Published in Iowa State University Dataset, 2023
Artwork Size: 11186085527 Bytes Pages: 11186085527 Bytes
Recommended citation: Adam Thelen, Tingkai Li, Jinqiang Liu, Chad Tischer, Chao Hu, "ISU-ILCC Battery Aging Dataset." Iowa State University Dataset, 2023.
Download Paper
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.
Download Paper
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.
Download Paper
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.
Download Paper
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.
Download Paper
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.
Download Paper
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.
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.
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.