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Found 6 papers, 0 papers with code
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.
Scalable Optimal Power Management for Large-Scale Battery Energy Storage Systems
Since the number of clusters is much fewer than the number of cells, the proposed approach significantly reduces the computational costs, allowing optimal power management to scale up to large-scale BESS.
A Novel Modular, Reconfigurable Battery Energy Storage System: Design, Control, and Experimentation
An optimal power management approach is developed to extensively exploit the merits of the proposed design.
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.
Safe Approximate Dynamic Programming Via Kernelized Lipschitz Estimation
We develop a method for obtaining safe initial policies for reinforcement learning via approximate dynamic programming (ADP) techniques for uncertain systems evolving with discrete-time dynamics.
