Machine learning and big data applied to energy storage system modeling and control
Organizers
Changfu Zou, Yicun Huang, Remus Teodorescu, Nadia Yousfi-Steiner, Weihan Li, Davide Martino Raimondo, Ramon Costa-Castelló, Zahra Nozarijouybari, Hosam K. Fathy
Abstract:
The rapid growth in the vehicle electrification and grid storage market has given rise to the need for intelligent and efficient modeling and control of energy storage systems. Despite the popularity of physics-based models in energy-related research, their applications have been greatly hindered by computational burden and parameterization complexity. Machine learning and big data, proven successful in many other disciplines, have sparked tremendous research interests in the field of energy storage. This open invited track provides an opportunity for like-minded researchers to explore, advertise and exchange works at a time frame where the trend of integrating machine learning and big data in control strategies has become eminent. The discussions that follow are expected to make an immediate impact on the participating researchers and initiate collaborations between research groups around the globe.
Important Dates
Invited Paper Submission: November 18, 2022 (extended, firm)
Discussion Paper: November 30, 2022
Notification of Acceptance: February 21, 2023
Final Paper Submission: March 31, 2023
Conference Dates: July 9-14, 2023
Code for submitting contributions: 457a7
Full description: https://www.ifac2023.org/media-download/116/616075e3c8bccaab/
