/AI Models for Battery Digital Twins in Robotic Applications

AI Models for Battery Digital Twins in Robotic Applications

Brussel | Just now

Monitor, predict, and optimize robots' battery performance for sustainable robotics.

Robotic systems’ performance relies on their battery performance, and managing battery health over time is critical for ensuring reliability and minimizing downtime. At IMEC-VUB-Brubotics, we are developing intelligent solutions to monitor and predict robot battery behavior using digital twins.

A digital twin is a virtual representation that simulates real-world battery behavior in real time. By leveraging AI, these twins will monitor battery performance, predict degradation, and optimize robotic operations.

We offer three exciting opportunities for interns to contribute to this innovative project:

AI-Powered Battery Performance Prediction: Design and prototype AI models to predict key metrics like capacity fade and state of health (SoH) using real-world robotic data.
Implementing the Battery Digital Twin in Robotic Simulations: Integrate the digital twin into robotic simulation environments (e.g., ROS, Gazebo) to test battery behavior in various scenarios.
Developing a Digital Twin Dashboard: Create an interactive dashboard to visualize battery performance, monitor metrics, and provide actionable insights in real-time.

What You Will Learn

Enhance your coding skills with guidance from our mentors.
Practical application of machine learning and AI techniques in robotics and energy management systems.
Hands-on experience with digital twin technology and its integration into real-world applications.
Expand your professional network in a diverse, inclusive, and innovative environment.

Who You Are

Master student pursuing an internship in Robotics, AI, Data Science, or Engineering.
Passionate about applying AI to robotics and energy management systems.
Familiar with programming in Python and/or C.
Familiar with deep learning, transfer learning, and other ML models or robotic simulation tools (depending on the opportunity).

Type of project: Internship

Duration: 4-6 months

Required degree: Master of Engineering Technology, Master of Engineering Science, Master of Science

Required background: Electromechanical engineering, Electrotechnics/Electrical Engineering, Energy, Computer Science, Mechanical Engineering

Supervising scientist(s): For further information or for application, please contact: Parham Haji Ali Mohamadi (Parham.HajiAliMohamadi@imec.be) and Bram Vanderborght (Bram.Vanderborght@imec.be)

Only for self-supporting students.

Apply