The talk starts at 12:15.

Please note that due to COVID-19, the participants can watch the streamed talk on Teams with a link (below).

Speaker: Clara Bertinelli Salucci

Location: Zoom (https://uio.zoom.us/j/62383562889?pwd=RCtXb3U0TklkRENEaHFweHJJc3BKZz09)

Title:  Lithium-ion battery degradation models under dynamic conditions

Abstract: Longevity and safety of lithium-ion batteries are facilitated by efficient monitoring and adjustment of the battery operating conditions. Hence, it is crucial to implement fast and accurate algorithms for State of Health monitoring on the Battery Management System. The task is challenging due to the complexity and multitude of the factors contributing to the battery degradation, especially because the different degradation processes occur at various timescales and their interactions play an important role. Data-driven methods bypass this issue by approximating the complex processes with statistical or machine learning models: they rely solely on the available cycling data, while remaining agnostic to the underlying physical processes.

In this seminar I will first illustrate the results we achieved with a Multivariable Fractional Polynomial regression on public laboratory data characterised by varying loads; then, I will present preliminary results from a semi-supervised learning approach on real operational data from electric or hybrid vessels.

Welcome!
Best regards,
Thea Roksvåg and Lars Henry Berge Olsen.