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
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Feng X, Ouyang M, Liu X, et al. Thermal runaway mechanism of lithium ion battery for electric vehicles: A review[J].Energy Storage Materials,2018(10):246-267.
张亚军,王贺武,冯旭宁,等.动力锂离子电池热失控燃烧特性研究进展[J].机械工程学报,2019,55(20):17-27.
Larsson F, Andersson P, Blomqvist P, et al. Toxic fluoride gas emissions from lithium-ion battery fires[J].Scientific Reports,2017(7):10018.
Larsson F, Andersson P, Blomqvist P, et al. Characteristics of lithium-ion batteries during fire tests[J].Journal of Power Sources,2014(271):414-420.
Liu P, Liu C, Yang K, et al. Thermal runaway and fire behaviors of lithium iron phosphate battery induced by over heating[J]. Journal of Energy Storage,2020(31):101714.
Liu P, Li Y, Mao B, et al. Experimental study on thermal runaway and fire behaviors of large format lithium iron phosphate battery[J]. Applied Thermal Engineering,2021 (192):116949.
Liu X, Wu Z, Stoliarov S I, et al. Heat release during thermally-induced failure of a lithium ion battery: Impact of cathode composition[J].Fire Safety Journal,2016(85):10-22.
Fu Y, Lu S, Li K, et al. An experimental study on burning behaviors of 18650 lithium ion batteries using a cone calorimeter[J]. Journal of Power Sources,2015(273): 216-222.
何春汕,王子阳,姚斌.不同放电功率下的储能用磷酸铁锂电池热失控特性实验研究[J].储能科学与技术,2024, 13(3):981-989.
Hu L H, Huo R, Chow W K. Studies on buoyancy-driven back-layering flow in tunnel fires[J].Experimental Thermal and Fluid Science,2008, 32(8):1468-1483.
Ingason H, Li Y Z, Lönnermark A. Tunnel Fire Dynamics[M].2nd ed. Cham: Springer,2024.
WU X Q, ZHANG X N, HUANG X Y, et al. A real‑time forecast of tunnel fire based on numerical database and artificial intelligence[J]. Building Simulation,2021,15(3):511‑524.
LIU Y H, YE Z C, CAO Z Y, et al. A dual‑module TCNN–LSTM model with self‑attention for predicting tunnel fires under mobile ventilation control[J].Applied Thermal Engineering,2026(287):129439.
ZHANG X N, WU X Q, HUANG X Y. Smart real‑time forecast of transient tunnel fires by a dual‑agent deep learning model[J].Tunnelling and Underground Space Technology,2022(129):104631.
Zhen Z ,Liang W ,Songlin L , et al.Intelligent fire location detection approach for extrawide immersed tunnels[J].Expert Systems With Applications,2024(239): 15-20.
LIU W, LI T N, SHI C L, et al. Prediction of HRR in tunnel fires under different tunnel inclinations and ambient pressure conditions based on recurrent neural networks[J]. Tunnelling and Underground Space Technology,2026(158): 107371.
贾进章,陈佳琦,陈怡诺.长短期记忆网络在隧道火灾实时致灾态势预测中应用研究[J].安全与环境学报,2025,25(4):1298-1309.
HOCHREITER S, SCHMIDHUBER J. Long short-term memory[J]. Neural Computation,1997,9(8):1735-1780.
Li Y Z, Ingason H. The maximum ceiling gas temperature in a large tunnel fire[J]. Fire Safety Journal, 2012(48):38-48.
Hu P, Zhang Z, Zhang X, et al. An experimental study on the transition velocity and smoke back-layering length induced by carriage fire in a ventilated tunnel[J].Tunnelling and Underground Space Technology,2020(106):103609.
SHIH S Y, SUN F K, LEE H Y. Temporal pattern attention for multivariate time series forecasting[J].Machine Learning,2019,108(8):1421-1441.
HYNDMAN R J, KOEHLER A B. Another look at measures of forecast accuracy[J]. International Journal of Forecasting,2006,22(4):679-688.
ROBERTS D R, BAHN V, CIUTI S, et al. Cross-validation strategies for data with temporal, spatial, hierarchical, or phylogenetic structure[J]. Ecography,2017, 40(8):913-929.
BERGMEIR C, HYNDMAN R J, KOO B. A note on the validity of cross-validation for evaluating autoregressive time series prediction[J].Computational Statistics & Data Analysis,2018(120):70-83.
DOI: https://doi.org/10.33142/sca.v9i10.20613
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