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Adaptive deterministic approach for optimized sizing of high-energy battery system applied in electric-powered application

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  • Han, Seungyun
  • Kobla Tagayi, Roland
  • Kim, Jaewon
  • Kim, Jonghoon

Abstract

For achieving decarbonization globally, most electric applications, such as electric vehicles (EVs) and photovoltaic (PV) generators, are moving toward the use of eco-friendly and renewable energy sources. Currently, one of the most utilized energy storage devices is the lithium-ion battery, which has various advantages than other systems. In addition, a lithium-ion battery presents nonlinear characteristics during operation. Therefore, these features should be considered in the design of battery systems with load requirements. In this paper, energy and power characteristics were analyzed in terms of battery system size in relation to EVs and PV generators. If a battery system is designed to supply power less than the energy demand, it will be utilized beyond the designed and safety range, and there will be generation of waste residual energy. However, if a battery system is designed to supply power more than the demand, additional investment and maintenance cost will be incurred to setup the battery system. Thus, this paper proposes an optimal sizing method for a lithium-ion battery system considering its nonlinear features using Peukert’s law and the analyzed load conditions. To obtain the ideal size of the battery system, an adaptive deterministic approach is implemented. After determining the optimal battery system size, a simulation of the battery system with a load profile is conducted to verify that it satisfies all constraints of the battery system.

Suggested Citation

  • 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).
    • Handle: RePEc:eee:appene:v:309:y:2022:i:c:s0306261921017141
    DOI: 10.1016/j.apenergy.2021.118498
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    2. Li, Shicheng & Xu, Lin & Du, Xiaofang & Wang, Nian & Lin, Feng & Abdelkareem, Mohamed A.A., 2023. "Combined single-pedal and low adhesion control systems for enhanced energy regeneration in electric vehicles: Modeling, simulation, and on-field test," Energy, Elsevier, vol. 269(C).
    3. Mónica Camas-Náfate & Alberto Coronado-Mendoza & Carlos Jesahel Vega-Gómez & Francisco Espinosa-Moreno, 2022. "Modeling and Simulation of a Commercial Lithium-Ion Battery with Charge Cycle Predictions," Sustainability, MDPI, vol. 14(21), pages 1-17, October.
    4. Zhichao Zhao & Lu Li & Yang Ou & Yi Wang & Shaoyang Wang & Jing Yu & Renhua Feng, 2023. "A Comparative Study on the Energy Flow of Electric Vehicle Batteries among Different Environmental Temperatures," Energies, MDPI, vol. 16(14), pages 1-15, July.

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