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The advancement in connectivity technology is driving a shift in business models in almost every field. Automakers need to adapt to a new business model in which the platform (automobile) and the mobility solutions (Devices and Services) are enabled by a strong dynamic connectivity. To succeed in this business model, it is imperative to deliver an unparalleled customer experience. Traditional customer experiences focused only in the platform (automobile) are no longer sufficient to address the mobility needs. The development of in-vehicle features should consider both the platform and the connectivity in a single development scope. This paradigm shift sets new challenges for the in-vehicle features designers. Designers have to speak not only the language of the experience but rather a language to address different levels of abstractions to ensure effective communication with all stakeholders and developers including those outside the organization.

Electrical Computer Aided Engineering (CAE) is necessary and useful for the automotive industry [1,2]. It provides the user with necessary information that helps him/her make faster and more certain design decisions. CAE facilitates for the user options and the means to locate and choose optimums [6]. It requires models that best represent the actual system while avoiding unnecessary numerical overhead. Therefore, it is necessary to build the mathematical model in a systematic way that captures dynamics of the actual system [3]. Also, an optimal solution for the system parameters is required to increase the accuracy of the model and to make a correct decision while designing, testing, and validating [3,6]. This paper studies the CAE analysis of a PMDC motor. It develops a systematic approach to model, simulate and analyze PMDC motors with robust output. It enhances the accuracy of PMDC to a 6-Sigma level.