Mobility has become a substantial part in our society. Since we spend a lot of our available time on the road, we expect the automotive environment to provide similar comfort levels than residential buildings. Within this context, this research thesis especially focuses on automotive thermal comfort control. The automotive cabin is a very special environment, which is characterized by extreme inhomogeneity and overall transient behavior. Thermal comfort is a very vague and a very subjective term, which depends on physiological and psychological variables. Theories for thermal comfort in transient environments have not been fully established yet and researchers are still busy with its investigation. At present, automotive industry relies on extensive thermal comfort models, manikins and powerful simulation tools to assess and control thermal comfort. This thesis studies the application of artificial intelligence and proposes a blackbox approach which aims for extracting thermal comfort knowledge directly from human's interaction with the HVAC controls. This methodology avoids the use of human physiological and psychological thermal comfort models and does not require any a-priori knowledge. A novel comfort acquisition tool has been developed and has been integrated into a research vehicle in order to gather the required data for system learning. Data has been collected during spring, autumn and summer conditions in Southern Africa. Methods of data mining have been applied and an intelligent implementation using artificial neural networks has been proposed. The achieved results are promising and allow for about 87 perecent correct classification. It is concluded that methods of artificial intelligence perform well and are far superior compared to conventional approaches. These methods can be used as a powerful tool for the development process of vehicle air-conditioning controls and have great potential for time and cost reduction.

The refrigerant circuits of car air-conditioning systems are fitted with so-called open type compressors, because there is only a lip seal preventing the refrigerant from leaking from the compressor housing to the atmosphere. In addition, the cycle uses damping elements between the compressor and the other components on the suction and pressure lines to reduce vibration and noise transfer from the engine to the car body. Both the lip seal and damping elements result in loss of refrigerant as they are made from elastomers and leak with age, and, under high temperature conditions inside the engine room, these elements also allow a relatively high permeation of the refrigerant gas to the atmosphere. With very high refrigerant losses in the older R12 -cooling cycles and the damage caused by this gas to the ozone layer in the stratosphere, the Montreal protocol phased out this refrigerant and the car industry was forced to revert completely to R134a until 1994/95. R134a has no ozone depletion potential, but it has a direct global warming potential, and, therefore, leakages also have to be minimised. R134a has, because of its molecular size, a high permeation potential and, hence, all the refrigerant hoses are lined internally. Unfortunately, these hoses also leak with age and significant refrigerant loss will occur [1] R134a can therefore only be viewed as a solution until an alternative refrigerant with no direct global warming potential has been developed. Candidates for new refrigerants are natural substances such as hydrocarbons or carbon dioxide [2]. Unfortunately, both substances have disadvantages and their use is restricted to special cases, for e.g. hydrocarbons are flammable and are not used in car air-conditioners, but in Germany it is used as a refrigerant in household refrigerators with hermetic cycles. What makes the implementation of carbon dioxide (CO2) difficult are the high system pressures and the low critical point [3].

The modelling and control method of a vehicle climatronic system, based on MATLAB/SIMULINK, is presented. In order to achieve high modelling accuracy, a developed simulation model library is introduced. The modelling approach is described and the developed models are validated with some of experimental data obtained. The models are nonlinear, independent of fluid type and based on thermo-dynamic principles. Analysis of the cooling circuit modelling and empirical real-time control models are created by using Fuzzy logic controller and Stateflow. Both of control input and output are implemented essentially at original vehicle CAN-Bus system. Feasible digital automatic control strategy basic to fuzzy theory, hardware and software solution are given. The simulation experiment is achieved with the Hardware-in-Loop technology. This control methodology is easily operated and worth applying for any further studies or methods.