Selecting Battery Modeling Approaches for Different Use Cases
Overview
Battery modeling, encompassing equivalent circuit models, physics-based electrochemical models, and data-driven models, is vital for the design, analysis, and control of battery systems in applications such as electric vehicles, eVTOLs, grid energy storage, and consumer electronics. Traditionally, equivalent circuit models have been employed for system-level simulations and battery management system (BMS) algorithms, while detailed electrochemical models have supported cell design and performance analysis. With increased computational capabilities and advanced modeling tools, along with a growing demand for higher accuracy, electrochemical models are now more widely used for tasks such as aging prediction, fast charging optimization, physics-based state estimation, and real-time simulation. These advancements allow engineers to achieve greater precision in performance prediction and control.
In this webinar, you will learn how MathWorks products support a range of battery modeling approaches and receive practical guidance on selecting the most suitable technique for your application. The session will also demonstrate how various modeling methods can be implemented using MathWorks tools through multiple examples.
Highlights
- Explore the differences between equivalent circuit, physics-based electrochemical, and data-driven battery modeling techniques.
- Gain practical guidance on selecting the right battery modeling approach for various uses cases.
- See demonstrations of MathWorks tools implementing multiple battery modeling methods through real-world examples.
Join us to gain actionable insights and see firsthand how MathWorks tools can accelerate your battery modeling projects.
Please allow approximately 45 minutes to attend the presentation and Q&A session. We will be recording this webinar, so if you can't make it for the live broadcast, register and we will send you a link to watch it on-demand.
About the Presenter
Xiangchun Zhang is an application engineer at MathWorks, specializing in battery modeling and battery management system (BMS) algorithm development. He began his research on Li-ion batteries in 2005 during his doctoral studies, where he focused on the multiscale modeling of Li-ion cells, including mechanics, heat generation, and electrochemical kinetics. Over two decades, he has contributed to both academic and industrial battery R&D, including solid-state battery development at Dyson. His work on batteries has been cited thousands of times. Zhang holds BS and MS degrees in engineering from Tsinghua University in Beijing, China, and a PhD in Mechanical Engineering from the University of Michigan, Ann Arbor.
