The optimization method proposed in the present study consists of a multi-objective genetic algorithm combined with an experimental investigation carried out on a test bench, by using a DI Diesel engine. The genetic algorithm selects the injection parameters for each operating condition whereas the output measured by the experimental apparatus determines the fitness in the optimization process. The genetic algorithm creates a random population, which evolves combining the genetic code of the most capable individuals of the previous generation. Each individual of the population is represented by a set of parameters codified with a binary string. The evolution is performed using the operators of crossover, mutation and elitist reproduction. This genetic algorithm allows competitive fitness functions to be optimized with a single optimization process. For the determination of the overall fitness function the concept of Pareto optimality has been implemented. In this work, the input variables used for the optimization method are injection parameters like start of pilot and main injection, injection pressure and duration. The engine used is a FIAT 1929 cc DI diesel engine, in which the traditional injection system has been replaced by a common rail high pressure injection system. The competitive fitness functions were determined based on the measured values of fuel consumption, emissions levels (i.e. NOx, soot, CO, CO2, HC); combustion noise and overall engine noise, for each operating conditions. The optimization was performed for different engine speed and torque conditions typical of the EC driving cycles.
A Combined Optimization Method for Common Rail Diesel Engines
CARLUCCI, Antonio Paolo;DE RISI, Arturo;DONATEO, Teresa;FICARELLA, Antonio
2002-01-01
Abstract
The optimization method proposed in the present study consists of a multi-objective genetic algorithm combined with an experimental investigation carried out on a test bench, by using a DI Diesel engine. The genetic algorithm selects the injection parameters for each operating condition whereas the output measured by the experimental apparatus determines the fitness in the optimization process. The genetic algorithm creates a random population, which evolves combining the genetic code of the most capable individuals of the previous generation. Each individual of the population is represented by a set of parameters codified with a binary string. The evolution is performed using the operators of crossover, mutation and elitist reproduction. This genetic algorithm allows competitive fitness functions to be optimized with a single optimization process. For the determination of the overall fitness function the concept of Pareto optimality has been implemented. In this work, the input variables used for the optimization method are injection parameters like start of pilot and main injection, injection pressure and duration. The engine used is a FIAT 1929 cc DI diesel engine, in which the traditional injection system has been replaced by a common rail high pressure injection system. The competitive fitness functions were determined based on the measured values of fuel consumption, emissions levels (i.e. NOx, soot, CO, CO2, HC); combustion noise and overall engine noise, for each operating conditions. The optimization was performed for different engine speed and torque conditions typical of the EC driving cycles.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
