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Long-Term Sweat Testing Dataset for Second-Life Batteries.

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This study presents a unique long-term dataset of second-life lithium-ion batteries from electric vehicles. The data supports research into battery degradation and performance prediction for aged cells in energy storage.

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Area of Science:

  • Materials Science
  • Electrochemistry
  • Energy Storage Systems

Background:

  • Electric vehicles (EVs) utilize lithium-ion batteries that have a finite lifespan in automotive applications.
  • Repurposing these "first-life" EV batteries for stationary energy storage represents a "second-life" application, promoting sustainability.
  • Existing datasets often focus on new or first-life cells, lacking data on aged, second-life batteries.

Purpose of the Study:

  • To introduce and describe a comprehensive, long-term cycling dataset exclusively featuring second-life lithium-ion batteries.
  • To provide a valuable resource for researchers investigating battery degradation, state of health (SOH) prediction, and performance benchmarking of aged cells.
  • To support the development and validation of models for second-life battery applications in stationary energy storage.

Main Methods:

  • Collected long-term cycling data from repurposed EV lithium-ion batteries across six distinct stationary energy storage use cases.
  • Utilized a Chroma 17020 battery cycler for data acquisition over an extended period (2019-2025).
  • Processed and cleaned raw, continuous log files, including handling missing values and segmenting data into individual cycles.

Main Results:

  • Generated a unique dataset containing no prior usage information, focusing solely on the second-life performance of batteries.
  • The dataset captures intermittent cycling data over a prolonged duration, offering insights into long-term degradation patterns.
  • Successfully organized and preserved the integrity of the experimental data for research purposes.

Conclusions:

  • The presented dataset is a crucial resource for advancing the understanding of second-life lithium-ion battery behavior.
  • It enables in-depth analysis of battery ageing mechanisms and facilitates the development of accurate SOH prediction models for repurposed batteries.
  • This work supports both academic and industrial efforts in optimizing the utilization and management of second-life energy storage solutions.