Immersive Digital-Twin + VR Learning Module for EV-Battery Cell Stacking

Description:

This project aims to build a VR training experience that couples an interactive station-level digital twin with guided practice for pouch-cell stacking and weld preparation. The team will start by eliciting task analyses from SMEs to define safety pre-checks, stack order rules, and common failure modes, then encode these as state machines and validation rules inside Unity/Unreal. The digital twin animates machine states (idle, clamp, align, reject) and material flow while a step-by-step coach provides just-in-time prompts, error-specific feedback, and recovery instructions. The module implements graded scenarios—basic, timed, and fault-injected—where learners handle misaligned cells, torn tabs, or fixture contamination. A scoring rubric computes pass/fail and proficiency tiers from time-to-complete, error counts, rework steps, and adherence to safety gates; all interactions are data-logged (timestamps, event codes, scenario IDs) to CSV/JSON for learning analytics. Optional peripherals (hand tracking, controllers, eye-tracking if available) capture process efficiency and attention shifts. The team pilots the module with novice users to evaluate learnability and transfer proxies (e.g., decreased errors on harder scenarios), iterates on UX and visuals for clarity, and ships a deployable build with a facilitator guide, scenario library, and a small dashboard that visualizes learner progress over time. 

Duration: Fall/Spring​ 

Team: 2 graduate students and 1 undergraduate student 

Compensation: Unpaid​ 

Preferred Skill and Knowledge:

  • Unity (C#) or Unreal (Blueprints) and VR headset setup (e.g., Meta Quest)
  • Basic discrete-event/logic simulation concepts (station states, queues)
  • Fundamentals of EV-battery processes (cells, tabs, stacking order, safety)
  • Usability/UX for training flows, CSV/JSON logging, and Git basics 

Application Procedure​:

Please send your resume and a short explanation of the project areas you're interested in and why to Chang S. "CS" Nam (csnam@ncat.edu)​.