Optimal energy-dissipation control for SOC based balancing in series connected Lithium-ion battery packs

  • Yunlong Zhang
  • Yuan HongEmail author
  • Ken Choi


Since the state-of-charge (SOC) based balancing can prolong the battery pack’s life and maximize its capacity, implementing the balancing process in the battery management system (BMS) can explicitly reduce the cost of the battery based energy-storage-system (ESS). With the same initial SOC distribution, different balancing topologies may lead to different amounts of energy transferred in the balancing process due to the energy transferring efficiency of equalizers. Furthermore, the more energy equalizers transfer, the more energy and time balancing process will consume. Thus, instead of solely minimizing the balancing time for balancing optimization, it is critical to take into account the energy dissipation of balancing process. This paper presents a novel balancing optimization method from the perspective of minimizing the energy dissipation of balancing process. First, we introduce a two-step balancing strategy in the fast charging system. This strategy can simplify the modular design of the balancing system, and ensure the safety performance of the fast charging system while maximizing the balancing current. Second, an effective system modeling method is proposed for different balancing topologies. With different initial SOC distributions, we simulate the energy dissipation for different balancing topologies. Simulation results are presented to show that the cascade and cascade-series connection topologies would achieve a balanced state in the optimal energy dissipation.


Lithium-ion battery Energy dissipation SOC distribution Balancing topology and system modeling 



We thank our colleagues from KETI and KEIT who provided insight and expertise that greatly assisted the research and greatly improved the manuscript. This work is supported by the Industrial Core Technology Development Program of MOTIE/KEIT, KOREA. [#10083639, Development of Camera-based Real-time Artificial Intelligence System for Detecting Driving Environment & Recognizing objects on Road Simultaneously]


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© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Electrical and Computer EngineeringIllinois Institute of TechnologyChicagoUSA
  2. 2.Computer ScienceIllinois Institute of TechnologyChicagoUSA

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