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Design and implementation of a battery management system with active charge balance based on the SOC and SOH online estimation

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  • Ren, Hongbin
  • Zhao, Yuzhuang
  • Chen, Sizhong
  • Wang, Taipeng

Abstract

the power delivery performance of series and parallel strings connected battery modules or packs is restricted by the worst cells in the string. Each cell has a slightly different capacity and terminal voltage due to manufacturing tolerances and operating conditions. The cell strings tend to loss balance during charging and discharging process. The motivation of this paper is to develop a battery management system (BMS) to monitor and control the temperature, state of charge (SOC) and state of health (SOH) et al. and to increase the efficiency of rechargeable batteries. An active energy balancing system for Lithium-ion battery pack is designed based on the online SOC and SOH estimation. The remainder capacity of the battery is estimated by measuring the terminal voltage for each cell, and the balance system will be triggered when the difference between the SOC of one cell and the average SOC is more or less than a predefined threshold in order to minimize the output voltage ripple. The simulation results indicate that the designed BMS can precisely synchronize the SOC while minimizing the output voltage ripple.

Suggested Citation

  • Ren, Hongbin & Zhao, Yuzhuang & Chen, Sizhong & Wang, Taipeng, 2019. "Design and implementation of a battery management system with active charge balance based on the SOC and SOH online estimation," Energy, Elsevier, vol. 166(C), pages 908-917.
    • Handle: RePEc:eee:energy:v:166:y:2019:i:c:p:908-917
    DOI: 10.1016/j.energy.2018.10.133
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    References listed on IDEAS

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    3. Dong, Guangzhong & Zhang, Xu & Zhang, Chenbin & Chen, Zonghai, 2015. "A method for state of energy estimation of lithium-ion batteries based on neural network model," Energy, Elsevier, vol. 90(P1), pages 879-888.
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