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A list of all the posts and pages found on the site. For you robots out there, there is an XML version available for digesting as well.
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Posts
Future Blog Post
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Blog Post number 4
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Blog Post number 1
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publications
ISU-ILCC Battery Aging Dataset
Published in Iowa State University Dataset, 2023
Recommended citation: Adam Thelen, Tingkai Li, Jinqiang Liu, Chad Tischer, Chao Hu, "ISU-ILCC Battery Aging Dataset." Iowa State University Dataset, 2023.
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Physics-Informed Neural Networks for Degradation Diagnostics of Lithium-Ion Batteries
Published in In the proceedings of 49th Design Automation Conference (DAC), 2023
Recommended citation: Sina Navidi, Adam Thelen, Tingkai Li, Chao Hu, "Physics-Informed Neural Networks for Degradation Diagnostics of Lithium-Ion Batteries." In the proceedings of Volume 3A: 49th Design Automation Conference (DAC), 2023.
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A Comparative Study on Physics-Informed Machine Learning for Battery Degradation Diagnostics
Published in ECS Meeting Abstracts 243, 2023
Recommended citation: Sina Navidi, Adam Thelen, Tingkai Li, Chao Hu, "A Comparative Study on Physics-Informed Machine Learning for Battery Degradation Diagnostics." ECS Meeting Abstracts 243, 2023.
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Degradation Diagnostics of Lithium-Ion Batteries Cycled Under Varying Conditions
Published in ECS Meeting Abstracts 243, 2023
Recommended citation: Tingkai Li, Adam Thelen, Naba Karan, Jinqiang Liu, Chad Tischer, Chao Hu, "Degradation Diagnostics of Lithium-Ion Batteries Cycled Under Varying Conditions." ECS Meeting Abstracts 243, 2023.
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Physics-informed machine learning for battery degradation diagnostics: A comparison of state-of-the-art methods
Published in Energy Storage Materials, 2024
Recommended citation: Sina Navidi, Adam Thelen, Tingkai Li, Chao Hu, "Physics-informed machine learning for battery degradation diagnostics: A comparison of state-of-the-art methods." Energy Storage Materials, 2024.
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Predicting battery lifetime under varying usage conditions from early aging data
Published in Cell Reports Physical Science, 2024
Recommended citation: Tingkai Li, Zihao Zhou, Adam Thelen, David Howey, Chao Hu, "Predicting battery lifetime under varying usage conditions from early aging data." Cell Reports Physical Science, 2024.
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UConn-MathWorks LFP/Gr Second-Life Battery Aging Dataset
Published in REIL Datasets, 2025
Recommended citation: Tingkai Li, Aidan Lawlor, Adarsh Narasimhamurthy, Xiaomeng Peng, Chao Hu, "UConn-MathWorks LFP/Gr Second-Life Battery Aging Dataset." REIL Datasets, 2025.
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Coupling a capacity fade model with machine learning for early prediction of the battery capacity trajectory
Published in Applied Energy, 2025
Recommended citation: Tingkai Li, Jinqiang Liu, Adam Thelen, Ankush Mishra, Xiao-Guang Yang, Zhaoyu Wang, Chao Hu, "Coupling a capacity fade model with machine learning for early prediction of the battery capacity trajectory." Applied Energy, 2025.
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ISU-UConn LFP/Graphite Emulated Degradation Dataset
Published in REIL Datasets, 2026
Recommended citation: Yifan Zhang, Tingkai Li, Shan Hu, Chao Hu, "ISU-UConn LFP/Graphite Emulated Degradation Dataset." REIL Datasets, 2026.
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Conformalized multi-step forecasting for high-rate dynamic systems
Published in Structural and Multidisciplinary Optimization, 2026
Recommended citation: Yang Kang Chua, Tingkai Li, Sina Navidi, Arman Razmarashooli, Mohammad Mundiwala, Daniel A Salazar Martinez, Metrid Okumu, Chao Hu, Simon Laflamme, Paul T Schrader, "Conformalized multi-step forecasting for high-rate dynamic systems." Structural and Multidisciplinary Optimization, 2026.
