Build a Battery Digital Twin with Twin Builder
Create a virtual copy of a drone battery that predicts its behavior in real time
Last reviewed: March 2026Overview
A digital twin is a virtual copy of a real physical object that updates in real time using sensor data. In aerospace, digital twins are used to monitor everything from jet engines to satellite power systems — predicting problems before they happen and optimizing performance.
In this project you'll build a digital twin of a simple lithium-polymer (LiPo) battery, the same type used in drones and model aircraft. Using Ansys Twin Builder, you'll create a circuit-based battery model that predicts voltage, current, and state of charge as the battery discharges under different flight profiles.
By the end you'll understand the core concept of digital twins — a physics model connected to real data — and you'll have a working model that could tell a drone pilot exactly how many minutes of flight time remain. This is the same approach companies like GE Aviation use to monitor jet engine health across their entire fleet.
What You'll Learn
- ✓ Understand what a digital twin is and why it matters for aerospace systems
- ✓ Build a simple equivalent circuit model of a battery in Twin Builder
- ✓ Simulate battery discharge under constant and variable load profiles
- ✓ Read and interpret voltage-vs-time discharge curves
- ✓ Connect model predictions to real-world drone flight planning
Step-by-Step Guide
Install Ansys Twin Builder
Download Ansys Twin Builder through the Ansys Student program (free for students). Install it and work through the built-in "Getting Started" tutorial to learn the interface. The key concepts are: components (blocks that model physics), connections (wires between blocks), and simulations (running the model forward in time).
Model the Battery Circuit
Build a simple equivalent circuit model: an ideal voltage source (representing battery chemistry), a series resistor (representing internal resistance), and a capacitor (representing charge storage). Use typical LiPo specs: 3.7V nominal, 1500mAh capacity, 20mΩ internal resistance.
Simulate Constant Discharge
Connect a constant-current load (simulating a drone motor at steady hover) and run the simulation. Watch the voltage drop over time as the battery discharges. Plot voltage vs. time and state of charge vs. time. How long until the battery hits the 3.0V cutoff?
Add a Realistic Flight Profile
Replace the constant load with a time-varying profile: high current during takeoff, moderate during cruise, and spikes during maneuvers. This is much more realistic. Compare the discharge curve to the constant-load case — does the battery last longer or shorter?
Predict Remaining Flight Time
Add a simple calculation block that estimates remaining flight time based on current state of charge and recent power consumption. This is the core "twin" behavior: using the model to predict the future. Test it at 50%, 25%, and 10% state of charge.
Document Your Twin
Export screenshots of your model, simulation results, and discharge curves. Write a short report explaining: what is a digital twin, how your battery model works, what it predicts, and how this concept scales to full aircraft systems. This is presentation-ready material for a science fair or college application.
Career Connection
See how this project connects to real aerospace careers.
Aerospace Engineer →
Digital twins are becoming standard practice for monitoring aircraft systems health throughout their lifecycle
Aviation Maintenance →
Maintenance teams use digital twins to predict component failures and schedule maintenance before breakdowns occur
Drone & UAV Ops →
Battery management is the #1 operational constraint for drone missions — accurate state-of-charge prediction is essential
Avionics Technician →
Understanding electrical system modeling helps avionics techs diagnose power system issues
Go Further
Extend your digital twin:
- Temperature effects — add a thermal model to show how cold weather reduces battery capacity
- Multiple cells — model a multi-cell battery pack with cell balancing
- Degradation — add a cycle-counting model that predicts battery health over hundreds of charge cycles
- Connect to real data — log voltage from an actual drone battery and compare to your model predictions