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International audience ; Nowadays, automotive manufacturers are submitted to strong constraints in engine calibration: lowest fuel consumption, emission-control legislation and driver requests for driving comfort and performances. These constraints lead to an increasingly complexity of the engines and thus an increasingly number of parameters to be tuned, making the empirical engine calibration by a scan of parameter values impossible at engine test-bench. New methodologies in automated engine calibration based on statistics and optimization have emerged in order to limit the number of experimental tests to be run. The optimization problem of engine calibration consists in the determination of engine tuning parameter maps that minimize the cumulated fuel consumption and pollutant emissions, under combustion noise constraints, on a driving cycle. The usual way to get this result is to select specific operating points representing this cycle in the engine working range and to define upper bounds applied on the different engine responses (allocations) for each of them, in order to obtain a weighted sum of these local responses respecting the global targets. The underlying problem is a multi-objective optimization problem: different compromises between fuel consumption, noise and pollutant emissions on each operating point are possible. We propose an adapted optimization method based on the MO-CMA-ES method (Multi-objective Covariance Adaptation Evolution Strategy) which takes into account the non trivial limits of the engine parameter variation domains and some robustness constraints. An other point addressed in this paper is the map optimization which consists in a global optimization of engine responses cumulated on the driving cycle. This method avoids the cumbersome choice of allocations for each considered operating point and includes directly the map regularity constraints in map parameterizations. Finally, application on real dataset obtained at automated test-bench for a diesel engine are presented .

International audience ; Nowadays, automotive manufacturers are submitted to strong constraints in engine calibration: lowest fuel consumption, emission-control legislation and driver requests for driving comfort and performances. These constraints lead to an increasingly complexity of the engines and thus an increasingly number of parameters to be tuned, making the empirical engine calibration by a scan of parameter values impossible at engine test-bench. New methodologies in automated engine calibration based on statistics and optimization have emerged in order to limit the number of experimental tests to be run. The optimization problem of engine calibration consists in the determination of engine tuning parameter maps that minimize the cumulated fuel consumption and pollutant emissions, under combustion noise constraints, on a driving cycle. The usual way to get this result is to select specific operating points representing this cycle in the engine working range and to define upper bounds applied on the different engine responses (allocations) for each of them, in order to obtain a weighted sum of these local responses respecting the global targets. The underlying problem is a multi-objective optimization problem: different compromises between fuel consumption, noise and pollutant emissions on each operating point are possible. We propose an adapted optimization method based on the MO-CMA-ES method (Multi-objective Covariance Adaptation Evolution Strategy) which takes into account the non trivial limits of the engine parameter variation domains and some robustness constraints. An other point addressed in this paper is the map optimization which consists in a global optimization of engine responses cumulated on the driving cycle. This method avoids the cumbersome choice of allocations for each considered operating point and includes directly the map regularity constraints in map parameterizations. Finally, application on real dataset obtained at automated test-bench for a diesel engine are presented .