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Automotive HVAC Dual Unit System Cool-Down Optimization Using a DFSS Approach
Technical Paper
2019-01-0892
ISSN: 0148-7191, e-ISSN: 2688-3627
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English
Abstract
Automotive AC systems are typically either single unit or dual unit systems, while the dual unit systems have an additional rear evaporator. The refrigerant evaporates inside these heat exchangers by taking heat and condensing the moisture from the recirculated or fresh air that is being pushed into the car cabin by air blowers. This incoming cold air in turn brings the cabin temperature and humidity to a level that is comfortable for the passengers. These HVAC units have their own thermal expansion valve to set the refrigerant flow, but both are connected to the main AC refrigerant loop. The airflows, however, are controlled independently for front and rear unit that can affect the temperature and amount of air coming into the cabin from each location and consequently the overall cabin cool-down performance. The goal of this paper is to find the optimal configuration of an AC system to achieve maximum cool down by investigating the effect of parameters such as distribution of air between the front and rear unit, front and rear evaporator, TXV settings, etc. This study uses a Design for Six Sigma (DFSS) approach to identify the best combination of system components and air distributions that achieve the goal of optimized AC performance. This strategy can guide the engineers to design more efficient HVAC systems that are optimized to meet the requirements of the AC system and passenger comfort. A 1D numerical model, using Siemens’ LMS Imagine. Lab AMESim 15, is developed to predict the AC system performance for these different system configurations. Both the refrigerant side and the air side components including the cabin are modeled and validated against the vehicle test data.
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Citation
Mirzabeygi, P., Khawaja, A., Govindarajalu, M., and Joshi, S., "Automotive HVAC Dual Unit System Cool-Down Optimization Using a DFSS Approach," SAE Technical Paper 2019-01-0892, 2019, https://doi.org/10.4271/2019-01-0892.Data Sets - Support Documents
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References
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