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A scaling approach for improved state of charge representation in rechargeable batteries

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  • Ahmed, Mostafa Shaban
  • Raihan, Sheikh Arif
  • Balasingam, Balakumar

Abstract

State of charge estimation is one of the key elements in battery management systems. Accurate estimation of state of charge in real time is crucial in many applications such as in electric vehicles and aerospace systems. As a result, state of charge modeling and real-time state of charge tracking remain active topics in the battery management systems research domain. One of the key steps in real-time state of charge estimation is the representation of the open circuit voltage as a parametrized function of the state of charge – these parameters will later be used in real-time state of charge estimation based on instantaneous voltage and current measurements. The accuracy of a real-time state of charge estimation scheme is built on the assumption that the open circuit voltage curve is error free. In this paper, we show an example where most of the traditional open circuit voltage characterization approaches would result in up to 10% worst-case state of charge error. Then we present a scaling approach that can reduce this worst-case modeling error to less than 1%. Later, we demonstrate how the proposed scaling approach can be incorporated in real-time state of charge estimation methods, such as the extended Kalman filter based ones. The proposed methods are demonstrated on data collected from nine different battery cells at 16 different temperatures ranging from -25°C to 50°C.

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  • Ahmed, Mostafa Shaban & Raihan, Sheikh Arif & Balasingam, Balakumar, 2020. "A scaling approach for improved state of charge representation in rechargeable batteries," Applied Energy, Elsevier, vol. 267(C).
    • Handle: RePEc:eee:appene:v:267:y:2020:i:c:s0306261920303925
    DOI: 10.1016/j.apenergy.2020.114880
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    References listed on IDEAS

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    Cited by:

    1. Kiarash Movassagh & Arif Raihan & Balakumar Balasingam & Krishna Pattipati, 2021. "A Critical Look at Coulomb Counting Approach for State of Charge Estimation in Batteries," Energies, MDPI, vol. 14(14), pages 1-33, July.
    2. Sneha Sundaresan & Bharath Chandra Devabattini & Pradeep Kumar & Krishna R. Pattipati & Balakumar Balasingam, 2022. "Tabular Open Circuit Voltage Modelling of Li-Ion Batteries for Robust SOC Estimation," Energies, MDPI, vol. 15(23), pages 1-23, December.
    3. Yonghong Xu & Cheng Li & Xu Wang & Hongguang Zhang & Fubin Yang & Lili Ma & Yan Wang, 2022. "Joint Estimation Method with Multi-Innovation Unscented Kalman Filter Based on Fractional-Order Model for State of Charge and State of Health Estimation," Sustainability, MDPI, vol. 14(23), pages 1-25, November.
    4. Wei, Zhongbao & Hu, Jian & Li, Yang & He, Hongwen & Li, Weihan & Sauer, Dirk Uwe, 2022. "Hierarchical soft measurement of load current and state of charge for future smart lithium-ion batteries," Applied Energy, Elsevier, vol. 307(C).
    5. Balakumar Balasingam & Mostafa Ahmed & Krishna Pattipati, 2020. "Battery Management Systems—Challenges and Some Solutions," Energies, MDPI, vol. 13(11), pages 1-19, June.

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