通过红外热点动态提前预警电池机械滥用引发的热失控，比传统方法早4秒响应。
Infrared Hotspot-Guided Early Warning of Lithium-Ion Battery Thermal Runaway Under Mechanical Abuse
- 用红外热点变化预测局部热失稳，作为早期预警信号。
- 两阶段模型在20帧前预警，准确率达90.8% ROC-AUC。
- 热点升温比电压变化早40帧（4秒），适合电池安全系统部署。
机械滥用会通过局部发热触发锂离子电池热失控，而传感器信号尚未明显时已存在热失稳迹象。本文提出两阶段早期预警方法：第一阶段基于红外热点动态估计局部热失稳，第二阶段融合机械、电学、热学及图像强度特征，在20帧内实现预警。采用实验级三折交叉验证，训练第二阶段时使用第一阶段的留出分数以避免模型过拟合。仅靠热点动态即达第一阶段ROC-AUC 0.945；两阶段分类器第二阶段达ROC-AUC 0.908，优于直接多模态融合，且保留可解释的中间失稳信号。热梯度上升平均比电压检测早40帧（4秒），支持电池管理系统更早干预。固定阈值下平均领先14.8帧。
原文摘要 · Abstract (English)
Mechanical abuse can trigger thermal runaway (TR) in lithium-ion batteries through localized heat generation before sensor signals become decisive. This paper proposes a two-stage early-warning approach that estimates localized thermal instability from infrared hotspot dynamics and then fuses this instability score with mechanical, electrical, thermal, and image-intensity features for a 20-frame warning horizon. Evaluation uses repeated experiment-wise three-fold validation, with out-of-fold Stage-I scores during Stage-II training to prevent stacked-model optimism. Hotspot dynamics alone achieve Stage-I ROC-AUC 0.945, and the two-stage classifier reaches Stage-II ROC-AUC 0.908, exceeding direct multimodal fusion while preserving an interpretable intermediate instability signal. Thermal gradient rise precedes voltage-based detection by 40 frames (4 seconds) on average, enabling earlier battery management system intervention. Lead-time analysis at a fixed 0.5 threshold yields a 14.8-frame mean lead time.
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