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Comparative Analysis of Battery Behavior with Different Modes of Discharge for Optimal Capacity Sizing and BMS Operation

Author

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  • Mazhar Abbas

    (Energy and Power Conversion Engineering, University of Science and Technology, Daejeon 34113, Korea)

  • Eung-sang Kim

    (Korea Electrotechnology Research Institute, Changwon 51543, Korea)

  • Seul-ki Kim

    (Korea Electrotechnology Research Institute, Changwon 51543, Korea)

  • Yun-su Kim

    (Korea Electrotechnology Research Institute, Changwon 51543, Korea)

Abstract

Battery-operated systems are always concerned about the proper management and sizing of a battery. A Traditional Battery Management System (BMS) only includes battery-aware task scheduling based on the discharge characteristics of a whole battery pack and do not take into account the mode of the load being served by the battery. On the other hand, an efficient and intelligent BMS should monitor the battery at a cell level and track the load with significant consideration of the load mode. Depending upon the load modes, the common modes of discharge (MOD) of a battery identified so far are Constant Power Mode (CPM), Constant Current Mode (CCM) and Constant Impedance Mode (CIM). This paper comparatively analyzes the discharging behavior of batteries at an individual cell level for different load modes. The difference in discharging behavior from mode to mode represents the study of the mode-dependent behavior of the battery before its deployment in some application. Based on simulation results, optimal capacity sizing and BMS operation of battery for an assumed situation in a remote microgrid has been proposed.

Suggested Citation

  • Mazhar Abbas & Eung-sang Kim & Seul-ki Kim & Yun-su Kim, 2016. "Comparative Analysis of Battery Behavior with Different Modes of Discharge for Optimal Capacity Sizing and BMS Operation," Energies, MDPI, vol. 9(10), pages 1-19, October.
  • Handle: RePEc:gam:jeners:v:9:y:2016:i:10:p:812-:d:80284
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    References listed on IDEAS

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    1. Vijayanarasimha Hindupur Pakka & Richard Mark Rylatt, 2016. "Design and Analysis of Electrical Distribution Networks and Balancing Markets in the UK: A New Framework with Applications," Energies, MDPI, vol. 9(2), pages 1-20, February.
    2. Lan-Rong Dung & Hsiang-Fu Yuan & Jieh-Hwang Yen & Chien-Hua She & Ming-Han Lee, 2016. "A Lithium-Ion Battery Simulator Based on a Diffusion and Switching Overpotential Hybrid Model for Dynamic Discharging Behavior and Runtime Predictions," Energies, MDPI, vol. 9(1), pages 1-21, January.
    3. Guido Carpinelli & Anna Rita Di Fazio & Shahab Khormali & Fabio Mottola, 2014. "Optimal Sizing of Battery Storage Systems for Industrial Applications when Uncertainties Exist," Energies, MDPI, vol. 7(1), pages 1-20, January.
    4. Chao Lu & Hanchen Xu & Xin Pan & Jie Song, 2014. "Optimal Sizing and Control of Battery Energy Storage System for Peak Load Shaving," Energies, MDPI, vol. 7(12), pages 1-15, December.
    5. Jing Li & Wei Wei & Ji Xiang, 2012. "A Simple Sizing Algorithm for Stand-Alone PV/Wind/Battery Hybrid Microgrids," Energies, MDPI, vol. 5(12), pages 1-17, December.
    6. Yinjiao Xing & Eden W. M. Ma & Kwok L. Tsui & Michael Pecht, 2011. "Battery Management Systems in Electric and Hybrid Vehicles," Energies, MDPI, vol. 4(11), pages 1-18, October.
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    1. Han, Seungyun & Kobla Tagayi, Roland & Kim, Jaewon & Kim, Jonghoon, 2022. "Adaptive deterministic approach for optimized sizing of high-energy battery system applied in electric-powered application," Applied Energy, Elsevier, vol. 309(C).

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