Mathworks Session #2: Reduced Order Modeling for Power Supplies and Battery Systems

Abstract

In the fast-evolving field of electronics, efficient and accurate modeling of key system components is essential for optimizing performance, reliability, and integration. This tutorial workshop introduces reduced order modeling techniques tailored for power supplies and battery systems, providing practical tools for engineers and researchers working in electronics and energy storage.

Participants will receive a comprehensive overview of reduced order modeling approaches, including system identification, artificial intelligence, and analytical/physics-based methods. The session will begin with a discussion of the strengths and limitations of each technique, helping attendees select the most appropriate approach for their applications.

Two detailed examples will be presented to illustrate common use cases. First, a classical system identification approach will be demonstrated for a power converter, extracting a time-varying state-space representation that captures the essential dynamics with minimal computational cost. Next, the process of designing, training, and deploying a neural network model for a lithium-ion battery will be reviewed, highlighting how data-driven methods can accelerate simulation and design.

By the end of the workshop, participants will understand the range of modeling strategies available for power electronics and battery systems, and will be equipped with practical skills to apply these techniques to real-world design and analysis challenges.