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There is a growing need for life-cycle data – so-called collectives – when developing components like elastomer engine mounts. Current standardized extreme load cases are not sufficient for establishing such collectives. Supplementing the use of endurance testing data, a prediction methodology for component temperature collectives utilizing existing 3D CFD simulation models is presented. The method uses support points to approximate the full collective. Each support point is defined by a component temperature and a position on the time axis of the collective. Since it is the only currently available source for component temperature data, endurance testing data is used to develop the new method. The component temperature range in this data set is divided in temperature bands. Groups of driving states are determined which are each representative of an individual band. Each of the resulting four driving state spaces is condensed into a substitute load case.

Collective life-cycle data is needed when developing components like elastomer suspension mounts. Life-time prediction is only possible using thermal load frequency distributions. In addition to current extreme load cases, the Idle Load Case is examined at Mercedes-Benz Car Group as a collective load case for Vehicle Thermal Management (VTM) numerical simulations in early development stages. It combines validation opportunities for HVAC, cooling and transmission requirements in hot-country-type ambient conditions. Experiments in climatic wind tunnels and coupled 3D CFD and heat transfer simulations of the Idle Load Case have been performed. Measurements show steady conditions at the end of the load case. Decoupling of the torque converter, changes in ambient temperature and the technical implementation of a wind barrier for still air conditions exhibit influence on component-level results. Solar load, however, does not significantly change the examined component temperatures.

The reliability of electronic components is of increasing importance for further progress towards automated driving. Thermal aging processes such as electromigration is one factor that can negatively affect the reliability of electronics. The resulting failures depend on the thermal load of the components within the vehicle lifetime - called temperature collective - which is described by the temperature frequency distribution of the components. At present, endurance testing data are used to examine the temperature collective for electronic components in the late development stage. The use of numerical simulation tools within Vehicle Thermal Management (VTM) enables lifetime thermal prediction in the early development stage, but also represents challenges for the current VTM processes [1, 2]. Due to the changing focus from the underhood to numerous electronic compartments in vehicles, the number of simulation models has steadily increased.