We attended the ACC2023 and we Organize a Tutorial session on Combining Physics and Machine Learning Methods to Accelerate Innovation in Sustainability: A Control Perspective with the following invited speakers:
- 10:00-10:30, Combining Physics-Based and Machine Learning Methods to AccelerateInnovation in Sustainable Transportation and Beyond: A Control Perspective, Gabriele Pozzato, Stanford University, Simona Onori, Stanford University
- 10:30-10:45, Machine Learning in Lithium-Sulfur Battery Modeling and Control: Key Challenges and Opportunities Hosam K. Fathy, University of Maryland
- 10:45-11:00, Information-Rich, Operation-Robust, Physics-Informed Features for Battery Health Estimation and Prediction under Limited Field Data, Anna Stefanopoulou, University of Michigan
- 11:00-11:15, Tracking Detailed Battery State of Health with Explainable AI Amalie Trewartha, Toyota Research Institute
- 11:15-11:30, Integrating Physics and Machine Learning for Battery Management in the Cloud, Weihan Li, RWTH Aachen University
Simone Fasolato presented the paper:
Fasolato, S., Allam, A., Li, X., Lee, D., Ko, J., Onori, S., “Reduced order model of lithium-iron phosphate battery dynamics: A POD-Galerkin approach”, IEEE Control Systems Letters, vol. 7, pp. 1117-1122, 2023
And Zahra Nozarijouybari (Univ. of Maryland) presented:
Nozarijouybari, Z., Allam, A., Onori, S., and Fathy, H. K., "On the Feasibility of Electrode Concentration Distribution Estimation in Single-Particle Lithium-Ion Battery Models," in IEEE Control Systems Letters, vol. 7, pp. 1099-1104, 2023
