Recurrent neural networks for AFR estimation and control in spark ignition automotive engines

Abstract

Since 80s continuous government constraints have pushed car manufacturers towards the study of innovative technologies aimed at reducing automotive exhaust emissions and increasing engine fuel economy. As a result of this stringent legislation, the automotive engine technology has experienced continuous improvements in many areas. In the field of Engine Control Systems (ECS) innovative procedures have been proposed and major efforts have been devoted to the study of transient phenomena related to operation and design of engine control strategies. Particular attention has been given to the control of mixture strength excursions, which is a critical task to assure satisfactory efficiency of three-way catalytic converters and thus to meet exhaust emissions regulations. This goal has to be reached in both steady state and transient conditions by estimating the air flow rate at the injector location and delivering the fuel in the right amount and with the appropriate time dependence. Furthermore, the ECS designers have to face with the On Board Diagnostics (OBD) requirements that were introduced in 1996 in California and later in Europe and represent one of the most challenging targets in the field of Automotive Control. OBD requires a continuous monitoring of all powertrain components in order to prevent those faults that could result in a strong increase of exhaust emissions. © 2008 Springer-Verlag Berlin Heidelberg.