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Recording frequency optimization for massive battery data storage in battery management systems

Author

Listed:
  • Zheng, Yuejiu
  • Ouyang, Minggao
  • Li, Xiangjun
  • Lu, Languang
  • Li, Jianqiu
  • Zhou, Long
  • Zhang, Zhendong

Abstract

Massive data storage is an advanced function in a fully functional battery management system (BMS). Reducing the recording signal length undoubtedly saves the precious memory space for BMS. And it also reduces the network and computation loads. However, it leads to a side effect that the trend of signal distortion is enhanced. The optimal recording frequency in practice should be as low as possible on the condition that little signal distortion happens. This paper presents a novel method which uses a multi-frequency recording technology that cooperates two approaches according to the signal dynamics. A flexible recording frequency method is applied for stationary signals which only records signals when their values are changed. While for dynamic signals, the most dynamic period is found using discrete wavelet transformation (DWT) and further analyzed by fast Fourier transformation (FFT). By comparing two recording signal indicators for four different recording frequencies, we conclude that recording at 1Hz is not qualified for the cell voltage and current during the dynamic period in our system due to the high dynamic performance of the vehicle. In the demonstrated vehicle, only by increasing the recording frequency to at least 2Hz, can the accuracy of the recorded cell voltage achieve the level the same as the measurement accuracy in engineering. And we also verify that when the recording frequency is reduced to the optimal frequency compared to the high frequency recorded original signals, the accuracy of the SOC estimation is not influenced.

Suggested Citation

  • Zheng, Yuejiu & Ouyang, Minggao & Li, Xiangjun & Lu, Languang & Li, Jianqiu & Zhou, Long & Zhang, Zhendong, 2016. "Recording frequency optimization for massive battery data storage in battery management systems," Applied Energy, Elsevier, vol. 183(C), pages 380-389.
    • Handle: RePEc:eee:appene:v:183:y:2016:i:c:p:380-389
    DOI: 10.1016/j.apenergy.2016.08.140
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    Cited by:

    1. Kong, Xiangdong & Zheng, Yuejiu & Ouyang, Minggao & Li, Xiangjun & Lu, Languang & Li, Jianqiu & Zhang, Zhendong, 2017. "Signal synchronization for massive data storage in modular battery management system with controller area network," Applied Energy, Elsevier, vol. 197(C), pages 52-62.
    2. Christodoulos Katis & Athanasios Karlis, 2023. "Evolution of Equipment in Electromobility and Autonomous Driving Regarding Safety Issues," Energies, MDPI, vol. 16(3), pages 1-34, January.
    3. Deng Ma & Kai Gao & Yutao Mu & Ziqi Wei & Ronghua Du, 2022. "An Adaptive Tracking-Extended Kalman Filter for SOC Estimation of Batteries with Model Uncertainty and Sensor Error," Energies, MDPI, vol. 15(10), pages 1-18, May.
    4. Yang, Jufeng & Xia, Bing & Huang, Wenxin & Fu, Yuhong & Mi, Chris, 2018. "Online state-of-health estimation for lithium-ion batteries using constant-voltage charging current analysis," Applied Energy, Elsevier, vol. 212(C), pages 1589-1600.
    5. Kai-Rong Lin & Chien-Chung Huang & Kin-Cheong Sou, 2023. "Lithium-Ion Battery State of Health Estimation Using Simple Regression Model Based on Incremental Capacity Analysis Features," Energies, MDPI, vol. 16(20), pages 1-20, October.
    6. Guo, Wenchao & Yang, Lin & Deng, Zhongwei & Li, Jilin & Bian, Xiaolei, 2023. "Rapid online health estimation for lithium-ion batteries based on partial constant-voltage charging segment," Energy, Elsevier, vol. 281(C).
    7. Yang, Jufeng & Li, Xin & Sun, Xiaodong & Cai, Yingfeng & Mi, Chris, 2023. "An efficient and robust method for lithium-ion battery capacity estimation using constant-voltage charging time," Energy, Elsevier, vol. 263(PB).
    8. Zhu, Rui & Duan, Bin & Zhang, Chenghui & Gong, Sizhao, 2019. "Accurate lithium-ion battery modeling with inverse repeat binary sequence for electric vehicle applications," Applied Energy, Elsevier, vol. 251(C), pages 1-1.
    9. Bizhong Xia & Zhen Sun & Ruifeng Zhang & Zizhou Lao, 2017. "A Cubature Particle Filter Algorithm to Estimate the State of the Charge of Lithium-Ion Batteries Based on a Second-Order Equivalent Circuit Model," Energies, MDPI, vol. 10(4), pages 1-15, April.

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