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<p>Lithium-ion battery fires present increasing safety engineering challenges, with U.S. incident rates rising 86% between 2020 and 2023, particularly in micromobility devices and urban environments. We analyzed 148 lithium-ion battery fire incidents documented through the Massachusetts State Fire Marshal’s specialized checklist program, employing logistic regression for charging-related risks, spatial clustering for geographic patterns, and time-series analysis for temporal trends. Consumer electronics showed significantly lower odds of charging-related fires (<i>OR</i> = 0.125–0.148, <i>p</i> &lt; 0.05), while micromobility devices had the highest injury rate (<i>IRR</i> = 2.98, 95% CI: 0.837–10.615). Charging was involved in 40.5% of cases, and online purchases were associated with greater risk (<i>OR</i> = 1.87, 95% CI: 1.23–2.84, <i>p</i> = 0.003). Significant urban clustering was observed (Moran’s <i>I</i> = 0.28, <i>p</i> = 0.003) with three metropolitan hotspots. Device-specific risk profiles demonstrate the effectiveness of existing consumer electronics safety standards and highlight critical intervention points for emerging high-risk devices, particularly in dense urban areas where targeted protection strategies are most needed.</p>

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Device-Specific Fire Risk Analysis of Lithium-Ion Battery Incidents: Engineering Implications for Detection and Protection Systems

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年2月18日

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  • Matthew Bucala,
  • Dac Nguyen,
  • Holli Knight,
  • Adam Barowy

摘要

Lithium-ion battery fires present increasing safety engineering challenges, with U.S. incident rates rising 86% between 2020 and 2023, particularly in micromobility devices and urban environments. We analyzed 148 lithium-ion battery fire incidents documented through the Massachusetts State Fire Marshal’s specialized checklist program, employing logistic regression for charging-related risks, spatial clustering for geographic patterns, and time-series analysis for temporal trends. Consumer electronics showed significantly lower odds of charging-related fires (OR = 0.125–0.148, p < 0.05), while micromobility devices had the highest injury rate (IRR = 2.98, 95% CI: 0.837–10.615). Charging was involved in 40.5% of cases, and online purchases were associated with greater risk (OR = 1.87, 95% CI: 1.23–2.84, p = 0.003). Significant urban clustering was observed (Moran’s I = 0.28, p = 0.003) with three metropolitan hotspots. Device-specific risk profiles demonstrate the effectiveness of existing consumer electronics safety standards and highlight critical intervention points for emerging high-risk devices, particularly in dense urban areas where targeted protection strategies are most needed.