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EXV to Optimize PHEV/BEV Automotive Air Conditioning System Performance and Simulation Methodology
Technical Paper
2020-01-1393
ISSN: 0148-7191, e-ISSN: 2688-3627
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English
Abstract
Due to increasing standards in fuel consumption, battery electric vehicles (BEV) and plug in electric hybrid vehicles (PHEV), are becoming more commonplace in the automotive industry. Batteries used in such applications require methods of thermal management to promote longer life, higher efficiency and performance. A common method of keeping the battery cool, in high heat conditions, is to use a water to refrigerant chiller. The already existing automotive air conditioning system is leveraged to enable the use of such a chiller. The added thermal transient load of the battery adds complexity to the refrigeration system. Balancing the thermal comfort of the occupants with temperature requirements of battery drives challenges to the overall system capacity. The sudden change in battery cooling loads can noticeably degrade the evaporator heat rejection. In extreme cases the battery cooling load can cause complete loss of refrigerant flow to the evaporator. This is primarily due to the sudden opening of the thermal expansion valve (TXV) in the chiller loop. The use of an electronic expansion valve (EXV) has proven to greatly improve the ability to load balance. The EXV provides the ability to develop sophisticated control strategies to meter the chiller refrigerant flow. Gradually increasing the chiller cooling reduces its sudden impact on the evaporator capacity. This is accomplished by optimizing the EXV opening rate. The simulation capability was developed to model the use of an EXV in a commercially available simulation software. The simulation methodology is shown to agree with empirical data. The simulation model is valuable for future system development and optimization.
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Martins, J. and Govindarajalu, M., "EXV to Optimize PHEV/BEV Automotive Air Conditioning System Performance and Simulation Methodology," SAE Technical Paper 2020-01-1393, 2020, https://doi.org/10.4271/2020-01-1393.Data Sets - Support Documents
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