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With increasing awareness of climate change, governments and organizations have made it their mission to see a greener future. Countries like Norway, South Korea, and Canada have promised to ban internal combustion engines (ICE) by 2025-2035. Growing demand for cleaner modes of travel have taken over the market, causing everyone to look at electric vehicles for the solution. Tesla’s revenue has tripled in the past five years, 15 new electric car manufacturer shave joined, and almost all big-name ICE companies have started producing electric/hybrid cars. As the number of electric vehicles increases, a solution to long charging times will be needed to keep up with the high-power-density fuel used in ICE. Charging stations are increasing in power ratings as Tesla introduces their 250-kW supercharger and EVBox with their 350-kW Ultronig stations. These stations are comprised of power modules that stack together to reach the desired power rating. Designing, testing, and implementing power modules for electric vehicles can be a complex process due to thermal efficiency and packaging challenges. To address these issues, it is essential to establish a design methodology for power modules that takes into account validation and packaging considerations. This thesis presents a design methodology for heat exchangers that allows for rapid prototyping with sufficient accuracy, approximately below 10%. The study includes numerical simulations, reduced modeling, and experimental validation, which can increase confidence during the design phase and reduce design times. Using reduced models for quick calculations instead of relying solely on numerical models can further expedite the process. A reliable and adaptable analytical methodology for heat exchanger design is crucial for successful optimization setup. / Thesis / Master of Applied Science (MASc)

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Concerns of the impact of greenhouse gasses (GHG) are leading heavy industry users to explore energy reduction strategies such as the conservation of electricity use in ventilated machines by the use of variable-cooling systems. For these strategies to be implemented, a thermal model of the system is required. This study focuses on the thermal modelling of through-ventilated, industrial, electric machines that employ a variable-cooling strategy, using only on-line data collected during regular machine operation. Two empirical thermal models were developed: a first-order model, and a second-order model which was extended from the first-order based on its performance. By means of an energy-balance, the first-order model was able to define an estimation of the motor temperature based on only a single variable, and thus was able to be fit directly to complete process-cycle data to determine the parameter. Over the 18 process-cycle samples, this parameter was found to vary by as much as $\pm$10\%, therefore, when a generalized model was proposed using the median value of the parameter, the maximum error seen over the process cycles was 9.0 $^{\circ}C$, with a maximum average error over a process-cycle of 4.2 $^{\circ}C$. An effort was made to determine the effects of reduced cooling on the model by performing reduced-cooling experiments during machine cool-downs, however the thermal-time constant, which directly relates the heat-transfer rate to the system capacitance, was found to vary by as much as 47\%, suggesting that the system's capacitance was changing, and that the first-order model was not accurate enough to distil these effects. A key obervation of the performance of the first-order model was that in heating it would under-predict the machine temperature, and in cooling would over-predict, suggesting that an additional heat-transfer path existed to the cooling air through some additional thermal capacitance. In an effort to include higher-order effects so that reduced-cooling effects could be established, a second-order model was developed by adding an additional lumped-node to the system, introducing the supposed additional conduction/capacitive path, where the heat-generating node was considered analogous to the motor's armature, and the additional node was considered as a thermal-sink. This model was then numerically fit to the cool-down data for both maximum and reduced flow-rate cases in order to identify the system's main heat transfer parameters, however, once again, a large variance in the parameters was found. Through model simulation, this was determined to be the result of the system not starting at a steady-state temperature distribution, which resulted in the parameter estimation under-predicting the true values. As such, the upper-limits of the parameter spreads were used to identify the model. Assuming the system's heat generation was due to Joule-losses only, the second-order model was found to perform marginally better than the first-order model, with a maximum error of 8.6 $^{\circ}C$, and a maximum average error of 3.3 $^{\circ}C$ over the process-cycles. Though the second-order model typically performed better than the first-order model in cooling, it was found that the model would vary between over-predicting and under-predicting the machine temperature, indicating that additional and higher-order core losses may play a role in the heating of the machine. Although the first-order model was found to be slightly less-accurate than that of the second-order, the first-order model has a much simpler and far less intrusive identification scheme than that of the second-order model with a relatively low loss in accuracy. As a result, it would be possible to to use the first-order model for on-line temperature monitoring of the machine by performing tests during operation where the cooling rate is reduced to identify the change in the model parameter. However a sufficient factor of safety ($\approx$10 $^{\circ}C$) would be required to account for the under-estimation that occurs in heating. For the second-order model to be implemented, more controlled testing is required in order to properly discern the effects of reduced cooling from the effects of the initial temperature distribution. Additionally, the inclusion of core-losses in the machine heat generation term should be investigated to improve model performance. / Thesis / Master of Applied Science (MASc)