抄録
In the last two decades, piston engine specifications have deeply evolved. Indeed, new challenges nowadays concern the reduction of pollutant (EURO, Tier, Japanese regulations) and CO2 emissions. To satisfy these new requirements, powertrains have become very complex systems including a large number of high technology components (high pressure injectors, turbocharger, Exhaust Gas Recirculation (EGR) loop, after-treatment devices...). A global CO2 emission decrease can also be achieved with an increasing use of oxygenated fuels. These two main ways of powertrain development require more complex control strategies. Few years ago, engine control strategies were mainly defined by experiments on engine test benches. This approach is not adapted to the complexity of future engines: on the one hand, tests are very expensive and on the other hand, they do not give many information to understand interactions between components. Today, a promising alternative to tests is the use of 0D/1D simulation tools. These methods have been widely used in the past ten years and allow to build engine control algorithms. However, they are generally based on empirical models and often suffer from a lack of predictivity. A solution for extending the range of application of the system simulation consists in developing more physical models based on the 3D calculations experience. This way has been recently followed at IFP Energies nouvelles, leading to the development and implementation of combustion and pollutant emissions models based on detailed tabulated chemistry methods, for both compression-ignition (CI) and spark-ignition (SI) engines. In this paper, a detailed description of an innovative tabulated chemistry method to predict CO and NOx emissions is proposed. Applications of this approach for both CI and SI combustions are presented and some validations are discussed, pointing out the combustion heat releases and cylinder pressures, but also the evolution of the pollutant emissions in front of load variations, in comparison with experimental data.
