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Yehui Han

Researcher at University of Wisconsin-Madison

Publications -  58
Citations -  3136

Yehui Han is an academic researcher from University of Wisconsin-Madison. The author has contributed to research in topics: Battery (electricity) & Inverter. The author has an hindex of 23, co-authored 58 publications receiving 2736 citations. Previous affiliations of Yehui Han include Massachusetts Institute of Technology & Wisconsin Alumni Research Foundation.

Papers
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Proceedings ArticleDOI

Design of module-integrated converters for photovoltaic strings

TL;DR: In this paper, a module-integrated converter (MIC) for photovoltaic (PV) modules connected in series in PV strings is proposed, which enables a range of independent maximum power point tracking (MPPT) for each module, and dramatically decreases the requirements of power rating and efficiency for power converters with the tradeoff of limited regulation ability.
Proceedings ArticleDOI

Mathematical modeling and performance analysis of battery equalization systems

TL;DR: Two types of battery equalization system structures are studied: series connection-based and module-based, and mathematical models are developed that describe the system-level behavior of the batteryequalization processes under these equalization structures.
Proceedings ArticleDOI

A GaN-based partial power converter with MHz reconfigurable switched-capacitor and RF SEPIC

TL;DR: In this article, a reconfigurable switched-capacitor (SC) based partial power architecture was proposed to enhance the performance of radio frequency (RF) resonant DC/DC converters with gallium nitride (GaN) power devices.
Proceedings ArticleDOI

High frequency resonant bidirectional SEPIC converter suitable for battery equalization and charger applications

TL;DR: In this article, a DC-DC converter that can be used in battery equalization and charging and discharging applications is proposed, analyzed, and realized, and then verified by simulation and experimental results.
Book ChapterDOI

A bayesian approach to battery prognostics and health management

TL;DR: Particle filters (PFs) are a novel class of nonlinear filters that combine Bayesian learning techniques with importance sampling to provide good state tracking performance while keeping the computational load tractable.