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State of Power Estimation of Echelon-Use Battery Based on Adaptive Dual Extended Kalman Filter

Author

Listed:
  • Enguang Hou

    (School of Electrical Engineering, Shandong University, Jinan 250061, China
    School of Rail Transportation, Shandong Jiao Tong University, Jinan 250357, China)

  • Yanliang Xu

    (School of Electrical Engineering, Shandong University, Jinan 250061, China)

  • Xin Qiao

    (School of Rail Transportation, Shandong Jiao Tong University, Jinan 250357, China)

  • Guangmin Liu

    (School of Rail Transportation, Shandong Jiao Tong University, Jinan 250357, China)

  • Zhixue Wang

    (School of Rail Transportation, Shandong Jiao Tong University, Jinan 250357, China)

Abstract

Owing to the degradation of the performance of a retired battery and the unclear initial value of the state of charge (SOC), the estimation of the state of power (SOP) of an echelon-use battery is not accurate. An SOP estimation method based on an adaptive dual extended Kalman filter (ADEKF) is proposed. First, the second-order Thevenin equivalent model of the echelon-use battery is established. Second, the battery parameters are estimated by the ADEKF: (a) the SOC is estimated based on an adaptive extended Kalman filtering algorithm, that uses the process noise covariance Q k and observes the noise covariance R k , and (b) the ohmic internal resistance and actual capacity are estimated based on the aforementioned algorithm, that uses the process noise covariance Q ? , k and observes the noise covariance R ? , k . Third, the working voltage and internal resistance are predicted using optimal estimation, and the SOP of the echelon-use battery is estimated. MATLAB simulation results show that, regardless of whether or not the initial value of the SOC is clear, the proposed algorithm can be adjusted to the adaptive algorithm, and if the estimation accuracy error of the echelon-use battery SOP is less than 4.8%, it has high accuracy. This paper provides a valuable reference for the prediction of the SOP of an echelon-use battery, and will be helpful for understanding the behavior of retired batteries for further discharge and use.

Suggested Citation

  • Enguang Hou & Yanliang Xu & Xin Qiao & Guangmin Liu & Zhixue Wang, 2021. "State of Power Estimation of Echelon-Use Battery Based on Adaptive Dual Extended Kalman Filter," Energies, MDPI, vol. 14(17), pages 1-14, September.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:17:p:5579-:d:630133
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    References listed on IDEAS

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    1. Tong, Shi Jie & Same, Adam & Kootstra, Mark A. & Park, Jae Wan, 2013. "Off-grid photovoltaic vehicle charge using second life lithium batteries: An experimental and numerical investigation," Applied Energy, Elsevier, vol. 104(C), pages 740-750.
    2. Zhang, Youlang & Li, Yan & Tao, Yibin & Ye, Jilei & Pan, Aiqiang & Li, Xinzhou & Liao, Qiangqiang & Wang, Zhiqin, 2020. "Performance assessment of retired EV battery modules for echelon use," Energy, Elsevier, vol. 193(C).
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    Cited by:

    1. Xin Zhang & Jiawei Hou & Zekun Wang & Yueqiu Jiang, 2022. "Joint SOH-SOC Estimation Model for Lithium-Ion Batteries Based on GWO-BP Neural Network," Energies, MDPI, vol. 16(1), pages 1-17, December.
    2. Tae-Won Noh & Junghoon Ahn & Byoung Kuk Lee, 2022. "Online Cell Screening Algorithm for Maximum Peak Current Estimation of a Lithium-Ion Battery Pack for Electric Vehicles," Energies, MDPI, vol. 15(4), pages 1-14, February.
    3. Huaqin Zhang & Jichao Hong & Zhezhe Wang & Guodong Wu, 2022. "State-Partial Accurate Voltage Fault Prognosis for Lithium-Ion Batteries Based on Self-Attention Networks," Energies, MDPI, vol. 15(22), pages 1-14, November.

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