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http://hdl.handle.net/11375/28374
Title: | Design Methodology and Modelling of a Fast Charging Battery Module and Thermal Management System for Electric Vehicles |
Other Titles: | Design Methodology of a Fast Charging Battery Module |
Authors: | Lempert, Jeremy Michael |
Advisor: | Emadi, Ali Cotton, James |
Department: | Mechanical Engineering |
Keywords: | electric vehicles;fast charging;CFD;thermal management;neural networks;lithium-ion;thermal modelling |
Publication Date: | 11-Nov-2020 |
Abstract: | With electric vehicles (EVs) emerging as a means to reduce transportation-related greenhouse gas emissions, battery charging times and range anxiety remain a key sticking point. While the electrochemical batteries that power EVs cannot compete with liquid fuels on the basis of energy density, higher efficiencies and large battery packs make it possible to achieve competitive driving range. Reduced charging times, however, remain a challenge spanning many disciplines, where cell selection and thermal management play a critical role. For the development of a fast charging, liquid-cooled battery module, the research outlined in this thesis presents a design methodology including the processes of selection and characterization of a suitable battery cell, modelling of heat generation inside the cells, and design and modelling of a thermal management system. Four different cells are compared. The cells are first characterized in a laboratory, and suitability for fast charging is evaluated based on the experimental results. Simplified thermal models are used for comparison of the cells. Factors such as charging efficiency and required cooling system size are considered. A three-cell, liquid-cooled test module is designed and constructed for a selected cell, and further characterization is conducted in order to develop a detailed loss model. Thermal modelling is accomplished using numerical models, developed using knowledge and assumptions of the underlying physics and material properties, and using a neural network-based approach—which can be developed without such knowledge or assumptions, but requires data from laboratory testing of the cell, module, or pack of specific interest. Results from the numerical model and neural network-based model are compared to experimental data at charge rates up to 5C and for a cycle of repeated driving with periodic fast charging. For a 5C charge, a peak temperature of 34.6 °C is measured in the laboratory, and modelled to within 0.6 °C. |
URI: | http://hdl.handle.net/11375/28374 |
Appears in Collections: | Open Access Dissertations and Theses |
Files in This Item:
File | Description | Size | Format | |
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Lempert_Jeremy_M_2020August_MASc.pdf | 19.31 MB | Adobe PDF | View/Open | |
Lempert - Final Thesis Submission Sheet1_JLsigned_AE signed.pdf | 448.56 kB | Adobe PDF | View/Open |
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