Date of Award

Summer 8-21-2019

Publication Type

Master Thesis

Degree Name

M.A.Sc.

Department

Mechanical, Automotive, and Materials Engineering

Keywords

Cabin model, Electric vehicle, Embedded CFD, Exergy, Heat pump model

Supervisor

Jianu, O.A.

Rights

info:eu-repo/semantics/openAccess

Abstract

The limited driving range, due to the poor storage capability of electric batteries, represents one of the greatest challenges in the development of electric vehicles. This concern leads to an extremely demanding design of every component within the vehicle powertrain in order to achieve their maximum energy efficiency and decrease the demand on the battery. Additionally, in cold climate conditions, the efficiency of the heating system of an electric vehicle decreases and it can result in further reducing its driving range. In this thesis, 1D modelling in Amesim will be used to analyze different concepts of thermal management for an electric vehicle. Firstly, a 1D model of the original refrigeration system of the chosen vehicle (Fiat 500e) was built by implementing the data of each component. The components were individually modelled, then assembled within a system level model and the final model was validated. Secondly, starting with the validated system, a 1D model of a heat pump system was proposed as a replacement for the commonly used positive temperature coefficient heater (PTC). This model was obtained exploiting the information available on the refrigeration system and assuming all the unknown characteristics. An energy and exergy analysis was carried out to determine the individual components and overall system performance. Finally, the vehicle cabin was modelled exploiting a new Embedded CFD tool of Amesim capable of combining the advantages of 1D and 3D modelling, hence providing lower CPU resources and time consumption required to perform a simulation due to the lower effort to model the temperature distribution inside the cabin. This approach gives also the chance to study zonal heating and cooling of the cabin in order to reduce the energy demand on the battery. Numerous simulations were performed to analyze the impact of different settings and parameters validating each of them through comparison with experimental data.

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