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e-Thermal is a vehicle level thermal analysis tool developed by General Motors to simulate the transient performance of the entire vehicle HVAC and Powertrain cooling system. It is currently in widespread (global) use across GM. This paper discusses the details of the air-conditioning module of e-Thermal. Most of the literature available on transient modeling of the air conditioning systems is based on finite difference approach that require large simulation times. This has been overcome by appropriately modeling the components using Sinda/Fluint. The basic components of automotive air conditioning system, evaporator, condenser, compressor and expansion valve, are parametrically modeled in Sinda/Fluint. For each component, physical characteristics and performance data is collected in form of component data standards. This performance data is used to curve fit parameters that then reproduce the component performance.

In this paper, a model condensation technique is developed to ensure consistent modeling of STL (Sound Transmission Loss) between coarse and detailed SEA model. In the Performance-Based coarse SEA Model, the component level performance (STL and absorption) is assigned to each path, which comes from various ways including detailed analytical SEA model. From the detailed SEA model for the component or even the whole vehicle, the equivalent performance data needs to be condensed and extracted for the coarse model. The condensation theory for equivalent STL is presented in this paper. The extra work needed to apply this technique to detailed SEA model is negligible by using AutoSEA script. An example for condensation of a detailed component model is given at the end. Comparison between the detailed analytical SEA model and the coarse SEA Model is consistent.

In addition to the thermal comfort of the vehicle occupants, their safety by ensuring adequate visibility is an objective of the automotive climate control system. An integrated dew point and glass temperature sensor is widely used among several other technologies to detect risk of fog formation on the cabin side (or inner) surface of the windshield. The erroneous information from a sensor such as the measurement lag can cause imperfect visibility due to the delayed response of the climate control system. Also the high value, low cost vehicles may not have this sensor due to its high cost. A differential equation based model of the cabin air humidity is proposed to calculate in real-time specific humidity of the passenger compartment air. The specific humidity is used along with the windshield surface temperature to determine relative humidity of air and therefore, the risk of fog formation on the interior surface of a windshield.