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<p>High-power lithium-ion battery (LIB) applications, such as electric racing cars and electric vertical take-off and landing (eVTOL) aircrafts, are growing rapidly. Degradation in LIBs such as lithium plating, particle cracking, and SEI breakdown is accelerated at high C-rate at different temperatures and depth-of-discharges (DOD); however, high-power cells are designed to better withstand these operating conditions as compared to high-energy cells. Despite this, publicly available datasets of high-power batteries are limited. In this work, we present a characterization dataset of 12 high-power NMC cells which includes capacity tests, high C-rate pulse tests, and impedance tests, all of which are conducted at&#xa0;temperature set points of 5 °C, 25 °C, and 40 °C. Additionally, the dataset captures cell-to-cell variations, enabling the development of stochastic battery models that account for parameter uncertainty and its impact on the cell terminal voltage.</p>

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High-power lithium-ion battery characterization dataset for stochastic battery modeling

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年8月28日

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  • Muhammad Aadil Khan,
  • Sai Thatipamula,
  • Luigi Tresca,
  • Le Xu,
  • Amalie Trewartha,
  • Simona Onori

摘要

High-power lithium-ion battery (LIB) applications, such as electric racing cars and electric vertical take-off and landing (eVTOL) aircrafts, are growing rapidly. Degradation in LIBs such as lithium plating, particle cracking, and SEI breakdown is accelerated at high C-rate at different temperatures and depth-of-discharges (DOD); however, high-power cells are designed to better withstand these operating conditions as compared to high-energy cells. Despite this, publicly available datasets of high-power batteries are limited. In this work, we present a characterization dataset of 12 high-power NMC cells which includes capacity tests, high C-rate pulse tests, and impedance tests, all of which are conducted at temperature set points of 5 °C, 25 °C, and 40 °C. Additionally, the dataset captures cell-to-cell variations, enabling the development of stochastic battery models that account for parameter uncertainty and its impact on the cell terminal voltage.