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A New Data-Stream-Mining-Based Battery Equalization Method

Author

Listed:
  • Cheng Lin

    (National Engineering Laboratory for Electric Vehicles, Collaborative Innovation Center of Electric Vehicles in Beijing, Beijing Institute of Technology, Beijing 100081, China)

  • Hao Mu

    (National Engineering Laboratory for Electric Vehicles, Collaborative Innovation Center of Electric Vehicles in Beijing, Beijing Institute of Technology, Beijing 100081, China)

  • Li Zhao

    (National Engineering Laboratory for Electric Vehicles, Collaborative Innovation Center of Electric Vehicles in Beijing, Beijing Institute of Technology, Beijing 100081, China)

  • Wanke Cao

    (National Engineering Laboratory for Electric Vehicles, Collaborative Innovation Center of Electric Vehicles in Beijing, Beijing Institute of Technology, Beijing 100081, China)

Abstract

Balancing battery cells is a key task for battery management systems (BMS). Imbalances of cells decrease the capacity and lifetime of the battery pack. Many balancing topologies and strategies have been proposed to balance the electric charges among cells and most of the intelligent control strategies select cells (to shuttle charges) by comparing their terminal voltages. However, the nature of battery equalization is to balance the energy stored in individual cells. The measured terminal voltage is just an external characteristic and cannot accurately reflect the state of charge (SOC) of the cell, especially in a noisy environment. Additionally, when the consistencies of cells are very poor, balancing the cells with terminal voltages will lead to serious errors. In this paper, we introduced a novel battery balancing method, in which the charge-balancing criterion was not the cell voltage, but the shuttling capacities among cells. Data stream mining (DSM) technique was used to calculate the shuttling capacities. A single switched capacitor (SSC) based cell balancing topology was used to test the performance of the proposed method. With the obtained summary information, the cells, the sequence, and the quantity of the equalized charge can be decided automatically by the proposed algorithm. The simulation and experiment results have shown that the proposed method was effective and convenient.

Suggested Citation

  • Cheng Lin & Hao Mu & Li Zhao & Wanke Cao, 2015. "A New Data-Stream-Mining-Based Battery Equalization Method," Energies, MDPI, vol. 8(7), pages 1-23, June.
    • Handle: RePEc:gam:jeners:v:8:y:2015:i:7:p:6543-6565:d:51863
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    References listed on IDEAS

    as
    1. Mohamed Daowd & Mailier Antoine & Noshin Omar & Philippe Lataire & Peter Van Den Bossche & Joeri Van Mierlo, 2014. "Battery Management System—Balancing Modularization Based on a Single Switched Capacitor and Bi-Directional DC/DC Converter with the Auxiliary Battery," Energies, MDPI, vol. 7(5), pages 1-41, April.
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    Cited by:

    1. Shing-Lih Wu & Hung-Cheng Chen & Chih-Hsuan Chien, 2019. "A Novel Active Cell Balancing Circuit and Charging Strategy in Lithium Battery Pack," Energies, MDPI, vol. 12(23), pages 1-17, November.
    2. Xintian Liu & Yafei Sun & Yao He & Xinxin Zheng & Guojian Zeng & Jiangfeng Zhang, 2017. "Battery Equalization by Fly-Back Transformers with Inductance, Capacitance and Diode Absorbing Circuits," Energies, MDPI, vol. 10(10), pages 1-16, September.
    3. Jianwen Cao & Bizhong Xia & Jie Zhou, 2021. "An Active Equalization Method for Lithium-ion Batteries Based on Flyback Transformer and Variable Step Size Generalized Predictive Control," Energies, MDPI, vol. 14(1), pages 1-25, January.
    4. Shun-Chung Wang & Chun-Yu Liu & Yi-Hua Liu, 2018. "A Fast Equalizer with Adaptive Balancing Current Control," Energies, MDPI, vol. 11(5), pages 1-15, April.

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