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Remaining Energy Prediction for Lithium-Ion Batteries: A Machine Learning Approach
In this paper, we present the first study on predicting the remaining energy of a battery cell undergoing discharge over wide current ranges from low to high C-rates.
Integrated Optimal Control for Fast Charging and Active Thermal Management of Lithium-Ion Batteries in Extreme Ambient Temperatures
A state-feedback model predictive control algorithm is then developed for integrated fast charging and active thermal management.
Impact of Virtual Inertia on DC Grid Stability with Constant Power Loads
We derive a closed-form stability criterion that can be used to evaluate the impact of virtual inertia on the system stability, and demonstrate that, given a set of system parameters, the stability of a DC grid powering CPLs can be improved for a range of virtual inertia designs.
Integrating Physics-Based Modeling with Machine Learning for Lithium-Ion Batteries
This paper proposes two new frameworks to integrate physics-based models with machine learning to achieve high-precision modeling for LiBs.
Integrating Electrochemical Modeling with Machine Learning for Lithium-Ion Batteries
Mathematical modeling of lithium-ion batteries (LiBs) is a central challenge in advanced battery management.
