Comparative analysis of time series neural network methods for three-way catalyst modeling

作     者：Zhuoxiao Yao Tao Chen Weipeng Lin Yifang Feng Zengchun Wei 

作者机构：State Key Laboratory of EnginesTianjin University135 Yaguan RdTianjin 300350China 

出 版 物：《Energy and AI》 (能源与人工智能（英文）)

年 卷 期：2024年第17卷第3期

页      面：220-232页

核心收录：

学科分类：0711[理学-系统科学] 07[理学] 08[工学] 081101[工学-控制理论与控制工程] 0811[工学-控制科学与工程] 071102[理学-系统分析与集成] 081103[工学-系统工程] 

主　　题：Relative Oxygen Level Neural network modeling Long short-term memory Nonlinear auto-regressive network with eXogenous inputs 

摘      要：Relative Oxygen Level of the Three-Way Catalyst is an important parameter that affects the conversion efficiency of pollutants. ROL is a time-varying hidden state variable that is difficult to directly observe in practice. Therefore, it is common to use a method of clearing oxygen storage to simplify control in vehicles. However, this method negates the positive effects of ROL on pollutant treatment. ROL can be indirectly observed through modeling methods. Chemical modeling methods involve extensive computational requirements that cannot meet the demands of practical control. In contrast, time-series neural networks offer computational speed advantages when dealing with similar problems. Therefore, the ROL observation models using both NARX and LSTM neural networks are developed and compared in this study. The results indicate that the NARX neural network exhibits higher precision with a smaller number of neurons and time steps. The LSTM neural network demonstrates greater stability when dealing with data error fluctuations. In practical applications, the ROL model can monitor the TWC operating status and assist in the development of intelligent pollutant aftertreatment control strategies.