Search Results for author: Hao Tu

Found 7 papers, 0 papers with code

On Simulation of Power Systems and Microgrid Components with SystemC-AMS

no code implementations4 Jul 2024 Rahul Bhadani, Satyaki Banik, Hao Tu, Srdjan Lukic, Gabor Karsai

This paper presents a faster method for simulating the electromagnetic transient response of microgrid components using SystemC-AMS.

System Identification for Lithium-Ion Batteries with Nonlinear Coupled Electro-Thermal Dynamics via Bayesian Optimization

no code implementations30 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.

Bayesian Optimization

Remaining Discharge Energy Prediction for Lithium-Ion Batteries Over Broad Current Ranges: A Machine Learning Approach

no code implementations23 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.

Impact of Virtual Inertia on DC Grid Stability with Constant Power Loads

no code implementations8 Nov 2022 Hao Tu, Hui Yu, Srdjan Lukic

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

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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