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A Consensus Algorithm for Multi-Objective Battery Balancing

Author

Listed:
  • Jorge Varela Barreras

    (Department of Mechanical Engineering, Imperial College London, London SW7 1AY, UK
    The Faraday Institution, Didcot OX11 0RA, UK)

  • Ricardo de Castro

    (Department of Mechanical Engineering, University of California, Merced, CA 95343, USA)

  • Yihao Wan

    (Department of Electrical Engineering, Technical University of Denmark, 2800 Kongens Lyngby, Denmark)

  • Tomislav Dragicevic

    (Department of Electrical Engineering, Technical University of Denmark, 2800 Kongens Lyngby, Denmark)

Abstract

Batteries stacks are made of cells in certain series-parallel arrangements. Unfortunately, cell performance degrades over time in terms of capacity, internal resistance, or self-discharge rate. In addition, degradation rates are heterogeneous, leading to cell-to-cell variations. Balancing systems can be used to equalize those differences. Dissipative or non-dissipative systems, so-called passive or active balancing, can be used to equalize either voltage at end-of-charge, or state-of-charge (SOC) at all times. While passive balancing is broadly adopted by industry, active balancing has been mostly studied in academia. Beyond that, an emerging research field is multi-functional balancing, i.e., active balancing systems that pursue additional goals on top of SOC equalization, such as equalization of temperature, power capability, degradation rates, or losses minimization. Regardless of their functionality, balancing circuits are based either on centralized or decentralized control systems. Centralized control entails difficult expandability and single point of failure issues, while decentralized control has severe controllability limitations. As a shift in this paradigm, here we present for the first time a distributed multi-objective control algorithm, based on a multi-agent consensus algorithm. We implement and validate the control in simulations, considering an electro-thermal lithium-ion battery model and an electric vehicle model parameterized with experimental data. Our results show that our novel multi-functional balancing can enhance the performance of batteries with substantial cell-to-cell differences under the most demanding operating conditions, i.e., aggressive driving and DC fast charging (2C). Driving times are extended (>10%), charging times are reduced (>20%), maximum cell temperatures are decreased (>10 °C), temperature differences are lowered (~3 °C rms), and the occurrence of low voltage violations during driving is reduced (>5×), minimizing the need for power derating and enhancing the user experience. The algorithm is effective, scalable, flexible, and requires low implementation and tuning effort, resulting in an ideal candidate for industry adoption.

Suggested Citation

  • Jorge Varela Barreras & Ricardo de Castro & Yihao Wan & Tomislav Dragicevic, 2021. "A Consensus Algorithm for Multi-Objective Battery Balancing," Energies, MDPI, vol. 14(14), pages 1-25, July.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:14:p:4279-:d:594900
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    References listed on IDEAS

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    1. Yongquan Sun & Saurabh Saxena & Michael Pecht, 2018. "Derating Guidelines for Lithium-Ion Batteries," Energies, MDPI, vol. 11(12), pages 1-19, November.
    2. Pelletier, Samuel & Jabali, Ola & Laporte, Gilbert & Veneroni, Marco, 2017. "Battery degradation and behaviour for electric vehicles: Review and numerical analyses of several models," Transportation Research Part B: Methodological, Elsevier, vol. 103(C), pages 158-187.
    3. Arnaud Devie & George Baure & Matthieu Dubarry, 2018. "Intrinsic Variability in the Degradation of a Batch of Commercial 18650 Lithium-Ion Cells," Energies, MDPI, vol. 11(5), pages 1-14, April.
    4. Yu Sui & Shiming Song, 2020. "A Multi-Agent Reinforcement Learning Framework for Lithium-ion Battery Scheduling Problems," Energies, MDPI, vol. 13(8), pages 1-13, April.
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    Cited by:

    1. João Pedro F. Trovão & Minh Cao Ta, 2022. "Electric Vehicle Efficient Power and Propulsion Systems," Energies, MDPI, vol. 15(11), pages 1-4, May.

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