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In the early stages of vehicle development, it is important for decision makers to understand a feasible constraint region that satisfies all system level requirements. The purpose of this paper is to propose a target cascading method to solve for a feasible design region which satisfies all constraints of the system based on model based simulation. In this method, the feasible design region is explored by using both global optimization methods and active learning techniques. In optimization problems, the inverse problem for understanding feasibility for specific designs is defined and solved. To determine the objective functions of the inverse problem, an index representing the achievement level of constraints from system requirements is introduced. To predict feasible regions in the specific design space, a surrogate model of minimized values of the index is trained by using a kriging model.

In recent years, swift changes in market demands toward achieving carbon neutrality have driven significant developments within the automotive industry. Consequently, employing computer simulations in the early stages of vehicle development has become imperative for a comprehensive understanding of performance characteristics. Of particular importance is the cooling performance of vehicles, which plays a vital role in ensuring safety and overall performance. It is crucial to predict optimal cooling performance, particularly about the heat generated by the powertrain during the initial phases of vehicle development. However, the utilization of thermal analysis models for assessing vehicle cooling performance demands substantial computational resources, rendering them less practical for evaluating performance associated with design changes in the planning phase.