2,696 research outputs found
Cost functions for degradation control of electric motors in electric vehicles
This paper introduces a novel set of electric motor degradation cost functions based on energy usage, energy loss and work output, against their continuous operation rated values recommended by the manufacturer. Unlike conventional electric motor degradation indicators such as the bearing life and insulation life based service factors, these cost functions account for the quantified time in the degradation process. The cost functions are evaluated throughout the operational life of the motor using real-time measurements. Hence, they give a very accurate indication, which may be adapted for online controller tuning. This solid establishment of a degradation cost function also enables the system designer to give the user a choice between performance and degradation minimization. The proposed cost function scheme has experimentally been verified using a hardware-in-the-loop electric powertrain test-rig where standard drive cycles are used to conduct the experiments. The experimental results reveal that the degradation cost functions Cumulative Input Energy Ratio (CIER), Cumulative Loss Ratio (CLR) and Cumulative Work Ratio (CWR) accurately represent the electric motor degradation both qualitatively and quantitatively
PhotoRaptor - Photometric Research Application To Redshifts
Due to the necessity to evaluate photo-z for a variety of huge sky survey
data sets, it seemed important to provide the astronomical community with an
instrument able to fill this gap. Besides the problem of moving massive data
sets over the network, another critical point is that a great part of
astronomical data is stored in private archives that are not fully accessible
on line. So, in order to evaluate photo-z it is needed a desktop application
that can be downloaded and used by everyone locally, i.e. on his own personal
computer or more in general within the local intranet hosted by a data center.
The name chosen for the application is PhotoRApToR, i.e. Photometric Research
Application To Redshift (Cavuoti et al. 2015, 2014; Brescia 2014b). It embeds a
machine learning algorithm and special tools dedicated to preand
post-processing data. The ML model is the MLPQNA (Multi Layer Perceptron
trained by the Quasi Newton Algorithm), which has been revealed particularly
powerful for the photo-z calculation on the base of a spectroscopic sample
(Cavuoti et al. 2012; Brescia et al. 2013, 2014a; Biviano et al. 2013).
The PhotoRApToR program package is available, for different platforms, at the
official website (http://dame.dsf.unina.it/dame_photoz.html#photoraptor).Comment: User Manual of the PhotoRaptor tool, 54 pages. arXiv admin note:
substantial text overlap with arXiv:1501.0650
Photometric redshift estimation based on data mining with PhotoRApToR
Photometric redshifts (photo-z) are crucial to the scientific exploitation of
modern panchromatic digital surveys. In this paper we present PhotoRApToR
(Photometric Research Application To Redshift): a Java/C++ based desktop
application capable to solve non-linear regression and multi-variate
classification problems, in particular specialized for photo-z estimation. It
embeds a machine learning algorithm, namely a multilayer neural network trained
by the Quasi Newton learning rule, and special tools dedicated to pre- and
postprocessing data. PhotoRApToR has been successfully tested on several
scientific cases. The application is available for free download from the DAME
Program web site.Comment: To appear on Experimental Astronomy, Springer, 20 pages, 15 figure
Automated physical classification in the SDSS DR10. A catalogue of candidate Quasars
We discuss whether modern machine learning methods can be used to
characterize the physical nature of the large number of objects sampled by the
modern multi-band digital surveys. In particular, we applied the MLPQNA (Multi
Layer Perceptron with Quasi Newton Algorithm) method to the optical data of the
Sloan Digital Sky Survey - Data Release 10, investigating whether photometric
data alone suffice to disentangle different classes of objects as they are
defined in the SDSS spectroscopic classification. We discuss three groups of
classification problems: (i) the simultaneous classification of galaxies,
quasars and stars; (ii) the separation of stars from quasars; (iii) the
separation of galaxies with normal spectral energy distribution from those with
peculiar spectra, such as starburst or starforming galaxies and AGN. While
confirming the difficulty of disentangling AGN from normal galaxies on a
photometric basis only, MLPQNA proved to be quite effective in the three-class
separation. In disentangling quasars from stars and galaxies, our method
achieved an overall efficiency of 91.31% and a QSO class purity of ~95%. The
resulting catalogue of candidate quasars/AGNs consists of ~3.6 million objects,
of which about half a million are also flagged as robust candidates, and will
be made available on CDS VizieR facility.Comment: Accepted for publication by MNRAS, 13 pages, 6 figure
Comparative analysis of forward-facing models vs backward-facing models in powertrain component sizing
Powertrain size optimisation based on vehicle class and usage profile is advantageous for reducing emissions. Backward-facing powertrain models, which incorporate scalable powertrain components, have often been used for this purpose. However, due to their quasi-static nature, backward-facing models give very limited information about the limits of the system and drivability of the vehicle. This makes it difficult for control system development and implementation in hardware-in-the-loop (HIL) test systems. This paper investigates the viability of using forward-facing models in the context of powertrain component sizing optimisation. The vehicle model used in this investigation features a conventional powertrain with an internal combustion engine, clutch, manual transmission, and final drive. Simulations that were carried out have indicated that there is minimal effect on the optimal cost with regards to variations in the driver model sensitivity. This opens up the possibility of using forward-facing models for the purpose of powertrain component sizing
Rear wheel torque vectoring model predictive control with velocity regulation for electric vehicles
In this paper we propose a constrained optimal control architecture for combined velocity, yaw and sideslip regulation for stabilisation of the vehicle near the limit of lateral acceleration using the rear axle electric torque vectoring configuration of an electric vehicle. A nonlinear vehicle and tyre model are used to find reference steady-state cornering conditions and design two model predictive control (MPC) strategies of different levels of fidelity: one that uses a linearised version of the full vehicle model with the rear wheels' torques as the input, and another one that neglects the wheel dynamics and uses the rear wheels' slips as the input instead. After analysing the relative trade-offs between performance and computational effort, we compare the two MPC strategies against each other and against an unconstrained optimal control strategy in Simulink and Carsim environment
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