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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.

Recent engine systems have become complex due to the requirements of fuel efficiency, exhaust gas emission control and good drivability. To decrease engine development period, model-based development has been adopted [1]. For torque-based vehicle control, engine torque estimation models are necessary. Simple mean-value torque models are available but these models require large amount of test data for development and validation. In addition, they cannot estimate transient torque precisely. On the other hand, complex physical models require considerable time for modeling and simulation. In order to decrease modeling time and retain model accuracy, the Wiebe function is utilized to calculate the heat release rate.

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.

In real-world automotive control, there are many constraints to be considered. In order to explicitly treat the constraints, we introduce a model-prediction-based algorithm called a reference governor (RG). The RG generates modified references so that predicted future variables in a closed-loop system satisfy their constraints. One merit of introducing the RG is that effort required in control development and calibration would be reduced. In the preceding research work by Nakada et al., only a single reference case was considered. However, it is difficult to extend the previous work to more complicated systems with multiple references such as the air path control of a diesel engine due to interference between the boosting and exhaust gas recirculation (EGR) systems. Moreover, in the air path control, multiple constraints need to be considered to ensure hardware limits. Hence, it is quite beneficial to cultivate RG methodologies to deal with multiple references and constraints.