Abstract
The concept of Digital Twin is creating and maintaining a digital representation of the real physical entity and supporting its performance utilizing simulation and optimization tools, which are fed with the real data obtained from the physical equipment. Development and implementation of the Digital Twin technology are one of the main challenges for today’s industry, more detailed studies are needed on design methods for Digital Twins. Besides using Digital Twin as a high-quality simulation, one of the commitments is monitoring and maintaining control of the whole system via a constant live link between virtual and physical entities. The related research study presents a detailed structural description of the developed Digital Twin virtual entity and the development of a framework that allows Robot Operating System (ROS) to securely communicate with remote Digital Twins via the Internet and harness ROS’s adaptability across vast distances and multiple systems. This paper is an extended version of the authors’ International Conference on Electrical Power Drive Systems (ICEPDS20) paper, in which we propose a development case study of Digital Twin for an electric motor based on an empirical performance model.
Highlights
This paper is an extended version of the authors’ IEEE International Conference on Electrical Power Drive Systems paper [1] that took place in October 2020 at SaintPetersburg, Russia
This paper is an extended version of the authors’ International Conference on Electrical Power Drive Systems (ICEPDS20) paper, in which we propose a development case study of Digital Twin for an electric motor based on an empirical performance model
This paper presents a development case of Digital Twin (DT) for an electric motor based on an Empirical Performance Model (EPM)
Summary
This paper is an extended version of the authors’ IEEE International Conference on Electrical Power Drive Systems paper [1] that took place in October 2020 at SaintPetersburg, Russia. A recent study presents a part of the project that aimed to develop a specialized unsupervised prognosis and control platform for electric propulsion drive systems performance estimation of an autonomous self-driving electric vehicle. This goal requires the development of several subtasks and related objectives, one of them is to develop physical models of different energy system components (motors, gearboxes, power converters, etc.) and the related reduced models of these components (testbed), which will serve to construct the DT of the system [12].
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