Application of LOLIMOT to CNG engine NO_x emission prediction test

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.