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High fuel efficiency and low emission technologies, such as Direct Injection (DI) gasoline and diesel engines and hybrid powertrains, have been developed to resolve environmental and energy resource issues. The hybrid powertrain system has achieved superior power performance as well as higher system efficiency and is expected to be a core powertrain technology because it is compatible with various power sources including fuel cells. It becomes important to control complicated hybrid systems that consist of not only a powertrain but also vehicle systems such as regenerative braking. Model-based control and calibration enables both control strategy optimization and control system development efficiency improvement.

The second-generation air-fuel ratio control method has been developed to reduce exhaust gas emissions in accordance with the improvements in catalysts. The control system consists of a feedforward control using a fuel behavior model, a feedback control using an universal exhaust gas oxygen (UEGO) sensor and a feedback control utilizing the heated exhaust gas oxygen (HEGO) sensor. This significantly improves air-fuel ratio tracking performance by feedforward control derived from the models that express the dynamic phenomena and the disturbance attenuation by UEGO feedback controller which compensates for the long dead-time characteristics by the state predictive control. The tracking performance and the disturbance attenuation can be achieved independently by a two-degree-of-freedom structure presented in this paper. The exhaust air-fuel ratio downstream of the catalyst precisely converges to stoichiometry, which maximizes the conversion efficiency of the catalyst.

This paper considers an application of reference governor (RG) to automotive diesel aftertreatment temperature control. Recently, regulations on vehicle emissions have become more stringent, and engine hardware and software are expected to be more complicated. It is getting more difficult to guarantee constraints in control systems as well as good control performance. Among model-based control methods that can directly treat constraints, this paper focuses on the RG, which has recently attracted a lot of attention as one method of model prediction-based control. In the RG, references in tracking control are modified based on future prediction so that the predicted outputs in a closed-loop system satisfy the constraints. This paper proposes an online RG algorithm, taking account of the real-time implementation on engine embedded controllers.