Combining physics-based and machine learning methods to accelerate innovation in sustainable transportation and beyond: a control perspective
Author:
Affiliation:
1. Stanford University,Energy Science & Engineering,Stanford,CA
Funder
Stanford University
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/10155646/10155787/10156343.pdf?arnumber=10156343
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1. Lightweight Electrochemical Hybrid Modeling Approach for Li-Ion Batteries Using Gaussian Process Regression;2024 American Control Conference (ACC);2024-07-10
2. Cycle Life Prediction for Lithium-ion Batteries: Machine Learning and More;2024 American Control Conference (ACC);2024-07-10
3. Unraveling the Degradation Mechanisms of Lithium-Ion Batteries;Energies;2024-07-09
4. Probabilistic machine learning for battery health diagnostics and prognostics—review and perspectives;npj Materials Sustainability;2024-06-03
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