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

Area of Science:

• Materials Science
• Electrochemistry
• Energy Storage

Background:

• High-power lithium-ion batteries (LIBs) are critical for electric vehicles and eVTOLs.
• Battery degradation (e.g., lithium plating, particle cracking) is accelerated by high C-rates, temperatures, and depth-of-discharge (DOD).
• Limited public datasets exist for high-power LIB characterization.

Purpose of the Study:

• To present a comprehensive characterization dataset for high-power LIBs.
• To facilitate the development of advanced battery models.
• To enable the study of cell-to-cell variations and their impact on battery performance.

Main Methods:

• Characterization of 12 high-power Nickel Manganese Cobalt (NMC) cells.
• Conducting capacity tests, high C-rate pulse tests, and impedance tests.
• Testing at controlled temperatures: 5 °C, 25 °C, and 40 °C.

Main Results:

• Detailed dataset including performance metrics under varied conditions.
• Quantification of cell-to-cell variations in high-power NMC cells.
• Data suitable for developing stochastic battery models.

Conclusions:

• The presented dataset addresses the scarcity of high-power LIB data.
• Enables more accurate modeling of battery behavior, considering uncertainties.
• Supports advancements in battery management systems for demanding applications.