Overview of Digital Twin Platforms for EV Applications

Area of Science:

• Engineering
• Computer Science
• Automotive Technology

Background:

• Digital twin (DT) technology provides real-time virtual representations of physical assets.
• Its application in electric vehicles (EVs) is expanding across various research domains.
• DTs enable advanced simulation for prediction and optimization.

Purpose of the Study:

• To provide an overview of digital twin platforms applicable to electric vehicles.
• To compare model-based and data-driven DT approaches for EV applications.
• To discuss the suitability of DT platforms for core EV systems.

Main Methods:

• A deductive comparison of model-based versus data-driven DT methodologies.
• Review of existing DT platforms utilized within the EV industry.
• Analysis of DT applicability across different EV systems and components.

Main Results:

• Digital twin technology can emulate real vehicles for performance prediction and safety enhancement.
• DTs are valuable for optimizing EV manufacturing, real-time monitoring, energy management, and recycling.
• Data-driven DTs demonstrate superior capability in managing highly complex EV systems.

Conclusions:

• Digital twin technology offers significant potential for advancing electric vehicle design, operation, and lifecycle management.
• Data-driven DTs are better suited for the inherent complexity of modern electric vehicle systems.
• Further research into DT platforms can drive innovation in the EV sector.