Development of a model-based auto-calibration methodology with genetic algorithms focusing on fuel efficiency and emission compliance over real-world driving cycles

Abstract

The aim of this study is to develop a methodology for the optimization of a diesel engine calibration in terms of fuel consumption while being emission compliant for a predefined route by using model-based calibration techniques with genetic algo rithms. A 2 liter diesel engine of light commercial vehicle data was used to develop the proposed methodology. The model-based calibration environment was structured in Simulink with a combination of an engine control unit (ECU), internal combus tion engine (ICE) and engine aftertreatment system (EAS) models. Accuracies of the models were examined and considered as adequate for this methodology development study. After the models were created, the calibration domain for a predefined route was computed in Matlab by weighting the engine operating points based on frequency and fuel consumption. These points in the ECU model were defined as the optimiza tion domain for the genetic algorithm in Matlab. The cost function of the optimization consists of fuel consumption, performance parameters, mechanical limits of ICE, and tailpipe emission limits defined by regulations. After iterating with different genetic algorithm configurations, the fuel consumption over the predefined RDE route was decreased by 3.07% at the end of simulations performed in Simulink. The smooth calibration maps were obtained while not violating any of the limits defined in the cost function. In addition to model accuracy improvements, for further study, the models can also be embedded into an ECU and optimization can be performed automatically according to the defined route, weather and driver information.