Cylinder deactivation is a fuel economy improvement technology that has attracted particular attention recently. The currently produced cylinder deactivation engines utilize fixed-type cylinder deactivation in which only a fixed number of cylinders are deactivated. As fixed-type cylinder deactivation has some shortcomings, variable-type cylinder deactivation with no limit on the number of deactivated cylinders is under research. For variable-type cylinder deactivation, control is more complicated and production cost is higher than fixed-type cylinder deactivation. Therefore, it is necessary to select the cylinder deactivation control method considering both advantages and disadvantages of the two control methods. In this study, a fuel economy prediction simulation model was created using the measurement data of various vehicles with engine displacements of 1.0–5.0 L. The fuel economy improvement of fixed-type cylinder deactivation was compared with that of variable-type cylinder deactivation using the created simulation. As a result of examining the fuel economy improvement of the test vehicle in the FTP-75 driving cycle, the improvement was 2.2–10.0% for fixed-type cylinder deactivation and 2.2–12.8% for variable-type cylinder deactivation. Furthermore, the effect of the engine load on fuel economy improvement under cylinder deactivation and the effect of changes in engine control were examined via a simulation.

Vehicle integrated thermal management system (VTMS) is an important technology used for improving the energy efficiency of vehicles. Physics-based modeling is widely used to predict the energy flow in such systems. However, physics-based modeling requires several experimental approaches to get the required parameters. The experimental approach to obtain these parameters is expensive and requires great effort to configure a separate experimental device and conduct the experiment. Therefore, in this study, a neural network (NN) approach is applied to reduce the cost and effort necessary to develop a VTMS. The physics-based modeling is also analyzed and compared with recent NN techniques, such as ConvLSTM and temporal convolutional network (TCN), to confirm the feasibility of the NN approach at EPA Federal Test Procedure (FTP-75), Highway Fuel Economy Test cycle (HWFET), Worldwide harmonized Light duty driving Test Cycle (WLTC) and actual on-road driving conditions. TCN performed the best among the tested models and was easier to build than physics-based modeling. For validating the two different approaches, the physical properties of a 1 L class passenger car with an electric control valve are measured. The NN model proved to be effective in predicting the characteristics of a vehicle cooling system. The proposed method will reduce research costs in the field of predictive control and VTMS design.

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