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.

The empirical model fails to capture the complex physical and chemical coupling processes occurring within the battery and the resulting response hysteresis, posing challenges for the development of precise control strategies for fuel cell systems. To address this issue, a control-oriented one-dimensional non-isothermal two-phase flow model was developed, with the transient effects of gas in the flow channel and the phase transition of water in the battery taken into account. The effects of current density on gas concentration and water-heat distribution characteristics were investigated. The impacts of operating conditions and model parameters on the output voltage of the battery were studied, and the advantages of the proposed model compared to the lumped parameter model in terms of output performance under current steps were analyzed. The results show that the model exhibits better applicability, providing a reliable foundation for model optimization and the design of control strategies at the fuel cell system level.