Optimization of vehicle powertrains is usually based on specific drive cycles and is performed on testbeds under reproducible conditions. However, in real-world operation, energy consumption and emissions differ significantly from the values obtained in testbed environments, which also implies breaching legislative thresholds. Therefore, in order to close the gap between testbed and real world, it is necessary to take random effects, like varying road and ambient conditions or various traffic situations, into account during the engine calibration process. In this article a stochastic optimization approach based on risk measures, that quantify the prevalent uncertainties, is presented. Rather than optimizing a deterministic value for one specific scenario described by a drive cycle, the distribution of possible outcomes is shaped in a way that it reflects the risk aversion and preferences of the decision maker. Simulation results show that incorporating randomness in the optimization process yields substantially more robust and reliable results.

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