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This paper describes an in-cylinder lean-rich combustion (no-post-injection for rich) switching control approach for modern diesel engines equipped with exhaust-treatment systems. No-post-injection rich combustion is desirable for regeneration of engine exhaust-treatment systems thanks to its less fuel penalty compared with regeneration approaches using post-injections and / or in-exhaust injections. However, for vehicle applications, it is desirable to have driver-transparent exhaust-treatment system regenerations, which challenge the in-cylinder rich-lean combustion transitions. In this paper, a nonlinear in-cylinder condition control system combined with in-cylinder condition guided fueling control functions were developed to achieve smooth in-cylinder lean-rich switching control at both steady-state and transient operation. The performance of the control system is evaluated on a modern light-duty diesel engine (G9T600).

A new diesel engine system adopting alternative combustion with rich and near rich combustion, and an airflow-dominant control system for precise combustion control was used with a 4-way catalyst system with LNT (lean NOx trap) to achieve Tier II Bin 5 on a 2.2L TDI diesel engine. The study included catalyst temperature control, NOx regeneration, desulfation, and PM oxidation with and without post injection. Using a mass-produced lean burn gasoline LNT with 60,000 mile equivalent aging, compliance to Tier II Bin 5 emissions was confirmed for the US06 and FTP75 test cycles with low NVH, minor fuel penalty and smooth transient operation.

This paper describes a hybrid robust nonlinear control approach for modern diesel engines running low temperature combustion and conventional diesel combustion modes. Using alternative combustion modes has become a promising approach to reduce engine emissions. However, due to very different in-cylinder conditions and fueling parameters for different combustion modes, control of engines operating multiple combustion modes is very challenging. It becomes difficult for conventional calibration / mapping based approaches to produce satisfactory results in terms of engine torque responses and emissions. Advanced control techniques are then demanded to accomplish the tasks. An innovative hybrid control system is designed to track different key engine operating variables at different combustion modes as well as avoid singularity which is inherent for turbocharged diesel engines running multiple combustion modes.

Alternative combustion modes such as homogeneous charge compression ignition (HCCI) and low temperature combustion (LTC) have shown very promising engine-out emissions. However, these combustion modes are close to the combustion stability boundaries and very sensitive to in-cylinder condition variations. Thus, transient control of engines operating in alternative combustion modes are very challenging compared with control of engines with conventional combustion mode only. This paper presents some advanced air-path control and estimation techniques/practice for multiple and alternative combustion mode engine transient operation. Based on the mean-value engine dynamic models, robust controllers can be designed to track the desired air-path performance variables to ensure desirable combustion during transient operation.

This paper explores the possibility of on-board fuel classification for fuel property adaptive compression-ignition engine control system. The fuel classifier is designed to on-board classify the fuel that a diesel engine is running, including alternative and renewable fuels such as bio-diesel. Based on this classification, the key fuel properties are provided to the engine control system for optimal control of in-cylinder combustion and exhaust treatment system management with respect to the fuel. The fuel classifier employs engine input-output response characteristics measured from standard engine sensors to classify the fuel. For proof-of-concept purposes, engine input-output responses were measured for three different fuels at three different engine operating conditions. Two neural-network-based fuel classifiers were developed for different classification scenarios. Of the three engine operating conditions tested, two conditions were selected for the fuel classifier to be active.