With the increasing market penetration of automated vehicles, there is a critical need for credible and repeatable methods to quantify their energy impacts. This paper presents a Model-Based Systems Engineering (MBSE)-driven Anything-in-the-Loop (XIL) methodology for quantifying the powertrain energy consumption and potential savings from various controls for automated vehicles in realistic road scenarios while preserving high-fidelity powertrain behavior. The novelty of this approach lies in its use of a unified MBSE backbone (AMBER: Argonne National Laboratory’s [Argonne’s] MBSE-centric platform for transportation energy analysis) to automate the seamless and traceable progression from pure simulation to Vehicle-in-the-Loop (VIL) testing. This work utilizes Argonne's multi-vehicle simulation tool, RoadRunner, which automatically constructs closed-loop road scenarios (road geometry, vehicle sensors, other vehicles, and traffic controls) and connects them to Argonne’s validated, high-fidelity vehicle and powertrain models in Autonomie. The MBSE backbone in AMBER organizes requirements, interfaces, plant and controller models, and test scenarios into a single set of models that is maintained across pure simulation, Software-in-the-Loop (SIL), Processor-in-the-Loop (PIL), and VIL stages. Each stage has a clear role: simulation enables rapid development and validation of advanced models or controls across a large number of scenarios; SIL supports standalone algorithm verification and scenario down-selection; PIL validates real-time execution, inputs/outputs, and timing on the target processor; and VIL provides closed-loop evaluation with a real vehicle under controlled laboratory conditions. AMBER’s automated build and configuration enable rapid retargeting across platforms and repeatable scenario reproduction, making validation fast and cost-effective. To demonstrate its practical application, the workflow is used to validate the functionality and quantify the energy savings of an eco-driving control against a calibrated human driver model. Experiments show strong repeatability and consistent energy gains for the eco-driving strategy while preserving trip time, yielding average energy savings of 7.8% across the evaluated scenarios. Overall, the MBSE-guided XIL workflow shortens development time and reduces test cost by limiting on-road testing and lowering integration risk before track evaluation, while producing credible, closed-loop energy assessments traceable from requirements to test evidence.