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A. Wasserburger, N. Didcock, C. Hametner:
"Efficient Real Driving Emissions Calibration of Automotive Powertrains under Operating Uncertainties";
Engineering Optimization, online (2021).

The steady state calibration of automotive powertrains is typically based on the assumption of one specific drive cycle and perfectly controllable operating conditions. During real operation, however, these assumptions are violated which implies that the calibration might in fact not be optimal.
Therefore, in order to achieve reliable performance in a real-world setting, these uncertainties have to be considered already during the calibration process. In this article, a stochastic optimization approach, that takes the mentioned operating uncertainties into account by including probability distributions of the disturbances, is suggested.
Furthermore, an approximation of the distribution of the optimization performance criterion is derived, that greatly reduces the computational load during optimization compared to Monte-Carlo sampling.
Simulation results show that the proposed probabilistic approach leads to lower expected values of emissions and consumption when compared with deterministic optimization approaches ignoring the stochastic influences.

Real driving emissions; engine calibration; stochastic optimization; distribution estimation

http://dx.doi.org/10.1080/0305215X.2021.1989589