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An Analytic Hierarchy Process for Selecting Battery Equalization Methods

Author

Listed:
  • Bruno Martin de Alcântara Dias

    (PEA—Polytechnic School (POLI-USP), São Paulo 05508-010, Brazil)

  • Cynthia Thamires da Silva

    (PEA—Polytechnic School (POLI-USP), São Paulo 05508-010, Brazil)

  • Rui Esteves Araújo

    (INESC TEC and Faculty of Engineering, University of Porto, 4200-465 Porto, Portugal)

  • Ricardo de Castro

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

  • Eduardo Lorenzetti Pellini

    (PEA—Polytechnic School (POLI-USP), São Paulo 05508-010, Brazil)

  • Cláudio Pinto

    (Continental Engineering Services Portugal, Unipessoal Lda, 4200-162 Porto, Portugal)

  • Armando Antônio Maria Laganá

    (PEA—Polytechnic School (POLI-USP), São Paulo 05508-010, Brazil)

Abstract

Batteries have been the predominant energy storage system used in electric vehicles. Battery packs have a large number of cells that develop charge, thermal, and capacity imbalances over time, limiting the power, range, and lifetime. Electronic battery management and state of charge (SoC) equalization methods are necessary to mitigate such imbalances. Today, it is possible to find a wide range of battery equalization methods in the literature, but how to decide which of these methods should be applied in practice? This paper compares 24 SoC equalization circuits that are typically found in automotive applications. We employ an analytic hierarchy process (AHP) approach to rank these equalization circuits according to multiple decision criteria (energy efficiency, equalization speed, implementation and control simplicity, hardware size, and total price). We also prepared a survey to collect design preferences from multiple battery balancing experts from around the world in order to better understand the relative importance of different criteria. The obtained results confirm that automotive engineers continue to favor passive balancing methods because of their low price, small PCB size, and implementation simplicity—despite the energy efficiency benefits of active balancing.

Suggested Citation

  • Bruno Martin de Alcântara Dias & Cynthia Thamires da Silva & Rui Esteves Araújo & Ricardo de Castro & Eduardo Lorenzetti Pellini & Cláudio Pinto & Armando Antônio Maria Laganá, 2022. "An Analytic Hierarchy Process for Selecting Battery Equalization Methods," Energies, MDPI, vol. 15(7), pages 1-21, March.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:7:p:2439-:d:780115
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    References listed on IDEAS

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    1. Pinto, Cláudio & Barreras, Jorge V. & de Castro, Ricardo & Araújo, Rui Esteves & Schaltz, Erik, 2017. "Study on the combined influence of battery models and sizing strategy for hybrid and battery-based electric vehicles," Energy, Elsevier, vol. 137(C), pages 272-284.
    2. Saaty, Thomas L., 1990. "How to make a decision: The analytic hierarchy process," European Journal of Operational Research, Elsevier, vol. 48(1), pages 9-26, September.
    3. Grošelj, Petra & Zadnik Stirn, Lidija, 2012. "Acceptable consistency of aggregated comparison matrices in analytic hierarchy process," European Journal of Operational Research, Elsevier, vol. 223(2), pages 417-420.
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    Cited by:

    1. Jiongjiong Cai & Peng Ke & Xiao Qu & Zihui Wang, 2022. "Research on the Design of Auxiliary Generator for Enthalpy Reduction and Steady Speed Scroll Expander," Energies, MDPI, vol. 15(9), pages 1-17, April.
    2. Xingxing Wang & Peilin Ye & Yujie Zhang & Hongjun Ni & Yelin Deng & Shuaishuai Lv & Yinnan Yuan & Yu Zhu, 2022. "Parameter Optimization Method for Power System of Medium-Sized Bus Based on Orthogonal Test," Energies, MDPI, vol. 15(19), pages 1-26, October.
    3. Denis Pelin & Andrej Brandis & Mario Kovačević & Filip Halak, 2022. "Design and Testing of a Multimode Capable Passive Battery Management System," Energies, MDPI, vol. 15(12), pages 1-21, June.

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