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Since the operation of the powertrain system and the engine speed and torque are determined in the ECU in hybrid vehicles, control parameters in these vehicles are more sensitive to a variety of performance factors than those employed in conventional vehicles. The three performance factors acceleration performance, NVH and fuel consumption in particular are in a tradeoff relationship, the calibration of control parameters in order to satisfy these performance targets entail considerable development costs. Given this, it is possible to increase the efficiency of hybrid vehicle development by determining Pareto design solutions for the three performance factors via multi-objective optimization using CAE, and selecting target performance and control parameters based on these Pareto design solutions.

An aerodynamic styling evaluation system employed at an early automotive development stage was constructed. The system based on CFD consists of exterior model morphing, computational mesh generation, flow calculation and result analysis, and the process is automatically and successively executed by process automation software. Response surfaces and a parallel coordinates chart output by the system allow users to find a well-balanced exterior form, in terms of aerodynamics and exterior styling, in a wide design space which are often arduous to be obtained by a conventional CAE manner and scale model wind tunnel testing. The system was designed so that 5-parameter study is completed within approximately two days, and consequently, has been widely applied to actual exterior styling development. An application for a hatchback vehicle is also introduced as an actual example.

Optimization methodology employing CFD for the aerodynamic design of automotive car styling is presented. The optimization process consists of three stages: Design of Experiments (DOE), Response Surface Modeling (RSM), and optimization algorithm execution. RSM requires a number of CFD calculations in order to ensure its accuracy, making it difficult to apply the RSM to aerodynamic design optimization. In order to resolve this issue, Adaptive Multi Stage RSM (AMS-RSM) was conceived. This method provided the response surface its required accuracy and robustness. The optimization process was realized by constructing an automatic optimization system consisting of software.