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A cooling fan is one of the primary components affecting the cooling performance of an engine cooling system. In recent years, with the increase in electric vehicles (EVs) and hybrid vehicles (HVs), the cooling performance and noise level of the cooling fan have become very important. Thus, the development of a low-noise fan with the same cooling performance is urgently required. To address this issue, it is critical to find the relation between the performance of the fan and the flow structures generated around it, which is discussed in the present paper. Specifically, a computational method is employed that uses unsteady Reynolds-averaged Navier-Stokes (URANS) coupling with a sliding mesh (SLM). Measurements of the P-Q (Pressure gain-Flow rate) characteristics are performed to validate the predictive accuracy of the simulation.

The aerodynamic performance of new vehicles is commonly determined using computational fluid dynamics (CFD) and wind tunnel tests. The final assessment is carried out by actual running tests. In particular, ideas regarding fuel consumption improvement that relate to components for the reduction of the coefficient of drag (CD) value are evaluated by coast-down tests. However, a difference often exists between the component's efficiency between wind tunnel tests and coast-down tests. Therefore, we focused on the efficiency of an air-dam spoiler in reducing CD values. A comparison was made between the aerodynamic effect of the air-dam spoiler in wind tunnel and coast-down tests in terms of the CD value and the wake structure behind the vehicle. To determine the relationship between the CD value and the wake structure behind the vehicle, we measured vehicle speed, wind velocity and direction, vehicle height, and pressure distribution on the back door.

In order to develop various parts and components for hybrid electric vehicles, understanding the effect of their structure and thermal performance on their fuel consumption and cruising distance is essential. However, this essential information is generally not available to suppliers of vehicle parts and components. In this report, following a previous study of electric vehicles, a simple method is proposed as the first step to estimate the algorithm of the energy transmission and then the cruising performance for hybrid electric vehicles. The proposed method estimates the cruising performance using only the published information given to suppliers, who, in general, are not supplied with more detailed information. Further, an actual case study demonstrating application of the proposed method is also discussed.

In order to develop various parts and components of vehicles, understanding the effects of their structures and thermal performance on the fuel consumption and cruising distance is important. However, because of the limited information available to parts suppliers, it is not always easy to predict and study vehicle fuel efficiency and cruising range performance under arbitrary driving conditions. In this study, the authors have developed an RFD (Regression Fuel-consumption Diagram) method to predict the cruising performance of internal-combustion engine vehicles (ICEV) based only on the published information given to suppliers by using standard reference vehicles, which had been regressed and identified for control characteristics and fuel consumption diagrams. As an example of the application of the RFD method to realistic situation, the effects of the driving mode and air-conditioning on the fuel consumption of ICEV are studied.