Derivative free optimization method and physical simulations coupled with statistical models for transient engine calibration

Abstract : Nowadays, automotive manufacturers have to deal with strong constraints: lowest fuel consumption, emission-control legislation and driver requests for driveability and performance. For this purpose, the classical engine has evolved towards a very complex system combining many hi-tech components with advanced control strategies. It leads to an increasingly complexity of the underlying optimization issue. The classical method to perform this optimization is to use statistical response surface models obtained from optimally designed experiments at test bench in steady-state conditions. In this approach, the transient effects observed during the driving cycle are not taken into account. A promising idea to include these transient effects in the optimization process is to couple physical modelling of the engine (to get the conditions in the combustion chamber before each combustion event) with statistical models (to obtain engine-out emissions after this combustion event). Powertrain system simulation is shown to provide a helpful tool to reach this objective thanks to a good compromise between accuracy and low CPU time. A dedicated parameterization of the engine maps and a Derivative Free Optimization method is then used to minimize the cumulative fuel consumption and pollutant emissions, under combustion noise constraints, on a driving cycle. An application on a real dataset obtained at automated test-bench for a diesel engine is presented.
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Gregory Font, Delphine Sinoquet, Hoël Langouët, Michel Castagne, Sébastien Magand. Derivative free optimization method and physical simulations coupled with statistical models for transient engine calibration. Conference design of experiments (DoE) in engine development, May 2011, Berlin, Germany. ⟨hal-02284372⟩

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