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Recovering large-scale battery aging dataset with machine learning.

Xiaopeng Tang1, Kailong Liu2, Kang Li3

  • 1Department of Chemical and Biological Engineering, Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong SAR 999077, China.

Patterns (New York, N.Y.)
|August 25, 2021
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Summary

This study uses machine learning to create battery aging datasets from industrial data, significantly reducing experimental time and costs. This approach recovers high-quality battery health data with minimal error, addressing data shortages for energy storage assessment.

Keywords:
accelerated battery aging experimentsbattery aging assessmentbattery aging dataset generationincremental capacity analysislithium-ion battery managementmachine learningmodel migration

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Area of Science:

  • Energy Storage Systems
  • Materials Science
  • Machine Learning Applications

Background:

  • Battery performance is critical for sustainable energy, but health assessment is hindered by insufficient aging data due to long experimental durations.
  • Industrial datasets, such as those from electric vehicle batteries, offer a large-scale data source but require refinement for accurate aging analysis.

Purpose of the Study:

  • To develop a method for generating high-quality battery aging datasets by combining industrial data with accelerated aging tests.
  • To address the challenge of limited data availability for reliable battery health assessment and energy storage system evaluation.

Main Methods:

  • A migration-based machine learning approach was employed to recover battery aging data.
  • Industrial data was integrated with accelerated aging tests to create a comprehensive dataset.
  • A dataset comprising 8,947 aging cycles across 15 operational modes was collected and evaluated.

Main Results:

  • The proposed method successfully recovered battery aging data with an ultra-low error rate of less than 1%.
  • Experimental time for data acquisition was reduced by up to 90% compared to traditional methods.
  • The approach demonstrated the potential to significantly alleviate data shortage issues in battery aging research.

Conclusions:

  • Combining industrial data with accelerated aging tests via migration-based machine learning offers an efficient solution for creating extensive, high-quality battery aging datasets.
  • This methodology provides a viable alternative to overcome the limitations of traditional aging experiments, improving battery health assessment.
  • The findings support the advancement of battery and energy storage system diagnostics and prognostics through enhanced data availability.