In its conventional form, dynamometers typically provide a fixed architecture for measuring torque, speed, and power, with their scope primarily centered on these parameters and only limited emphasis on capturing aggregated real-time performance factors such as battery load and energy flow across the diverse range of emerging electric vehicle (EV) powertrain architectures. The objective of this work is to develop a valid, appropriate, scalable modular test framework that combines a real-time virtual twin of a compact physical dynamometer with world leading real-time mechanical and energy parameters/attributes useful for its virtual validation, as well as the evaluation of other unknown parameters that respectively span iterations of hybrid and electric vehicle configurations, ultimately allowing the assessment of multiple chassis without having to modify the physical testing facility's test bench. This integration enables a blended approach, using a live data source for now, providing a point of calibration and validation for the virtual model(s), as well as using the virtual model capability to determine other unknown/measurable characteristics about the physical model. So, this test framework makes that system's capability, representing an enhancement to its ability, with a virtual twin merging them together to enable virtual evaluation of multiple configurations of the chassis without changing the physical test stand. So, this combined real-world and virtual framework offers a scalable and flexible testing and modelling platform for both early performance characterisation, as well as life cycle-based energy evaluations. In responding to the identified gaps, this work introduces an innovative hybrid chassis dynamometer framework that applies to a real-world test bench in tandem with a concurrently simulated virtual model, offering early-stage validation and optimisation potential using the shift-left development proposition. The result is a reusable and forward-thinking platform supporting efficient EV development by forecasting and drawing informed insights into energy flow, battery performance, and lifecycle behaviour from ahead of typical physical testing boundaries.