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20152023
most citedIntegrating Physics-Based Modeling with Machine Learning for Lithium-Ion Batteries

188 citations · 504 across the 14 of their papers we have counts for

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Showing eess.SYShow all

7 papers · 1 filter

eess.SY2023★ 45 cited

A Novel Modular, Reconfigurable Battery Energy Storage System: Design, Control, and Experimentation

Amir Farakhor, Di Wu, Yebin Wang +1

This paper presents a novel modular, reconfigurable battery energy storage system. The proposed design is characterized by a tight integration of reconfigurable power switches and…

eess.SY2020★ 72 cited

Real-Time Optimal Lithium-Ion Battery Charging Based on Explicit Model Predictive Control

Ning Tian, Huazhen Fang, Yebin Wang

The rapidly growing use of lithium-ion batteries across various industries highlights the pressing issue of optimal charging control, as charging plays a crucial role in the health…

eess.SY2020★ 1 cited

One-Shot Parameter Identification of the Thevenin's Model for Batteries: Methods and Validation

Ning Tian, Yebin Wang, Jian Chen +1

Parameter estimation is of foundational importance for various model-based battery management tasks, including charging control, state-of-charge estimation and aging assessment. Ho…

eess.SY2019

Safe Approximate Dynamic Programming Via Kernelized Lipschitz Estimation

Ankush Chakrabarty, Devesh K. Jha, Gregery T. Buzzard +2

We develop a method for obtaining safe initial policies for reinforcement learning via approximate dynamic programming (ADP) techniques for uncertain systems evolving with discrete…

eess.SY2019★ 47 cited

Nonlinear Double-Capacitor Model for Rechargeable Batteries: Modeling, Identification and Validation

Ning Tian, Huazhen Fang, Jian Chen +1

This paper proposes a new equivalent circuit model for rechargeable batteries by modifying a double-capacitor model proposed in [1]. It is known that the original model can address…

eess.SY2019★ 5 cited

Robust Extended Kalman Filtering for Systems with Measurement Outliers

Huazhen Fang, Mulugeta A. Haile, Yebin Wang

Outliers can contaminate the measurement process of many nonlinear systems, which can be caused by sensor errors, model uncertainties, change in ambient environment, data loss or m…