Digital Twin-Enhanced Chassis Dynamometer for EV Drivetrain Testing

Accurate prediction of drivetrain performance and energy efficiency in electric vehicles (EVs) remains a challenge due to the limitations of conventional chassis dynamometer testing, which often overlooks dynamic vehicle-roller interactions, suspension responses, and regenerative braking effects. This paper presents a novel digital twin-enabled testing framework using a compact, two-roller chassis dynamometer integrated with real-time multibody dynamics and electromechanical co-simulation. The physical test bench is coupled with a virtual model of the EV, including suspension geometry, rear-to-front axle load distribution, tire–roller contact mechanics, and motor-inverter dynamics. By enabling bi-directional data exchange between the physical and virtual domains, the system emulates real-world load shifts, torque demand variations, and energy recovery under standardized drive cycles. This integrated approach enhances simulation fidelity, supports certification-compliant testing, and improves the accuracy of torque and energy estimations by correcting bench-induced artifacts in real time. The framework serves as a scalable solution for both performance validation and controller development in EV drivetrain systems, bridging the gap between laboratory testing and real-world conditions through a high-fidelity digital twin.