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The large amount of controllable fuel injection parameters of Diesel engine equipped with high pressure common-rail fuel injection system makes the control of combustion more flexible, and also makes the workload of calibration and optimization much heavier. For higher efficiency, model-based approaches are presented and researched. This contribution presents a new method for modeling which is constituted by Neural Network and Adaptive Network-based Fussy Inference System (ANFIS). The experiment is carried out on a 6-cylinder common rail diesel engine. The analysis and experiment show that effective modeling can be achieved using this method.

A mathematical model of vehicle fuel cell system thermal management has been developed to investigate the effects of various design and operating conditions on the thermal management and to understand the underlying mechanism. The fuel cell stack structure is represented by a lumped thermal mass model, which has the heat transfer and pressure loss characteristics of the fuel cell stack structure. The whole thermal management system is discretized into many volumes, where each flowspit is represented by a single volume, and every pipe is divided into one or more volumes. These volumes are connected by boundaries. The model is solved numerically to analyze thermal management system performance. The effects of coolant flow rates and air flow rates on the system thermal performance, the stack thermal capacity on the transient thermal performance have been investigated in detail.