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Benchmarking half-cell model fitting approaches for lithium-ion battery degradation diagnostics
Published in eTransportation, 2026
Recommended citation: Tingkai Li, Yifan Zhang, Benjamin Nowacki, Sina Navidi, Thomas Schmitt, Shan Hu, Chao Hu, "Benchmarking half-cell model fitting approaches for lithium-ion battery degradation diagnostics." eTransportation, 2026.
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Practical regeneration of Li-ion batteries for a second life: The role of degradation mechanisms
Published in Journal of Power Sources, 2026
Recommended citation: Jayanth Ramamurthy, Adam Thelen, Mihir Ojha, Shervin Alaei, Ben Nowacki, Tingkai Li, Sina Navidi, Weimin Jiao, Todd A Kingston, Matthieu Dubarry, "Practical regeneration of Li-ion batteries for a second life: The role of degradation mechanisms." Journal of Power Sources, 2026.
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Enhancing Engineering Education through Student-Led Leadership Development
Published in 2026 ASEE Annual Conference & Exposition, 2026
Recommended citation: Elika Shams, Tingkai Li, Maxwell Bartlett, Aayushi Verma, Alireza Miraliakbar, Kaitlyn G Mahoney, Yinyu Wang, Mohamadamin Rajabinezhad, Oluwaseun Moses Akinola, Aida Ghiaei, "Enhancing Engineering Education through Student-Led Leadership Development." 2026 ASEE Annual Conference & Exposition, 2026.
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Human-Centered Leadership in an AI-Driven World: Practitioner Reflections on Engineering Leadership Education
Published in 2026 ASEE Annual Conference & Exposition, 2026
Recommended citation: Emily Moore, Stacie Edington, Beverly Kris Jaeger-Helton, Kenneth W Lamb, James N Magarian, Cindy Rottmann, Amin Azad, Meagan R Kendall, Tingkai Li, Monica Pheifer, Kim Graves Wolfinbarger, Elika Shams, Michael P Manning, "Human-Centered Leadership in an AI-Driven World: Practitioner Reflections on Engineering Leadership Education." 2026 ASEE Annual Conference & Exposition, 2026.
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talks
Introduction to Battery Data Analysis in Python
Published:
A guest lecture to students in ELT 493 – Industrial Networking and Data Acquisition. The talk covers the basics of python data analysis and battery data analytics.
Degradation Diagnostics of Lithium-Ion Batteries Cycled Under Varying Conditions
Published:
Lithium-ion (Li-ion) battery cell degradation is a complex process driven primarily by how the cells are used. Stress factors like temperature, charging and discharging rates, and the depth of discharge all influence the rate and type of degradation modes cells experience. Understanding the possible degradation modes and their severity under a given usage profile can help to optimize cell design, manufacturing, and control. In addition, elucidating the relationship between different degradation modes and their effects on cell capacity fade can help improve battery lifetime modeling strategies. In this study, we examine non-intrusive degradation diagnostics based on differential voltage analysis (half-cell curve fitting) and confirm the existence of certain degradation modes and mechanisms by destructively analyzing specific cells from a newly generated battery aging dataset of 245 nickel-manganese-cobalt/graphite (NMC) cells cycled under varying rates and depths of discharge. The results help to establish the link between the various aging stress factors (charging and discharging rates and depth of discharge), the measurable voltage vs. capacity data, and the observed capacity fade trends. We also investigate the impact of dominant degradation modes on battery lifetime and combine the destructive analyses with cycle aging data to understand the relationship between the rate of early-life degradation and total cell lifetime.
Introduction to Battery Data Analysis in Python
Published:
A guest lecture to students in ELT 493 – Industrial Networking and Data Acquisition. The talk covers the basics of python data analysis and battery data analytics.