为解决在选择性催化还原技术(selective catalytic reduction，SCR)的控制策略开发中局部线性模型树(local linear model tree，LOLIMOT)排放模型预测精度不足的问题，提出一种通过优化空间边界，将原模型的超矩形输入空间约束在物理意义范围内的改进LOLIMOT模型。通过某天然气发动机的辨识试验，从分布特征和计算原理角度，分析了该方法对预测结果的影响。结果表明：与原算法相比，改进算法的线性相关度R²提升了1.9%，验证了改进策略的有效性。改进LOLIMOT算法具备较高的收敛速度和稳定性，在排放模型领域具备一定的应用优势。

To solve the problem of insufficient prediction accuracy of the local linear model tree (LOLIMOT) emission model in the development of the selective catalytic reduction technology (SCR) control strategy, a method of optimizing the space boundary is proposed. This method aims to constrain the super-rectangular input space of the original model within the scope of physical definitions in the modified LOLIMOT model. Through the identification test of a compressed natural gas (CNG) engine, the effects of this method on prediction results are analyzed considering distribution characteristics and calculation principles. The results show that compared with the original algorithm, the linear correlation R² of the improved algorithm is increased by 1.9%, verifying the effectiveness of the proposed strategy. The modified LOLIMOT algorithm demonstrates higher convergence speed and stability, offering valuable application advantages in the field of emission models.

TK421.5

国家重点研发计划资助项目（2018YFB0106401）。