteries Using a Novel Multiple Forgetting Factor Recursive Least Square Algorithm," Energies, MDPI, vol. 11(11), pages 1-19, November.
  • Handle: RePEc:gam:jeners:v:11:y:2018:i:11:p:3180-:d:183308
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    References listed on IDEAS

    as
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    Cited by:

    1. Wei, Zhongbao & Zhao, Difan & He, Hongwen & Cao, Wanke & Dong, Guangzhong, 2020. "A noise-tolerant model parameterization method for lithium-ion battery management system," Applied Energy, Elsevier, vol. 268(C).
    2. Qiaohua Fang & Xuezhe Wei & Tianyi Lu & Haifeng Dai & Jiangong Zhu, 2019. "A State of Health Estimation Method for Lithium-Ion Batteries Based on Voltage Relaxation Model," Energies, MDPI, vol. 12(7), pages 1-18, April.
    3. Hong, Jichao & Li, Kerui & Liang, Fengwei & Yang, Haixu & Zhang, Chi & Yang, Qianqian & Wang, Jiegang, 2024. "A novel state of health prediction method for battery system in real-world vehicles based on gated recurrent unit neural networks," Energy, Elsevier, vol. 289(C).

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