智能城市应用

基于模块化 LSTM 的隧道锂离子电池热失控火灾温度预测方法研究

叶荣森 (重庆科技大学 安全科学与工程学院), 鲁宁 (重庆科技大学 安全科学与工程学院), 唐婷婷 (重庆科技大学 安全科学与工程学院), 吴笑益 (重庆科技大学 安全科学与工程学院), 李强 (重庆科技大学 安全科学与工程学院), 杜贤坤 (重庆科技大学 安全科学与工程学院), 羊科宇 (重庆科技大学 安全科学与工程学院)

摘要


针对隧道锂离子电池热失控火灾温度演化非线性强、测点差异明显的问题,提出融合空间注意力、多尺度时序提取、温度分布特征及残差校正的模块化LSTM预测方法。基于1∶20缩尺隧道平台,在3类加热装置与多种通风工况下采集顶棚测点温度数据。结果表明,训练覆盖工况的总体R2为0.953,MAE为1.75℃;在独立测试工况AT-100-0.4下,较基础双层LSTM,模型MAE与RMSE分别降低53.64%和68.61%,R2提升至0.894。模型在低温区预测较准确,高温峰值区误差相对较大,可为隧道火灾温度超前预警提供参考。

关键词


隧道火灾;锂离子电池;热失控;温度预测;模块化;LSTM

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参考


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DOI: https://doi.org/10.33142/sca.v9i10.20613

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