{"id":13363,"date":"2026-02-23T08:26:19","date_gmt":"2026-02-23T13:26:19","guid":{"rendered":"https:\/\/blogs.mathworks.com\/student-lounge\/?p=13363"},"modified":"2026-02-23T10:24:40","modified_gmt":"2026-02-23T15:24:40","slug":"mathworks-research-internship-experience-tingkai-li","status":"publish","type":"post","link":"https:\/\/blogs.mathworks.com\/student-lounge\/2026\/02\/23\/mathworks-research-internship-experience-tingkai-li\/","title":{"rendered":"MathWorks Research Internship Experience: Tingkai Li"},"content":{"rendered":"
\n
<\/div>\n
Today we\u2019re talking to Tingkai Li, who participated in the <\/span>Development Collaborative Research Grant program<\/span><\/a> 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.<\/span><\/div>\n

Meet Tingkai Li<\/h2>\n
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\u2019m currently a 4th year PhD student in mechanical engineering at University of Connecticut. I\u2019ve interned at MathWorks for summer 2024 and summer 2025, both times at the Apple Hill campus.<\/div>\n
\"Picture2.png\"<\/div>\n

What\u2019s your research about?<\/h2>\n
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.<\/div>\n
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\u2019s 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.<\/div>\n
The Development Collaborative Research Grant <\/span><\/a>(DCRG)project I\u2019m 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.<\/div>\n

The Research Internship Experience<\/h2>\n
For my internship I was located in the Apple Hill campus, working closely with the Predictive<\/span> Maintenance<\/span> Toolbox<\/span>\u2122 <\/span><\/a>team to explore and develop some functionality for extracting features from battery usage data( e.g., charge\/discharge measurements, electrochemical impedance spectra, etc.)for various data-driven battery modelling workflows (e.g., state of health estimation, remaining useful life prediction).These projects align very well with what I\u2019ve learned and developed in my research, allowing me to bring my knowledge to customers by providing such features in MathWorks\u2019 ecosystem.<\/div>\n
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<\/span><\/a>).<\/div>\n
\"Picture3.png\"<\/div>\n
<\/div>\n

What\u2019s next?<\/h2>\n
I\u2019ll go back to school and continue working on both the DCRG project and my other projects. With an expected graduation on Spring 2026, I am considering positions in the industry where I can leverage my domain knowledge in battery modelling and a broader context of AI application in engineering.<\/div>\n
<\/div>\n<\/div>\n