Search Results for author: Huazhen Fang

Found 9 papers, 1 papers with code

Model-free Resilient Controller Design based on Incentive Feedback Stackelberg Game and Q-learning

no code implementations13 Mar 2024 Jiajun Shen, Fengjun Li, Morteza Hashemi, Huazhen Fang

In the swift evolution of Cyber-Physical Systems (CPSs) within intelligent environments, especially in the industrial domain shaped by Industry 4. 0, the surge in development brings forth unprecedented security challenges.

Q-Learning

Scalable Optimal Power Management for Large-Scale Battery Energy Storage Systems

no code implementations25 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.

Management

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

no code implementations12 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.

Management

BattX: An Equivalent Circuit Model for Lithium-Ion Batteries Over Broad Current Ranges

no code implementations11 Nov 2022 Nikhil Biju, Huazhen Fang

Advanced battery management is to lithium-ion battery systems as the brain is to the human body.

Management

High-Order Leader-Follower Tracking Control under Limited Information Availability

no code implementations12 Jul 2022 Chuan Yan, Tao Yang, Huazhen Fang

Limited information availability represents a fundamental challenge for control of multi-agent systems, since an agent often lacks sensing capabilities to measure certain states of its own and can exchange data only with its neighbors.

Vocal Bursts Intensity Prediction

Implicit Particle Filtering via a Bank of Nonlinear Kalman Filters

no code implementations9 May 2022 Iman Askari, Mulugeta A. Haile, Xuemin Tu, Huazhen Fang

The implicit particle filter seeks to mitigate particle degeneracy by identifying particles in the target distribution's high-probability regions.

Integrating Physics-Based Modeling with Machine Learning for Lithium-Ion Batteries

no code implementations24 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.

BIG-bench Machine Learning Management

Integrating Electrochemical Modeling with Machine Learning for Lithium-Ion Batteries

no code implementations22 Mar 2021 Hao Tu, Scott Moura, Huazhen Fang

Mathematical modeling of lithium-ion batteries (LiBs) is a central challenge in advanced battery management.

BIG-bench Machine Learning Management

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