Development of compact mechanism for lithium-ion battery venting gas fires using Cantera ordinary differential equation neural network algorithm

IF 5 Q2 ENERGY & FUELS
Mengjie Li, Hao Hu, Li Lu, Huangwei Zhang
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引用次数: 0

Abstract

Lithium-ion battery fires pose significant challenges to the development of electric vehicles and energy storage systems due to their potential hazards and complex combustion behavior. To address these issues, this work develops the Cantera Ordinary Differential Equation Neural Network (CODENN) algorithm, which combines the computational power of neural ordinary differential equations with Cantera's advanced chemical kinetics modeling. This integration allows for the optimization of a wide range of chemical reactions, improving both the precision and versatility of reaction mechanism development. Using CODENN, a compact mechanism (COM) was developed by optimizing the Arrhenius parameters of the RED mechanism, which had been overly reduced from the detailed CRECK2003 mechanism (114 species, 1999 reactions). CRECK2003 was chosen for its proven accuracy in predicting the combustion properties of LIB venting gases. The resulting COM mechanism, with 30 species and 213 reactions, achieves a high fidelity of 94.8 % in predicting ignition delay times across equivalence ratios from 0.3 to 2.5, demonstrating the reliability and robustness of CODENN algorithm. Further analysis of speciation data and CO net production rates shows that the COM mechanism closely aligns with the species evolution of the ORI mechanism during autoignition, while exhibiting notably more intense CO production and consumption than the ORI mechanism. Path flux analysis indicates that, despite having shorter reaction chains than the ORI mechanism, the COM mechanism preserves the fundamental physical logic of fuel consumption (CH₄) leading to H₂O formation while introducing additional pathways for the generation and consumption of H and OH radicals. Sensitivity analysis across diverse equivalence ratios and temperatures consistently identifies reaction R5 (H + O₂ ≤> O + OH) as the most temperature-sensitive reaction, underscoring its critical role in reaction kinetics of LIB venting gases.
基于Cantera常微分方程神经网络算法的锂离子电池气体灭火机理研究
锂离子电池火灾由于其潜在的危险和复杂的燃烧行为,给电动汽车和储能系统的发展带来了重大挑战。为了解决这些问题,本研究开发了Cantera常微分方程神经网络(CODENN)算法,该算法将神经常微分方程的计算能力与Cantera先进的化学动力学建模相结合。这种集成允许对广泛的化学反应进行优化,提高反应机制开发的精度和多功能性。利用CODENN,通过优化RED机理的Arrhenius参数,建立了紧凑机理(COM),该机理由详细的CRECK2003机理(114种,1999种反应)简化而成。选择CRECK2003是因为它在预测锂离子电池排放气体的燃烧特性方面具有较好的准确性。所得到的COM机制包含30种物质和213种反应,在0.3 ~ 2.5的等效比范围内预测点火延迟时间的保真度达到94.8%,证明了CODENN算法的可靠性和鲁棒性。进一步的物种形成数据和CO净产率分析表明,COM机制与ORI机制在自燃过程中的物种进化密切相关,同时表现出明显比ORI机制更强烈的CO生产和消耗。通径通量分析表明,尽管COM机制的反应链比ORI机制短,但COM机制保留了燃料消耗(CH₄)导致H₂O形成的基本物理逻辑,同时引入了H和OH自由基产生和消耗的额外途径。在不同当量比和温度下的敏感性分析一致地确定反应R5 (H + O₂≤>;O + OH)是温度最敏感的反应,强调了其在LIB排气反应动力学中的关键作用。
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CiteScore
4.20
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