NDLTD Global ETD Search
  • Refine Query
  • Source
  • Publication year
  • to
  • Language
  • No language data
  • Tagged with
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • About
  • The Global ETD Search service is a free service for researchers to find electronic theses and dissertations. This service is provided by the Networked Digital Library of Theses and Dissertations.
    Our metadata is collected from universities around the world. If you manage a university/consortium/country archive and want to be added, details can be found on the NDLTD website.

Search results

Showing 1 to 1 of 1 (0.036 seconds)

Spelling suggestions: "subject:"lowfidelity model"" "subject:"lowfidelity godel""

1

Systems Modeling of Thermal Management System for Battery Electric Vehicles

Parikesit Pandu Dewanatha (20766728) 25 February 2025 (has links)
<p dir="ltr">The rise of battery electric vehicles (BEVs) has been driven by global initiatives to reduce carbon emissions and support technological advancements in battery technology. However, heat loads in these vehicles are inherently transient, and traditional thermal management system (TMS) design approaches are not suitable for designing TMS that allow up-front consideration of transient operation. Graph-based modeling has been explored as a tool for modeling dynamic systems, including thermal systems, due to its modularity and suitability for control design and optimization. It has been successfully applied to air-cycle machines and component-level thermal modeling. For BEV applications, there is an opportunity to expand graph-based modeling into system-level TMS modeling. This approach can solve the complexities of the BEV thermal management, especially with the needs of the rapidly evolving automotive industry.</p><p dir="ltr"> In this thesis, I present the modeling of a BEV TMS using a graph-based modeling framework at both the component and cycle levels. By developing a physics-based, reduced-order model, the thermal interactions within individual components and between connected components are analyzed and discussed in detail. Furthermore, I validate the graph-based model against a high-fidelity benchmark model to assess its accuracy and reliability. The validation process involves simulating and analyzing the dynamic state variables and key performance parameters of the TMS, including temperature, pressure, and enthalpy. These metrics are compared to a high-fidelity benchmark model across various operating conditions. The validated framework provides a strong foundation for future advancements in thermal management systems for BEVs.</p>
Electric Vehicle
Thermal Management System
Battery Electric Vehicle
System Modeling
System Validation
Thermodynamics
Python
Dymola
Reduced-Order Model
Low-Fidelity Model
HVAC
Refrigeration Cycle
Two-Phase Flow
Closed-Loop Analysis
Open-Loop Analysis
Data Analysis
Transient Response Analysis
EV
Vapor Compression Cycle
Cabin Cycle
Battery Cycle
Electronics Cooling Cycle
Graph-Based Model

Page generated in 0.0386 seconds