Welcome to the first FME BATTERY Publication Webinar!
Svenas Burba, PhD candidate at the University of Agder (UiA), will present the research from his recent scientific publication: "Data‐Driven Entropy Coefficient Estimation for Large Format Lithium‐Ion Batteries in Batteries & Supercaps ".
The first publication from the FME BATTERY family can help predict battery performance in colder conditions, more quickly and at a lower cost.
Electric vehicles and large-scale energy storage systems rely on lithium-ion batteries that must perform reliably in all kinds of weather. But here’s the challenge: batteries behave differently at different temperatures. Cold conditions, in particular, can reduce efficiency and shorten range. To predict and optimise battery behaviour, researchers need to understand how temperature affects the battery’s voltage, a relationship influenced by a thermodynamic property called entropy.
“In this work we propose faster data-driven and empirical entropy estimation methods that use cell charge/discharge voltage profiles at various operating temperatures from 100Ah prismatic lithium-iron-phosphate/graphite cell to estimate entropy coefficient,” says Burba Svenas, PhD Candidate at the University of Agder (UiA). He explains that the research demonstrate increased model voltage prediction accuracy across a range of temperatures, with best predictions at low temperatures.
“This work offers fast and cheap alternatives for cell entropy estimation alongside experimental potentiometric and calorimetric methods,” says Svenas.
We will send out a Teams link a few days before the webinar. If you wish to add the webinar to your calendar before then, the registration confirmation will have an attachment with the webinar time that you can add to your calendar.
Looking forward to seeing you!
