机械滥用下锂离子电池热失控的红外热点引导预警
Infrared Hotspot-Guided Early Warning of Lithium-Ion Battery Thermal Runaway Under Mechanical Abuse
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中文总结 AI 辅助
针对机械滥用下锂离子电池热失控预警问题，提出两阶段融合红外热点动态与多模态特征的方法，经实验验证可实现更早的预警与更高的ROC-AUC。
中文摘要 AI 辅助
机械滥用可在传感器信号变得具有决定性前，通过局部产热触发锂离子电池热失控（TR）。本文提出一种两阶段预警方法，该方法从红外热点动态估计局部热不稳定性，随后将该不稳定性评分与机械、电气、热学及图像强度特征融合，实现20帧的预警窗口。评估采用重复实验式三折验证，阶段II训练期间使用折外的阶段I评分以避免堆叠模型的乐观偏差。仅热点动态即可达到阶段I ROC-AUC 0.945，两阶段分类器达到阶段II ROC-AUC 0.908，优于直接多模态融合，同时保留可解释的中间不稳定性信号。热梯度上升平均早于基于电压的检测40帧（4秒），使电池管理系统可更早干预。在固定0.5阈值下的提前期分析得出平均提前期为14.8帧。
英文摘要
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.
