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<p>This paper describes a long-term cycling dataset of repurposed lithium-ion batteries originally used in electric vehicles. After their initial automotive use – referred to as their “first life” – these batteries were redeployed for stationary energy storage applications, representing their “second life”. The dataset covers six distinct use cases modelled to represent real-world energy storage applications. Unlike other published datasets, which focus on new or first-life cells, this work exclusively features second-life batteries with no available data from prior usage. It is aimed at supporting research into battery degradation, state of health prediction, and performance benchmarking under aged conditions. Data was collected using a Chroma 17020 batter cycler and originally stored as large, continuous log files. This paper outlines the steps taken to reorganise and clean the data – handling missing values and segmenting it into individual cycles – while preserving its raw experimental content. Cycling occurred intermittently between 2019 and 2025, offering a uniquely long observation period. The dataset supports both academic and industrial research into battery ageing and second-life applications.</p>

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Long-Term Sweat Testing Dataset for Second-Life Batteries

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年6月23日

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  • Matthew Beatty,
  • Dani Strickland,
  • Joe Warren,
  • John Chan,
  • Pedro Ferreira

摘要

This paper describes a long-term cycling dataset of repurposed lithium-ion batteries originally used in electric vehicles. After their initial automotive use – referred to as their “first life” – these batteries were redeployed for stationary energy storage applications, representing their “second life”. The dataset covers six distinct use cases modelled to represent real-world energy storage applications. Unlike other published datasets, which focus on new or first-life cells, this work exclusively features second-life batteries with no available data from prior usage. It is aimed at supporting research into battery degradation, state of health prediction, and performance benchmarking under aged conditions. Data was collected using a Chroma 17020 batter cycler and originally stored as large, continuous log files. This paper outlines the steps taken to reorganise and clean the data – handling missing values and segmenting it into individual cycles – while preserving its raw experimental content. Cycling occurred intermittently between 2019 and 2025, offering a uniquely long observation period. The dataset supports both academic and industrial research into battery ageing and second-life applications.