Guest lecture, Iowa Lakes Community College, Online
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.
Conference talk, 243rd ECS Meeting, Boston, MA, USA
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.
Guest lecture, Iowa Lakes Community College, Onlne
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.