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High hydrocarbon emissions during the cold start period is a wellrecognized challenge that has increasing importance in moving towards green vehicles. A model-based controller is a helpful way to reduce cold start hydrocarbon emissions. In our previous work, a model-based controller for a Spark-Ignition engine and an after-treatment system have been developed by using the balanced truncation technique. In this work, the objective was to determine whether balanced truncation surpasses the other model reduction methods. Guyan reduction was selected to compare with balanced truncation for reducing both a linear structural model and the linearized cold start model. An interpretation of the inner domain in Guyan reduction was determined for the linearized cold start model.

The performance of various types of control systems for an electric governor of a diesel engine was examined. The amount of fuel injection of a diesel engine is usually controlled by an electric governor system in these decades, and a PID controller is installed for the electric governor. Even when the optimal parameters for PID controller are well tuned, it is difficult to keep constant rotation speed of the engine, because the applied load to generators may vary according to its running conditions. In this study, a neural network was applied to regulate the parameters in the PID controller for the axial-moving DC motor to control the amount of fuel injection. Experimental studies show that the parameter regulation system using neural network presented here showed good performance under various running conditions. Furthermore, it was shown that various types of training algorithms were applied to neural network control systems and their performance was compared.