The manuscript was received on 12 February 2010 and was accepted after revision for publication on 4 January 2011. DOI: 10.1177/0954407011398177Abstract: A mean-value model of a diesel engine with a variable-geometry turbocharger (VGT) and exhaust gas recirculation (EGR) is developed, parameterized, and validated. The intended model applications are system analysis, simulation, and development of modelbased control systems. The goal is to construct a model that describes the gas flow dynamics including the dynamics in the manifold pressures, turbocharger, EGR, and actuators with few states in order to obtain short simulation times. An investigation of model complexity and descriptive capabilities is performed, resulting in a model that has only eight states. A Simulink implementation including a complete set of parameters of the model are available for download. To tune and validate the model, stationary and dynamic measurements have been performed in an engine laboratory. All the model parameters are estimated automatically using weighted least-squares optimization and it is shown that it is important to tune both the submodels and the complete model and not only the submodels or not only the complete model. In static and dynamic validations of the entire model, it is shown that the mean relative errors are 5.8 per cent or lower for all measured variables. The validations also show that the proposed model captures the system properties that are important for control design, i.e. a non-minimum phase behaviour in the channel EGR valve to the intake manifold pressure and a non-minimum phase behaviour, an overshoot, and a sign reversal in the VGT to the compressor mass flow channel, as well as couplings between channels.
Abstract-Just like it has irrevocably reshaped social life, the fast growth of smartphone ownership is now beginning to revolutionize the driving experience and change how we think about automotive insurance, vehicle safety systems, and traffic research. This paper summarizes the first ten years of research in smartphone-based vehicle telematics, with a focus on userfriendly implementations and the challenges that arise due to the mobility of the smartphone. Notable academic and industrial projects are reviewed, and system aspects related to sensors, energy consumption, cloud computing, vehicular ad hoc networks, and human-machine interfaces are examined. Moreover, we highlight the differences between traditional and smartphonebased automotive navigation, and survey the state-of-the-art in smartphone-based transportation mode classification, driver classification, and road condition monitoring. Future advances are expected to be driven by improvements in sensor technology, evidence of the societal benefits of current implementations, and the establishment of industry standards for sensor fusion and driver assessment.
Abstract-A control structure is proposed and investigated for coordinated control of exhaust gas recirculation (EGR) valve and variable geometry turbochargers (VGT) position in heavy duty diesel engines. Main control goals are to fulfill the legislated emission levels, to reduce the fuel consumption, and to fulfill safe operation of the turbocharger. These goals are achieved through regulation of normalized oxygen/fuel ratio, , and intake manifold EGR-fraction. These are chosen both as main performance variables and feedback variables since they contain information about when it is possible to decrease the fuel consumption by minimizing the pumping work. Based on this a novel and simple pumping work minimization strategy is developed. The proposed performance variables are also strongly coupled to the emissions which makes it easier to adjust set-points, e.g., depending on measured emissions during an emission calibration process, since it is more straightforward than control of manifold pressure and air mass flow. Further, internally the controller is structured to handle the different control objectives. Controller tuning is important for performance but can be time consuming so the controller objectives are captured in a cost function, which makes automatic tuning possible even though objectives are conflicting. Performance tradeoffs are necessary and are illustrated on the European Transient Cycle. The controller is validated in an engine test cell, where it is experimentally demonstrated that the controller achieves all the control objectives and that the current production controller has at least 26% higher pumping losses compared to the proposed controller.Index Terms-Diesel engine modeling, exhaust gas recirculation (EGR)-fraction, engine control, oxygen/fuel ratio, proportional-integral-derivative (PID).
Network science plays a central role in understanding and modeling complex systems in many areas including physics, sociology, biology, computer science, economics, politics, and neuroscience. One of the most important features of networks is community structure, i.e., clustering of nodes that are locally densely interconnected. Communities reveal the hierarchical organization of nodes, and detecting communities is of great importance in the study of complex systems. Most existing community-detection methods consider low-order connection patterns at the level of individual links. But high-order connection patterns, at the level of small subnetworks, are generally not considered. In this paper, we develop a novel community-detection method based on cliques, i.e., local complete subnetworks. The proposed method overcomes the deficiencies of previous similar community-detection methods by considering the mathematical properties of cliques. We apply the proposed method to computer-generated graphs and real-world network datasets. When applied to networks with known community structure, the proposed method detects the structure with high fidelity and sensitivity. When applied to networks with no a priori information regarding community structure, the proposed method yields insightful results revealing the organization of these complex networks. We also show that the proposed method is guaranteed to detect near-optimal clusters in the bipartition case.
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