To address environmental pollution problems, electric vehicles (EVs) are attracting attention as future mobility vehicles. However, an increase in the number of advanced systems coupled with such vehicles imposes a limit on the development of EVs. The conventional design methods require a large amount of experimental and simulation data to satisfy the target performance of the system. Therefore, it takes time to arrive at the desired design  solution. Hence, we propose a new design method using domainadaptive designable data augmentation (DADDA). DADDA is a deep learning-based generative model that applies an inverse generator and domain adaptation concept to the data augmentation algorithm. This model aims to rapidly provide a design solution for a new system with a performance level similar to that of the existing system by adapting the domain of the existing system when the design information for a new system is insufficient.