用于控制的三元催化转换器储氧量观测模型开发

Development of Observation Model for Oxygen Storage of Three-Way Catalyst for Control

车载三元催化转换器(TWC)具备一定的储氧与释氧功能,该功能直接影响催化器在过量空气系数(φ_(a))波动下对污染物的转化效率.对TWC内部储、释氧过程的监控和预测,有助于改善和控制发动机瞬态排放,但TWC的储氧、释氧过程是其复杂化学反应过程的一部分,缺少直接观测的手段.由于详细化学机理的建模过于复杂,难以满足实时控制的需要.因此,笔者以化学反应机理建模为基础,利用长短期记忆(LSTM)神经网络对时间序列数据的观测能力,构建了储氧量观测模型,准确且快速地反馈TWC当前储氧量,并给出对TWC下游排气过量空气系数的预测.结果表明:所建立的观测模型对车辆不同工况的储氧量预测结果,以机理模型观测结果为参考,二者的平均相对误差均值为5.87%.下游过量空气系数预测结果的平均相对误差均值约为0.27%,运算时间约为机理模型的0.77%.

The three-way catalyst(TWC)has certain oxygen storage and release function,which directly affects the transformation efficiency of pollutants in the volatility of the catalyst in the excess air coefficient(φ_(a))fluctuation.Therefore,the monitoring and prediction of the oxygen storage and release process of TWC can help to decrease and control the transient emissions of the engine.However,the oxygen storage and release process of TWC is part of its complex chemical reaction and lacks the way to direct observation,and modeling based on detailed chemical mechanisms is too complicated to meet the needs of real-time control.Therefore,this study is based on the chemical reaction mechanism modeling,using the long and short-term memory(LSTM)neural network to observe time series data,and constructs a oxygen storage observation model,which accurately and quickly feeds back the TWC oxygen storage and downstreamφ_(a).The results show that the observation model has an average relative error of 5.87%for the prediction results of the vehicle’s oxygen storage under different operating conditions,and the average relative error of the downstreamφ_(a) prediction results is about 0.27%,and the prediction time is about 0.77%of the mechanism model.