运输箱内的镍钴锰酸锂（NCM）电池因其高能量密度，且热失控中会产生大量危害性气体，导致运输事故风险更为突出。本文将长短期记忆神经网络（LSTM）和非接触式传感技术相结合，研究运输箱内NCM电池热失控特性及预警方法。首先，搭建运输箱热失控实验平台，选取50% SOC的NCM单体电池及模组电池为实验对象，开展加热触发条件下的热失控实验，对比了2种动力锂电池在运输箱内的热失控特性；其次，基于LSTM构建了融合电化学气体（H₂、CO）传感器与红外温度传感器的多源非接触式传感动态预警算法，分析多种非接触式传感器的监测预警效果。结果表明：NCM模组热失控时间较单体电池热失控时间可更快地监测运输过程中的动力锂电池安全状态。针对运输对象为NCM单体及模组，该模型提出了风险概率评判标准，对不同运输对象均实现了精准的早期识别，平均提前预警时间达120 s。研究将为道路运输过程中的NCM动力锂电池运输风险管控提供重要参考。

Objective: Nickel–cobalt–manganese oxide (NCM) lithium-ion batteries are widely used in electric vehicles because of their high energy density. However, external heating, mechanical damage, internal short circuits, or other abnormal conditions can trigger thermal runaway, resulting in rapid temperature rise, fire, explosion, and the release of hazardous gases such as hydrogen (H₂) and carbon monoxide (CO). Inside a closed transport container, heat and gases may accumulate rapidly, increasing transportation risks. However, conventional monitoring methods, which mainly rely on battery voltage, current, or contact temperature measurements, are difficult to directly apply to transported batteries. This study therefore investigates the thermal runaway characteristics of NCM cells and modules in a closed transport container and develops a long short-term memory (LSTM)-based early-warning method using multisource, noncontact sensor data. Methods: A thermal runaway experimental platform in a transport container was established, and NCM single cells and battery modules at 50% state of charge were selected as the experimental objects. Heating-triggered thermal runaway experiments were conducted under comparable conditions, and the valve-opening time, thermal runaway time, temperature variation, and characteristic gas release of the two battery forms were analyzed. Noncontact monitoring was achieved using electrochemical H₂ and CO sensors and an infrared temperature sensor installed inside the container. The gas sensors detected characteristic gases generated by battery decomposition reactions, whereas the infrared sensor monitored battery temperature changes. The collected infrared temperature, H₂, and CO time-series data were input simultaneously into an LSTM neural network, which extracted temporal correlations and synergistic changes among the multisource sensor signals. Based on these outputs, a risk probability criterion was proposed to determine whether the transported battery was in a thermal runaway risk state; the model output was used for binary risk-state identification rather than for classifying multiple risk levels or different thermal runaway stages. Results: The experimental results showed clear differences between the thermal runaway behaviors of the NCM cell and module. The module’s valve-opening and thermal runaway times were 770 and 896 s, respectively, which were earlier than the corresponding times of 890 and 1 037 s for the single cell. Owing to thermal interactions within the module, its thermal runaway onset and maximum temperatures were 98.0 ℃ and 672.8 ℃, respectively, which were lower than the corresponding values of 139.8 ℃ and 734.5 ℃ for the single cell. These results indicated that thermal interactions among cells substantially changed the module’s thermal runaway evolution, accelerating triggering and causing the module to enter a hazardous state earlier than the single cell. Therefore, NCM modules presented more prominent thermal runaway risks and imposed higher requirements on early-warning technologies. Infrared temperature and electrochemical gas sensors showed good detection capability during the early development of thermal runaway, with infrared temperature reflecting the external thermal response of the batteries and H₂ and CO signals reflecting the internal gas-generating reactions. Together, these combined signals provided complementary information on battery temperature and gas release and were suitable for monitoring NCM cells and modules under transportation conditions. By integrating temporal changes in infrared temperature, H₂, and CO signals and analyzing the synergistic variations in temperature gradients and characteristic gas concentrations, the LSTM-based multisource early-warning model accurately identified the thermal runaway risk state of NCM cells or modules. The model achieved an average warning lead time of 120 s, providing additional time for vehicle stopping, cargo isolation, cooling, fire suppression, and personnel evacuation. Conclusions: Thermal interactions within an NCM module substantially accelerate thermal runaway development in a closed transport container. Compared with a single cell, the module exhibits earlier valve opening and thermal runaway, as well as lower thermal runaway onset and maximum temperatures. Noncontact infrared temperature and electrochemical gas sensors can effectively monitor the thermal and gas-release characteristics of NCM batteries during transportation. The proposed LSTM-based method, which integrates multisource sensor data and determines thermal runaway risk using a risk probability criterion, enables accurate early warning for NCM cells and modules. The results provide a reference for intelligent monitoring and emergency response during the road transportation of NCM lithium-ion batteries.