Sensitivity Study on Thermal and Soot Oxidation Dynamics of Gasoline Particulate Filters
Sensitivity Study on Thermal and Soot Oxidation Dynamics of Gasoline Particulate Filters
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DOI：
10.4271/2019-01-0990
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发表时间：
2019-04
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影响因子：
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通讯作者：
Aki Takahashi;S. Korneev;S. Onori
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文献类型：
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作者：
Aki Takahashi;S. Korneev;S. Onori
汽油颗粒过滤器 (GPF) 是用于过滤汽油直喷 (GDI) 发动机排放的碳烟的装置。 H. Arunachalam 等人在之前的论文中提出了二氧化铈涂层 GPF 的数值模型。 2017 年的开发是为了预测再生事件期间的内部温度和燃烧的烟灰量。由于内部温度和累积烟灰在实时运行期间无法直接测量，并且由于它们对于 GPF 健康监测和再生调度至关重要，因此上述模型对于 OBD 应用而言是一个有价值的工具。在本文中，我们首先进行随机分析，以了解模型参数与二氧化铈（IV）氧化物体积分数初始值之间的关系，因为这种状态的确定性值未知。采用粒子群优化 (PSO) 算法来定义模型参数与二氧化铈 (IV) 氧化物体积分数的初始状态之间的关系类型。然后对模型参数进行敏感性研究，以研究系统测量中的参数可识别性。还研究了初始温度和初始烟灰量的影响。结果表明，该模型对 GPF 再生的活化能最敏感，这与之前的研究一致。此外，当参数不确定性最多为 20% 时，该模型能够预测 GPF 温度，误差小于 5%。二氧化铈（IV）氧化物与参数之间的关系以及灵敏度分析的结果可以在将来同时用于基于观测器的设计。
Gasoline particulate filters (GPFs) are devices used to filter soot emitted by gasoline direct injection (GDI) engines. A numerical model for a ceria-coated GPF presented in a previous paper by H. Arunachalam et al. in 2017 was developed to predict internal temperature and soot amount combusted during regeneration events. Being that both the internal temperature and the accumulated soot cannot be directly measured during real-time operation and owing to their critical importance for GPF health monitoring as well as regeneration scheduling, the above model turns out to be a valuable tool for OBD applications. In this paper, we first conduct a stochastic analysis to understand the relation between the model parameters and the initial value of the ceria (IV) oxide volume fraction, as a deterministic value for such a state is not known. A particle swarm optimization (PSO) algorithm was employed to define what type of relationship the model parameters were with respect to the initial state of the ceria (IV) oxide volume fraction. A sensitivity study is then conducted over the model parameters to study parameter identifiability from system measurements. Effects of the initial temperature and initial amount of soot were studied as well. Results indicated that the model is most sensitive to the activation energy of GPF regeneration, agreeing with previous studies. Additionally, the model was shown to be able to predict the GPF temperature with less than 5% error when there was at most 20% uncertainty in the parameters. The results of the relationship between ceria (IV) oxide and the parameters, as well as the sensitivity analysis can be used simultaneously in the future for observer-based design.