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Influence Analysis and Optimization of Sampling Frequency on the Accuracy of Model and State-of-Charge Estimation for LiNCM Battery

Author

Listed:
  • Pingwei Gu

    (School of Control Science and Engineering, Shandong University, Shandong 250061, China)

  • Zhongkai Zhou

    (School of Control Science and Engineering, Shandong University, Shandong 250061, China)

  • Shaofei Qu

    (School of Control Science and Engineering, Shandong University, Shandong 250061, China)

  • Chenghui Zhang

    (School of Control Science and Engineering, Shandong University, Shandong 250061, China)

  • Bin Duan

    (School of Control Science and Engineering, Shandong University, Shandong 250061, China)

Abstract

Battery characterization data is the basis for battery modeling and state estimation. It is generally believed that the higher the sampling frequency, the finer the data, and the higher the model and state estimation accuracy. However, scientific selection strategy for sampling frequency is very important but rarely studied. This paper studies the influence of sampling frequency on the accuracy of battery model and state estimation under four different sampling frequencies: 0.2 Hz, 1 Hz, 2 Hz, and 10 Hz. Then, a function is proposed to depict the relationship between accuracy and sampling frequency, which shows an optimal selection principle. The iterative identification algorithm is presented to identify the model parameters, and state-of-charge (SOC) is estimated via extended Kalman filter algorithm. Experimental results with different operating conditions clearly show the relationship between sampling frequency, accuracy, and data quantity, and the proposed selection strategy has high practical value and universality.

Suggested Citation

  • Pingwei Gu & Zhongkai Zhou & Shaofei Qu & Chenghui Zhang & Bin Duan, 2019. "Influence Analysis and Optimization of Sampling Frequency on the Accuracy of Model and State-of-Charge Estimation for LiNCM Battery," Energies, MDPI, vol. 12(7), pages 1-19, March.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:7:p:1205-:d:217842
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    References listed on IDEAS

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    1. Farmann, Alexander & Sauer, Dirk Uwe, 2018. "Comparative study of reduced order equivalent circuit models for on-board state-of-available-power prediction of lithium-ion batteries in electric vehicles," Applied Energy, Elsevier, vol. 225(C), pages 1102-1122.
    2. Xia, Bizhong & Chen, Chaoren & Tian, Yong & Wang, Mingwang & Sun, Wei & Xu, Zhihui, 2015. "State of charge estimation of lithium-ion batteries based on an improved parameter identification method," Energy, Elsevier, vol. 90(P2), pages 1426-1434.
    3. Ng, Kong Soon & Moo, Chin-Sien & Chen, Yi-Ping & Hsieh, Yao-Ching, 2009. "Enhanced coulomb counting method for estimating state-of-charge and state-of-health of lithium-ion batteries," Applied Energy, Elsevier, vol. 86(9), pages 1506-1511, September.
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    Cited by:

    1. Saeed Mian Qaisar, 2020. "Event-Driven Coulomb Counting for Effective Online Approximation of Li-Ion Battery State of Charge," Energies, MDPI, vol. 13(21), pages 1-20, October.
    2. Chen, Biao & Jiang, Haobin & Chen, Xijia & Li, Huanhuan, 2022. "Robust state-of-charge estimation for lithium-ion batteries based on an improved gas-liquid dynamics model," Energy, Elsevier, vol. 238(PC).
    3. Shaofei Qu & Yongzhe Kang & Pingwei Gu & Chenghui Zhang & Bin Duan, 2019. "A Fast Online State of Health Estimation Method for Lithium-Ion Batteries Based on Incremental Capacity Analysis," Energies, MDPI, vol. 12(17), pages 1-11, August.

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