2011
DOI: 10.1016/j.ymssp.2010.08.016
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Adaptation of a zero-dimensional cylinder pressure model for diesel engines using the crankshaft rotational speed

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Cited by 14 publications
(18 citation statements)
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“…They do not depend on the rotational speed of the crankshaft, which has been proved in many previous papers. This was confirmed by both machine learning model simulations [26], using artificial neural network modeling [27], a zero-dimensional cylinder pressure model [28], the crankshaft rotation measurement experimental method [29], using the crankshaft dynamics research method [30], a comparison of frictional losses research [31], and both by studying the influence of initial starting conditions on the above factors [32].…”
mentioning
confidence: 81%
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“…They do not depend on the rotational speed of the crankshaft, which has been proved in many previous papers. This was confirmed by both machine learning model simulations [26], using artificial neural network modeling [27], a zero-dimensional cylinder pressure model [28], the crankshaft rotation measurement experimental method [29], using the crankshaft dynamics research method [30], a comparison of frictional losses research [31], and both by studying the influence of initial starting conditions on the above factors [32].…”
mentioning
confidence: 81%
“…The system works as follows: an electromagnet (31), whose operation is controlled by the electronic control unit of the nozzles (11), is added to the fuel supply system of the engine with a mechanical cable throttle actuator. The core of the electromagnet is connected to the tension roller (27) through which the control cable passes to the throttle (28).…”
Section: Working Out the Fuel Supply Systemmentioning
confidence: 99%
“…This results in vehicle-to-vehicle variations in the measurements that should be compensated for to achieve robustness in the misfire detection algorithm. Both in (Weißenborn et al, 2011) and Paper 6, the estimated flywheel tooth angle errors were in an interval of around ±0.06…”
Section: Misfire Detection Based On Crankshaft Angular Velocity Measumentioning
confidence: 92%
“…Detailed models of elastic engine shafting for both speed and gas pressure torque prediction are widely used [8,[26][27][28], but issues related to insufficiently accurate prediction was reported as well, and addressed to some level of imbalance in friction modelling and neglecting auxiliaries powertrain [8,26]. The wealth of literature, however, references simplified Single Degree of Freedom (1-DoF) [9][10][11][12][13][14][29][30][31] or 2-DoF rigid body dynamic models [32]. In this work, considering model application to the steady-state operation, simplified 1-DoF dynamic engine model is applied along with detailed angle resolved friction model.…”
Section: Engine Dynamic and Friction Modelmentioning
confidence: 99%
“…Engine speed disturbances due to incremental disc geometric faults were corrected using improved crankshaft energy concept [11][12][13]. Similar approach related to combustion in CI engine was also presented by Weißenborn et al [14].…”
Section: Introductionmentioning
confidence: 99%