| Date | Time |
|---|---|
| 11 Dec 2020 | 05:30 PM SGT |
| 11 Dec 2020 | 10:00 PM SGT |
| 12 Dec 2020 | 03:00 AM SGT |
A feed forward deep neural network is trained with voltage, current, and temperature inputs and state of charge outputs to and from a lithium ion battery cell.
Operating conditions include different current levels and different temperatures. Achieved estimation accuracy was around 1% MAE.
Please allow approximately 45 minutes to attend the presentation and Q&A session. We will be recording this webinar, so if you can't make it for the live broadcast, register and we will send you a link to watch it on-demand.
Javier Gazzarri is a Principal Application Engineer at MathWorks in Novi, Michigan, USA, focusing on the use of physical modeling tools as an integral part of Model Based Design. Much of his work gravitates around battery modeling, from cell-level to system-level, parameter estimation for model correlation, battery management system design, balancing, aging, and state-of-charge estimation. Before joining MathWorks, Javier worked on fuel cell modeling at the National Research Council of Canada in Vancouver, British Columbia. He has a Bachelor’s degree in Mechanical Engineering from the University of Buenos Aires (Argentina), a MASc degree (Inverse Methods), and a PhD degree (Solid Oxide Fuel Cells) both from the University of British Columbia (Canada).
