1,720,978 research outputs found
Model-order reduction for wave propagation dynamics in internal combustion engine air path systems
No full textModeling and Simulation of Residential Power Demand Including Transportation
No full textPersonal transportation has a significant impact on the residential electric energy usage due to the interaction of alternative fueled vehicles with the electric grid. This phenomenon is projected to grow significantly, as several studies confirm that the market penetration of alternative fueled vehicles will steadily increase in the future.
This paper presents a control-oriented model that predicts the daily residential power demand considering multiple energy carriers and different types of alternative fueled vehicles for personal transportation. The model has been used to perform an energy analysis on a large sample of homes with the objective of evaluating the impact of personal transportation on the residential electric power demand. Two penetration levels are considered in the study and the results are evaluated based on several metrics
Model-Based Wheel Torque and Backlash Estimation for Drivability Control
No full textTo improve torque management algorithms for drivability, the powertrain controller must be able to compensate for the nonlinear dynamics of the driveline. In particular, the presence of backlash in the transmission and drive shafts excites sharp torque fluctuations during tip-in or tip-out transients, leading to a deterioration of the vehicle drivability and NVH.
This paper proposes a model-based estimator that predicts the wheel torque in an automotive drivetrain, accounting for the effects of backlash and drive shaft flexibility. The starting point of this work is a control-oriented model of the transmission and vehicle drivetrain dynamics that predicts the wheel torque during tip-in and tip-out transients at fixed gear. The estimator is based upon a switching structure that combines a Kalman Filter and an open-loop prediction based on the developed model. The estimator relies only on measurements and signals that are commonly available in production vehicles, and in this paper is implemented in real-time using a rapid prototyping ECU. The estimator is verified experimentally on a test vehicle on a chassis dynamometer
Advanced Range Estimation for Electric Busses with Physics Informed Machine Learning
Full text linkGiven the growing focus on environmentally sustainable practices and the desire for cost effective solutions, electric buses have caught the eye of many public transportation companies. To make electric buses an ideal addition to a fleet, they must complete required routes in all conditions, making accurate range finding of these buses an invaluable tool. A current approach for range estimation is to develop energy-based models of components and integrate them in a larger model that predicts the overall battery power draw, estimating the remaining range available. Such an analytical model is limited by the variety of extraneous variables affecting the system (traffic, temperature, passenger count), individual components which are difficult to model accurately, as well as finite access to required data and parameters for calibration and verification. In this context, the proposed research aims to improve the state of the art of range estimation for electric vehicles by combining data driven machine learning techniques with physics-based analysis (PBA). This combined model is applied to a case study of the regenerative braking in electric buses. First, a feed forward neural network model was trained to estimate regenerative braking based on available experimental data, then this network was integrated into a physics-based bus model. This implementation was then used to assess the capabilities of the combined model to account for various lapses in data quality, and how the overall accuracy can be improved from using a strictly analytical model. The combined model resulted in a clear improvement of the regenerative braking modeling, and therefore an improvement in the analytical modeling of the electric bus.No embargoAcademic Major: Mechanical Engineerin
ENABLING EFFICIENT FLEET COMPOSITION SELECTION THROUGH THE DEVELOPMENT OF A RANK HEURISTIC FOR A BRANCH AND BOUND METHOD
Full text linkIn the foreseeable future, autonomous mobile robots (AMRs) will become a key enabler
for increasing productivity and flexibility in material handling in warehousing facilities,
distribution centers and manufacturing systems.
The objective of this research is to develop and validate parametric models of AMRs,
develop ranking heuristic using a physics-based algorithm within the framework of the
Branch and Bound method, integrate the ranking algorithm into a Fleet Composition
Optimization (FCO) tool, and finally conduct simulations under various scenarios to
verify the suitability and robustness of the developed tool in a factory equipped with
AMRs. Kinematic-based equations are used for computing both energy and time
consumption. Multivariate linear regression, a data-driven method, is used for designing
the ranking heuristic. The results indicate that the unique physical structures and
parameters of each robot are the main factors contributing to differences in energy and
time consumption. improvement on reducing computation time was achieved by
comparing heuristic-based search and non-heuristic-based search. This research is
expected to significantly improve the current nested fleet composition optimization tool
by reducing computation time without sacrificing optimality. From a practical
perspective, greater efficiency in reducing energy and time costs can be achieved.Ford Motor CompanyNo embargoAcademic Major: Aerospace Engineerin
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Artificial Weather Forecasts with Realistic Uncertainty for Performance Evaluation of Home Energy Management Systems
Full text linkHome energy management systems (HEMS) and other demand side management strategies are necessary for the establishment of stable and sustainable energy grid. HEMS schedule the timing of deferrable loads within homes by minimizing energy costs according to a time variable price for electricity while maintaining occupant comfort. Scheduling these loads establishes a challenging optimization problem, in which the controller must handle significant operational uncertainty, including error in weather forecasts and load requests from residents. To properly evaluate HEMS performance, it is necessary to do so using conditions that accurately mimic realworld forecast uncertainty. However, there is a lack of open access historical weather forecast data, leading researchers to rely upon synthetic weather forecasts.
This thesis aims to improve the accuracy of HEMS performance evaluation by generating synthetic weather forecasts with uncertainty that is more accurate to real-world conditions. Live
weather forecast uncertainty was analyzed by recording data from an AccuWeather API and comparing to weather observations from the National Oceanic and Atmospheric Administration. A long-short term memory model was developed to generate synthetic weather forecasts with realistic uncertainty. This analysis showed that real-world weather forecast uncertainty is dependent upon time of day, location, and the lead time between a weather forecast and its prediction. While the model developed to generate synthetic weather forecast does not accurately match real-world uncertainty, it establishes a methodology which can be improved upon and implemented in future HEMS evaluation.No embargoAcademic Major: Mechanical Engineerin
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
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