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Extended Rauch–Tung–Striebel Smoother for the State of Charge Estimation of Lithium-Ion Batteries Based on an Enhanced Circuit Model

Author

Listed:
  • Yinfeng Jiang

    (School of Mechatronic Engineering and Automation, Shanghai University, Shanghai 200444, China)

  • Wenxiang Song

    (School of Mechatronic Engineering and Automation, Shanghai University, Shanghai 200444, China)

  • Hao Zhu

    (HNU College of Mechanical and Vehicle Engineering, Hunan University, Changsha 410082, China)

  • Yun Zhu

    (Hunan Hong Xun Yi’An New Energy and Technology, Co., Ltd., Zhuzhou 412007, China)

  • Yongzhi Du

    (Hunan Hong Xun Yi’An New Energy and Technology, Co., Ltd., Zhuzhou 412007, China)

  • Huichun Yin

    (Hunan Hong Xun Yi’An New Energy and Technology, Co., Ltd., Zhuzhou 412007, China)

Abstract

The state of charge (SOC) of a lithium battery system is critical since it indicates the remaining operating hours, full charge time, and peak power of the battery. This paper recommends an extended Rauch–Tung–Striebel smoother (ERTSS) for estimating SOC. It is implemented based on an improved equivalent circuit model with hysteresis voltage. The smoothing step of ERTSS will reduce the estimation error further. Additionally, the genetic algorithm (GA) is employed for searching the optimal ERTSS’s smoothing time interval. Various dynamic cell tests are conducted to verify the model’s accuracy and error estimation deviation. The test results demonstrate that ERTSS’s SOC estimation error is limited to 4 % with an initial error between −25 ∘ C and 45 ∘ C and that the root mean square error (RMSE) of ERTSS’s SOC estimation is approximately 5% lower than that of extended Kalman filter (EKF). The ERTSS improves the SOC estimation accuracy at all operating temperatures of batteries.

Suggested Citation

  • Yinfeng Jiang & Wenxiang Song & Hao Zhu & Yun Zhu & Yongzhi Du & Huichun Yin, 2022. "Extended Rauch–Tung–Striebel Smoother for the State of Charge Estimation of Lithium-Ion Batteries Based on an Enhanced Circuit Model," Energies, MDPI, vol. 15(3), pages 1-17, January.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:3:p:963-:d:736744
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    References listed on IDEAS

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    1. Sewon Park & Seo Yeong Jeong & Tae Kyung Lee & Min Woo Park & Hyeong Yong Lim & Jaekyung Sung & Jaephil Cho & Sang Kyu Kwak & Sung You Hong & Nam-Soon Choi, 2021. "Replacing conventional battery electrolyte additives with dioxolone derivatives for high-energy-density lithium-ion batteries," Nature Communications, Nature, vol. 12(1), pages 1-12, December.
    2. Quanqing Yu & Changjiang Wan & Junfu Li & Lixin E & Xin Zhang & Yonghe Huang & Tao Liu, 2021. "An Open Circuit Voltage Model Fusion Method for State of Charge Estimation of Lithium-Ion Batteries," Energies, MDPI, vol. 14(7), pages 1-22, March.
    3. Lin Su & Guangxu Zhou & Dairong Hu & Yuan Liu & Yunhai Zhu, 2021. "Research on the State of Charge of Lithium-Ion Battery Based on the Fractional Order Model," Energies, MDPI, vol. 14(19), pages 1-23, October.
    4. 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.
    5. Longxing Wu & Kai Liu & Hui Pang & Jiamin Jin, 2021. "Online SOC Estimation Based on Simplified Electrochemical Model for Lithium-Ion Batteries Considering Current Bias," Energies, MDPI, vol. 14(17), pages 1-12, August.
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    1. Suwei Zhai & Wenyun Li & Cheng Wang & Yundi Chu, 2022. "A Novel Data-Driven Estimation Method for State-of-Charge Estimation of Li-Ion Batteries," Energies, MDPI, vol. 15(9), pages 1-17, April.

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