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Management System for Large Li-Ion Battery Packs with a New Adaptive Multistage Charging Method

Author

Listed:
  • Ricardo Velho

    (Instituto de Telecomunicações, Universidade da Beira Interior, 6201-001 Covilhã, Portugal)

  • Miguel Beirão

    (Instituto de Telecomunicações, Universidade da Beira Interior, 6201-001 Covilhã, Portugal)

  • Maria Do Rosário Calado

    (Instituto de Telecomunicações, Universidade da Beira Interior, 6201-001 Covilhã, Portugal)

  • José Pombo

    (Instituto de Telecomunicações, Universidade da Beira Interior, 6201-001 Covilhã, Portugal)

  • João Fermeiro

    (Instituto de Telecomunicações, Universidade da Beira Interior, 6201-001 Covilhã, Portugal)

  • Sílvio Mariano

    (Instituto de Telecomunicações, Universidade da Beira Interior, 6201-001 Covilhã, Portugal)

Abstract

Among the wide diversity of existing technologically mature batteries, lithium-ion (Li-ion) batteries have become popular because of their longevity, high energy density, high efficiency and lack of memory effect. Differential charging of cells with age has turned balancing management systems into an important research subject. This paper proposes a new battery management system (BMS) to improve the capacity usage and lifespan of large Li-ion battery packs and a new charging algorithm based on the traditional multistage method. The main advantages of the proposed system are its versatility and ability to implement different charging and balancing methods in a very accessible way. The combination of charging methods with balancing methods represents an evolution when compared with other works in the literature.

Suggested Citation

  • Ricardo Velho & Miguel Beirão & Maria Do Rosário Calado & José Pombo & João Fermeiro & Sílvio Mariano, 2017. "Management System for Large Li-Ion Battery Packs with a New Adaptive Multistage Charging Method," Energies, MDPI, vol. 10(5), pages 1-21, May.
    • Handle: RePEc:gam:jeners:v:10:y:2017:i:5:p:605-:d:97309
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    References listed on IDEAS

    as
    1. Meng Di Yin & Jeonghun Cho & Daejin Park, 2016. "Pulse-Based Fast Battery IoT Charger Using Dynamic Frequency and Duty Control Techniques Based on Multi-Sensing of Polarization Curve," Energies, MDPI, vol. 9(3), pages 1-20, March.
    2. Yuanmao Ye & Ka Wai Eric Cheng, 2016. "An Automatic Switched-Capacitor Cell Balancing Circuit for Series-Connected Battery Strings," Energies, MDPI, vol. 9(3), pages 1-15, February.
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    Cited by:

    1. Chusheng Lu & Longyun Kang & Xuan Luo & Jinqing Linghu & Hongye Lin, 2019. "A Novel Lithium Battery Equalization Circuit with Any Number of Inductors," Energies, MDPI, vol. 12(24), pages 1-14, December.
    2. 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.
    3. Yat Chi Fong & Ka Wai Eric Cheng & S. Raghu Raman & Xiaolin Wang, 2018. "Multi-Port Zero-Current Switching Switched-Capacitor Converters for Battery Management Applications," Energies, MDPI, vol. 11(8), pages 1-17, July.
    4. Robert Salas-Puente & Silvia Marzal & Raul Gonzalez-Medina & Emilio Figueres & Gabriel Garcera, 2018. "Practical Analysis and Design of a Battery Management System for a Grid-Connected DC Microgrid for the Reduction of the Tariff Cost and Battery Life Maximization," Energies, MDPI, vol. 11(7), pages 1-31, July.
    5. João Faria & José Pombo & Maria do Rosário Calado & Sílvio Mariano, 2019. "Power Management Control Strategy Based on Artificial Neural Networks for Standalone PV Applications with a Hybrid Energy Storage System," Energies, MDPI, vol. 12(5), pages 1-24, March.
    6. 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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