Utilizing model-based controller design in automotive and powertrain industry is recently attracting more attention due to its benefits in reducing controller development time and cost. Recent automotive emission legislations put more limits on engine emissions in transients. Hence, the models, which are capable of predicting engine performance and emissions in transient, are of the utmost importance. On the other hand, the model-based controller design requires accurate meanwhile fast to run models to be employed in both controller development and subsequent hardware in loop processes. In this paper, a new quasi-static control oriented diesel engine modeling approach is investigated based on the block oriented modeling method to predict the engine behavior in sense of both performance and emissions in transient and steady state operation. The accuracy and speed of model execution are two important attributes of models, which are in mutuality. In the proposed modeling a tradeoff between these two factors are made and some solutions are employed to increase both model accuracy and speed. The diesel engines are nonlinear dynamic systems. In the proposed modeling approach, this behavior is assumed to be composed of a semi-static combustion process surrounded by peripheral dynamic processes. This static in cylinder process model is responsible for the performance and emissions of the engine. Thermodynamic modeling coupled with chemical reaction model altogether with 1D gas dynamic model is employed to predict the performance and emission of in-cylinder process based on some boundary conditions which are derived from peripheral systems. Usually an iterative time consuming method is employed to solve the thermodynamic models. In order to decrease the run-time of model, a neural network is trained to mimic the thermodynamic model. On the other hand ordinary time differential equations are used to model the peripheral dynamic systems such as induction and exhaust systems. In order to validate the model for both steady and transient regimes, real step responses as well as experimental frequency response are compared with model results. The comparison of experimental data with model results shows tight agreement in both performance and emission prediction capabilities.