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车载储氢瓶快速充气后气体状态的预测模型 被引量:2

Prediction Model of Gas State after Rapid Inflation of Onboard Hydrogen Storage Tank
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摘要 本文依照工程热力学基本理论建立了车载储氢瓶中氢气充入质量与氢气状态参数的预测模型,通过实验数据验证了模型的合理性。利用该模型分析了充氢温度对充气结束后气瓶内填充质量与最终温度的影响和环境温度对充气结束后车载储氢瓶内最终温度的影响。结果表明:车载气瓶内初始压力越低,可填充气体质量随充氢温度的升高其减少率越大,最终温度随充氢温度的升高其温升率越大;车载气瓶内初始压力越低,最终温度随环境温度的升高其温升率越低。同时该预测模型可以针对车载气瓶内不同的初始条件去预测气源氢气所需的最低预冷温度,为目前加氢站的气源氢气温度的控制提供理论依据,进而减少加氢站氢气冷却所需能耗。 According to the basic theory of engineering thermodynamics, this paper establishes a prediction model of the hydrogen charging quality and the hydrogen state parameters in the vehicle-mounted hydrogen storage bottle. The rationality of the model is verified by experimental data. This model is used to analyze the influence of hydrogen charging temperature on filling quality and final temperature in the cylinder after filling and the influence of ambient temperature on final temperature in the vehicle hydrogen storage cylinder after filling. The results show that the lower the initial pressure in the vehicle cylinder, the greater the reduction rate of the filling gas mass with the increase of the hydrogen filling temperature, and the greater the temperature rise rate of the final temperature with the increase of the hydrogen filling temperature;The lower the initial pressure in the vehicle cylinder, the lower the temperature rise rate of the final temperature with the increase of the ambient temperature.At the same time, the prediction model can be used to predict the minimum pre-cooling temperature required by the gas source hydrogen according to different initial conditions in the vehicle cylinder, so as to provide a theoretical basis for the control of the temperature of the gas source hydrogen in the current hydrogen filling station and thus reduce the energy consumption required by the hydrogen cooling in the filling station.
作者 唐乾 李兴虎 Tang Qian;Li Xinghu(Beihang University,Beijing 100191)
出处 《中国汽车》 2021年第2期38-43,共6页 China Auto
关键词 车载储氢瓶 充气 模型 预测 氢气状态参数 on-board hydrogen storage bottle inflatable model predict hydrogen state parameters
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