Methodological Approach to Battery Electric Vehicle Design: Energy-Based Model Development from a Detailed Digital Twin

On the path to the decarbonization of the transport sector, the development of electric vehicles (EVs) is crucial to meeting the targets set by international regulatory bodies. EVs operate with zero tailpipe emissions and offer high energy efficiency and flexibility; however, challenges remain in achieving a fully sustainable electricity supply. In this context, powertrain design plays a fundamental role in determining vehicle performance and mission feasibility, which are strongly influenced by operating conditions and application characteristics, such as driving profiles and ambient temperature.A key challenge is the optimal sizing of components, particularly the battery pack and the electric motor. Therefore, a structured and methodological approach to powertrain design is essential to ensuring an optimal configuration. To this end, the project focuses on an integrated approach based on a master-and-slave modeling framework applied to a light-duty commercial vehicle at two levels. The first level consists of an Energy-based Model (EM), which enables a rapid evaluation of energy consumption across different operating conditions and design layouts and sizing, providing insights into the overall powertrain efficiency. The second level involves a high-fidelity Digital Twin (DT), allowing for a more detailed analysis of component behavior and systems interactions. These two modeling approaches are integrated into an iterative framework, where the EM model facilitates the exploration of a wide range of sizing configurations, while the DT model refines the analysis by capturing detailed component dynamics, ultimately leading to an optimized powertrain design.In this study, the two modeling approaches are presented in detail, and their correlation is evaluated under different operating conditions. Specifically, three ambient temperatures (-5, 15, 40 °C) are tested across two distinct driving profiles. The EM and DT models demonstrate strong agreement in overall performance estimation, with a relative error within the range of 0-10% and a computational time reduction of one order of magnitude, confirming their suitability for integration into systematic powertrain design methodology.