Scholarlink

<p>This study provides a multi-modal thermal runaway (TR) dataset for pristine and aged lithium-ion battery cells and modules. The novel specialized experimental frameworks, including an airtight canister and force measurement testbench, were designed to capture thermal and mechanical dynamics during TR. Rigorous uncertainty analysis validated the reliability of the experimental setup and dataset. The dataset comprises synchronized temperature, pressure, and force evolution data across nickel-manganese-cobalt (NMC) cylindrical, pouch cells and pouch modules for fresh and aged state of health (SOH). This multi-modal dataset characterizes SOH-dependent TR kinetics by integrating internal peak pressure, venting timing, and intervals from venting to peak expansion force. Consequently, this new dataset enables the systematic parametrization of SOH-informed TR models across scales from cell to modules, facilitating high-fidelity design-enabling solutions for real-world applications by providing critical metrics for the optimization degradation-informed mitigation protocols for lithium-ion battery systems.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Multi-modal thermal runaway dataset of fresh and aged lithium-ion battery cells and modules

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年7月15日

查看全文
  • Eunji Kwak,
  • Jinho Jeong,
  • Jun Hyeong Kim,
  • Yeongjin Shin,
  • Moonwoo Park,
  • Ki-Yong Oh

摘要

This study provides a multi-modal thermal runaway (TR) dataset for pristine and aged lithium-ion battery cells and modules. The novel specialized experimental frameworks, including an airtight canister and force measurement testbench, were designed to capture thermal and mechanical dynamics during TR. Rigorous uncertainty analysis validated the reliability of the experimental setup and dataset. The dataset comprises synchronized temperature, pressure, and force evolution data across nickel-manganese-cobalt (NMC) cylindrical, pouch cells and pouch modules for fresh and aged state of health (SOH). This multi-modal dataset characterizes SOH-dependent TR kinetics by integrating internal peak pressure, venting timing, and intervals from venting to peak expansion force. Consequently, this new dataset enables the systematic parametrization of SOH-informed TR models across scales from cell to modules, facilitating high-fidelity design-enabling solutions for real-world applications by providing critical metrics for the optimization degradation-informed mitigation protocols for lithium-ion battery systems.