no code implementations • 30 May 2024 • Hao Tu, Xinfan Lin, Yebin Wang, Huazhen Fang
Essential to various practical applications of lithium-ion batteries is the availability of accurate equivalent circuit models.
no code implementations • 23 Apr 2024 • Hao Tu, Manashita Borah, Scott Moura, Yebin Wang, Huazhen Fang
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.
no code implementations • 5 Apr 2024 • Zehui Lu, Hao Tu, Huazhen Fang, Yebin Wang, Shaoshuai Mou
A state-feedback model predictive control algorithm is then developed for integrated fast charging and active thermal management.
no code implementations • 25 Oct 2023 • Amir Farakhor, Di wu, Yebin Wang, Huazhen Fang
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.
no code implementations • 12 Jan 2023 • Amir Farakhor, Di wu, Yebin Wang, Huazhen Fang
An optimal power management approach is developed to extensively exploit the merits of the proposed design.
no code implementations • 24 Dec 2021 • Hao Tu, Scott Moura, Yebin Wang, Huazhen Fang
This paper proposes two new frameworks to integrate physics-based models with machine learning to achieve high-precision modeling for LiBs.
no code implementations • 3 Jul 2019 • Ankush Chakrabarty, Devesh K. Jha, Gregery T. Buzzard, Yebin Wang, Kyriakos Vamvoudakis
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.