MathWorks Research Internship Experience: Tingkai Li

著者 Tanya Kuruvilla, February 23, 2026

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Today we’re talking to Tingkai Li, who participated in the Development Collaborative Research Grant program at MathWorks, which supports academic research aligned with development priorities for MATLAB and Simulink. Through the program, he advanced battery health estimation and degradation forecasting while helping translate research into practical industry tools.

Meet Tingkai Li

My name is Tingkai Li, born and grew up in Quanzhou, China. I hold my BS in mechanical engineering from Iowa State University, and I’m currently a 4th year PhD student in mechanical engineering at University of Connecticut. I’ve interned at MathWorks for summer 2024 and summer 2025, both times at the Apple Hill campus.

What’s your research about?

My research falls under the general topic of prognostics and health management(PHM) for lithium-ion batteries, in which I specialize in lifetime prediction, capacity fade forecasting, state of health (SOH) estimation, and degradation diagnostics.

Lithium-ion batteries have been widely used all over the place in our daily lives. Similar to other engineered systems and even human beings, lithium-ion batteries age gradually over time, both naturally and by usage. Research in PHM for lithium-ion batteries focuses on delivering a better understanding and a more accurate representation of the battery’s internal state, as well as a forecast of future degradation. All these advancements help with a better prediction of the performance of both batteries themselves and the systems relying on them for power supply by identifying potential failures that may cause chaotic accidents and by optimizing the maintenance scheduling for the systems.

The Development Collaborative Research Grant(DCRG)project I’m part of focuses on developing more accurate, robust, and trustworthy approaches to estimate the state of health (SOH) and forecast future degradation under varying usage conditions for batteries. My role in this project includes designing and running battery aging tests, eventually developing better approaches for SOH estimation and degradation forecasting.

The Research Internship Experience

For my internship I was located in the Apple Hill campus, working closely with the Predictive Maintenance Toolbox™team toexplore anddevelopsome functionalityfor extracting features from batteryusagedata(e.g.,charge/discharge measurements, electrochemical impedance spectra,etc.)for various data-driven batterymodelling workflows (e.g., state of health estimation, remaining useful life prediction).These projectsalignvery well with whatI’velearned and developed in my research, allowing me to bringmyknowledge to customersby providing such features in MathWorks’ ecosystem.

So far, I have developed several prototype and examples during my two internships, some of which are expected to be shipping out in the future releases. One of my projects during my internship in Summer 2024 has been available in R2025a as an example (Automatic Data Segmentation and Feature Extraction for Reference Performance Test in Lab-Measured Battery Aging Data – MATLAB & Simulink).

What’snext?

I’llgo back to school andcontinue working onboththe DCRG project andmy other projects.With anexpected graduation on Spring 2026, Iam considering positions in the industry where I canleveragemydomain knowledge in batterymodellinganda broader context ofAI application inengineering.