Development of a Digital Twin for an Electric Vehicle Emulator Modeling, Control, and Experimental Validation

TL;DR

This paper develops and validates a digital twin for an electric vehicle emulator, integrating energy-based modeling and control strategies to accurately replicate vehicle dynamics and validate energy management under various conditions.

Contribution

It introduces an EMR-based modeling framework for EV emulation, enabling systematic control design and validation through experimental testing.

Findings

Maximum vehicle mass of 13.5 kg for 180 W motor at 1900 rpm

Strong correlation between experimental and simulation results

Effective control of energy flow and speed via EMR framework

Abstract

This paper presents the development and validation of a digital twin for a scaled-down electric vehicle (EV) emulator, designed to replicate longitudinal vehicle dynamics under diverse operating conditions. The emulator integrates a separately excited DC motor (SEDCM), a four-quadrant DC-DC converter, a battery emulator, and a mechanical load emulator. The system models tractive effort, aerodynamic drag, and gradient resistance using Newton's second law. In contrast to conventional graphical modeling tools (e.g., block diagrams and bond graphs), the adopted Energetic Macroscopic Representation (EMR) framework offers clear advantages by explicitly representing energy interactions and facilitating the systematic derivation of control structures. A control strategy developed within this framework governs energy flow across the powertrain, enabling accurate speed control via armature voltage regulation. Experimental tests conducted on a Lucas-Nulle test bench show strong correlation with simulation results. The study also introduces a methodology to compute the maximum admissible vehicle mass - determined to be 13.5 kg for a 180 W motor operating at 1900 rpm - based on acceleration and slope constraints. Furthermore, a switching algorithm for the bidirectional converter ensures reliable four quadrant operation. Overall, the proposed framework provides a scalable and effective approach for EV emulation, control design, and energy management validation.