Digital Twin (DT) technology has become a core enabler of Industry 4.0 by providing a dynamic virtual representation of physical systems for real-time monitoring, simulation, and optimization, supported by IoT sensing and connectivity. Electric drives can par-ticularly benefit from DTs because they enable condition monitoring, fault diagnosis, and Remaining Useful Life (RUL) estimation. The reviewed literature is analysed in terms of application, model type, integration level, strengths, and limitations. The proposed six-layer framework separates offline commissioning and validation from online synchronization and decision support. Additionally, an induction motor drive use case is presented to demonstrate how the proposed framework can be applied in practice. The use case comprises of a V/f-controlled induction motor supplied by an inverter, together with isolated voltage and current measure-ments, speed acquisition, signal preprocessing, virtual model synchronization using an Extended Kalman Filter, and supervisory control for improving drive operation. The proposed framework provides criteria for designing and validating DTs for electric drive monitoring, fault diagnosis, predictive maintenance, and supervisory control.