Steadily rising energy prices and increasingly strict emissions legislation enforce the development of measures that increase efficiency of modern vehicles. An important contribution towards more efficient vehicles is the introduction of measures regarding auxiliary units. These measures increase the gross efficiency of a vehicle and therefore also the vehicle's range.
Among the auxiliary power units of a vehicle like a long-haul truck, the refrigerant compressor generally consumes the biggest amount of energy. Therefore, it is reasonable to focus efficiency-increasing efforts on optimizing the A/C system.
An important tool used in the development of optimization approaches is the simulation of the relevant systems. This allows a cost-optimized evaluation of the optimization approaches and also lets the engineer compare multiple variations of these approaches within a short period of time. For a significant evaluation of the potentials to be expected by implementation of different measures and variations optimizing the A/C system, it is necessary to simulate these under several climatic conditions. To prevent the development of non-functional optimization approaches, it is also necessary to simulate the climatic conditions inside the cabin, as well as parameters regarding safety like fogging of panes or the CO2 concentration inside the cabin.
The presented simulation model contains models of every system necessary to evaluate the impact on energy consumption, comfort and safety aspects of measures increasing the A/C system's efficiency. All these models are adaptable to the specific use case and it is even possible to replace specific signals by measured data to reconstruct test drives. As an example for a possible system to be simulated the A/C system and relevant components of a long-haul truck were implemented.
This simulation tool differs from otherwise published tools by offering a holistic set of models that allow a high level of detail and still retaining extraordinarily brief computational times. This is achieved by the implementation of a new methodic approach for the simulation model, which will be presented in detail within this paper. The simulation tool has been validated using measured data. The application of this simulation tool is being demonstrated using an optimization approach as an example.