Committee Chair

Wu, Dalei

Committee Member

Liang, Yu; Yuan, Yukun

Department

Dept. of Computer Science and Engineering

College

College of Engineering and Computer Science

Publisher

University of Tennessee at Chattanooga

Place of Publication

Chattanooga (Tenn.)

Abstract

Existing large-scale batteries, such as those used in electric vehicles, electric planes, and electric boats/ferries, are built from hundreds or thousands of battery cells connected in fixed series-parallel configurations. These rigid topologies limit efficient power management, making it difficult to respond to dynamic cell imbalance and thereby reducing overall battery operating time. To address these limitations, reconfigurable battery designs have been proposed where the interconnection topology among cells can be adjusted in real time to reflect evolving cell dynamics and uncertainties in the operating environment. In this thesis, we model reconfigurable batteries as dynamic graphs and investigate graph-based deep reinforcement learning approaches for adaptively optimizing cell-to-cell topology under practical operational constraints. We evaluate the proposed methods by using the open-source battery simulation platform PyBaMM, measuring performance in terms of voltage balancing and State of Charge (SOC) uniformity. Overall, this work establishes a foundation and offers insights for the development of future intelligent reconfigurable battery systems.

Acknowledgments

I would like to take this opportunity to express my sincere appreciation to my supervisors, Dr. Dalei Wu and Dr. Yu Liang, for their constant guidance, mentorship, and support during my master’s program. I am also grateful to another member of my thesis committee, Dr. Yukun Yuan, a valuable member of my committee, for his support in enhancing this research. I would like to take a moment to appreciate my research colleagues, Philip Segraves and Yifan Liu, for our collaboration, academic discussions, and contributions to our research and publications. I found our collaboration to be not only academically enriching but also rewarding. Finally, I am thankful to all those who, in one way or another, supported me during my entire master’s journey. This thesis is a reflection of my hard work, and I am thankful for all the support and encouragement I received during my journey.

Degree

M. S.; A thesis submitted to the faculty of the University of Tennessee at Chattanooga in partial fulfillment of the requirements of the degree of Master of Science.

Date

5-2026

Subject

Deep learning (Machine learning); Electric batteries; Reinforcement learning

Keyword

Dynamic reconfigurable battery; graph neural networks; deep reinforcement learning

Document Type

Masters theses

DCMI Type

Text

Extent

xii, 98 leaves

Language

English

Rights

http://rightsstatements.org/vocab/InC/1.0/

License

http://creativecommons.org/licenses/by/4.0/

Date Available

5-31-2027

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Available for download on Monday, May 31, 2027

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