Author

Date of Award

2026

Document Type

Thesis

Degree Name

Master of Science in Engineering (MSE)

Department

Electrical and Computer Engineering

Committee Chair

Avimanyu Sahoo

Committee Member

Laurie Joiner

Committee Member

George Nelson

Research Advisor

Avimanyu Sahoo

Subject(s)

Lithium ion batteries--Thermal properties, Lithium ion batteries--Performance--Simulation methods

Abstract

The recent development of lithium-ion battery technology has fueled its adoption in electric vehicles (EVs) as the primary power source due to its high energy and power density. However, the high energy density comes at the cost of higher safety requirements. To ensure the lithium-ion battery pack's operational safety, performance optimization, and longevity, a Battery Management System (BMS), an electronic controller, is employed. Recent reports of battery pack overheating, fire, and explosion in EVs expose the limitations of the BMS in detecting the abnormalities and preventing them from propagating to other cells. To address these limitations, in the first part of the thesis, a detailed literature review of the thermal modeling, including heat generation, transfer, and the effect of thermal interconnection among the cells, thermal parameter identification, and thermal management systems are presented. In addition, the survey also discusses the gaps, such as the need for a generalized modeling of packs including cooling arrangements, and their implementation in BMS found in the literature and suggest future direction in thermal management of battery packs. In the second part, the thesis addresses the critical thermal safety challenges by developing a generalized control-oriented, interconnected thermal model and an associated identification framework for Li-ion battery packs, intended for implementation in BMS to enable abnormality detection and monitoring at the cell, module, and pack levels. The battery pack is modeled as an electro-thermal network graph that captures heat generation and transfer interactions among cells, neighboring units, and cooling channels. The resulting dynamics are expressed as a semi-linear system, where inter-cell (direct and through the cooling channel) heat conduction is encoded through the graph Laplacian and the control input enters nonlinearly. To identify the model parameters (thermal conductance and internal resistances), a multi-excitation perturbation framework is introduced that allows identification of heat generation terms (internal resistances) and thermal coupling parameters for heat transfer separately. A reduced-order thermal model is then developed to characterize the dominant group-level thermal behavior of the pack. This reduced model is identified using temporally collected data from a single experiment, enabling efficient characterization of large-scale battery packs. Simulation studies on a 3S3P pack configuration are also presented to validate the accuracy and effectiveness of the proposed modeling and identification framework, demonstrating its potential for enhancing thermal monitoring and supporting advanced BMS design.

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