| description abstract | One of the most effective factors influencing performance, efficiency, and pollutant emissions of internal combustion engines is the combustion phasing: in gasoline engines electronic control units (ECUs) manage the spark advance (SA) in order to set the optimal combustion phase. Combustion control is assuming a crucial role in reducing engine tailpipe emissions and maximizing performance. The number of actuations influencing the combustion is increasing, and as a consequence, the calibration of control parameters is becoming challenging. One of the most effective factors influencing performance and efficiency is the combustion phasing: for gasoline engines, control variables such as SA, airtofuel ratio (AFR), variable valve timing (VVT), and exhaust gas recirculation (EGR) are mostly used to set the combustion phasing. The optimal control setting can be chosen according to a target function (cost or merit function), taking into account performance indicators, such as indicated mean effective pressure (IMEP), brakespecific fuel consumption (BSFC), pollutant emissions, or other indexes inherent to reliability issues, such as exhaust gas temperature or knock intensity. Many different approaches can be used to reach the best calibration settings: design of experiment (DOE) is a common option when many parameters influence the results, but other methodologies are in use: some of them are based on the knowledge of the controlled system behavior by means of models that are identified during the calibration process. The paper proposes the use of a different concept, based on the extremum seeking approach. The main idea consists in changing the values of each control parameter at the same time, identifying its effect on the monitored target function, and allowing to shift automatically the control setting towards the optimum solution throughout the calibration procedure. An original technique for the recognition of control parameters variations effect on the target function is introduced, based on spectral analysis. The methodology has been applied to data referring to different engines and operating conditions, using IMEP, exhaust temperature, and knock intensity for the definition of the target function and using SA and AFR as control variables. The approach proved to be efficient in reaching the optimum control setting, showing that the optimal setting can be achieved rapidly and consistently. | |
