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<p>As consumer adoption and total energy consumption of electric vehicles continues to rapidly increase, it is important to develop comprehensive system modeling frameworks that consider the complex interactions of their mechanical, electrical, and thermal subsystems to guide component technology development. This thesis studies the influence of cabin glass properties on the performance of an electric vehicle thermal system and overall cabin design considerations. The work first builds a generic long-range electric vehicle dynamic thermal system model while considering the system architecture, component sizing, control scheme, and glass properties. This comprehensive system model is used to assess the influence of cabin glass radiative properties on vehicle performance. The system model incorporates simplified models for all salient components in the electric traction drive, cabin HVAC, and battery subsystems, and uses a higher fidelity cabin thermal model that is able to capture the individual properties of the cabin glass used in the vehicle. To study the cabin model in isolation, a heat-up scenario is used to find that a cabin air temperature reduction of 8 °C through the use of different glass properties alone. Additionally, the cabin model is run repeatedly to produce a large data set that is trained using a machine learning regression model. This surrogate regression model that is used to reduce the computational time allowing for fast studies of glass properties and build an application engineering tool. The overall system performance is then evaluated under a dynamic NEDC drive cycle which is repeated until battery depletion to determine a vehicle range. A system validation is done on the HVAC subsystem by using steady-state thermodynamic analysis and comparing to the dynamic system model. This results in good agreement between four different subsystem modeling approaches. The system model is used to study five different glazing design cases, each corresponding to different transmission and reflection properties of the glass, by predicting their impact on the vehicle range. The cases span all theoretically possible glass properties while also enabling inspection of practical glass technologies that are available or under development to be adopted in modern electric vehicles. The influence of glass on vehicle range is then further compared at various locations across the United States to understand and illustrate the effects of ambient conditions and solar load. The system model predicts a vehicle range of 188.5 miles under a high solar loading scenario typical for Phoenix, AZ using traditional glass properties, which increases to a range of 221.6 miles using high-performance glass properties, representing a significant potential gain of 33.1 miles using technologies available on the market today. Under this same loading scenario, the glass properties at their extreme physical limits could theoretically affect the vehicle range by up to 92.5 miles. The influence of the glass properties is location-specific, and the model predicts that using the same glass at different locations can affect the range of vehicle by up to 100.8 miles for traditional glass properties and 73.4 miles for high-performance glass properties. </p>
| Identifer | oai:union.ndltd.org:purdue.edu/oai:figshare.com:article/21657290 |
| Date | 01 December 2022 |
| Creators | Andrew Penning (14202806) |
| Source Sets | Purdue University |
| Detected Language | English |
| Type | Text, Thesis |
| Rights | CC BY 4.0 |
| Relation | https://figshare.com/articles/thesis/THERMAL_SYSTEM_ANALYSIS_OF_AN_ELECTRIC_VEHICLE_AND_THE_INFLUENCE_OF_CABIN_GLASS_PROPERTIES/21657290 |
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