Zobrazeno 1 - 5
of 5
pro vyhledávání: '"Gina P. Novillo"'
Autor:
Joselyn Elizabeth Morales Espinoza, Paúl Andrés Molina Campoverde, Gina P. Novillo, Néstor Diego Rivera Campoverde, Gabriel Moisés Rodriguez Fernandez
Publikováno v:
Materials Today: Proceedings. 49:8-15
This paper has the purpose of estimating the NOx (Nitrous Oxides) emissions of vehicles equipped with ignition engines caused during the start-up of road slope. Studies currently carried out are scarce on driving right after start-up and its influenc
Autor:
Andrea Karina Naula Bermeo, Rivera Campoverde Néstor Diego, Paúl Andrés Molina Campoverde, Gina P. Novillo
Publikováno v:
Systems and Information Sciences ISBN: 9783030591939
The present project shows an algorithm capable of visualizing the behavior of the PID signals of a vehicle by identifying common driving maneuvers such as: starting, changing gear and engine braking during a road test. The data of this study were acq
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::69fcaf5784e21f08e0754874654b374b
https://doi.org/10.1007/978-3-030-59194-6_11
https://doi.org/10.1007/978-3-030-59194-6_11
Publikováno v:
2019 AEIT International Conference of Electrical and Electronic Technologies for Automotive (AEIT AUTOMOTIVE).
Battery has a fundamental role in energy storage systems for hybrid electric vehicles (HEV), plug-in hybrid electric vehicles (PHEV), electric vehicles (EV) and nowadays in smart grids. The battery state of charge (SOC) behavior is affected by operat
Autor:
Hector Adrian Auquilla Veintimilla, Gina P. Novillo, Néstor Diego Rivera Campoverde, Cesar Daniel Beltrán Orellana
Publikováno v:
International Journal on Advanced Science, Engineering and Information Technology. 10:929
This paper presents a vibration analysis of an internal alternative combustion engine through frequency analysis and wavelet transform, where a form study of the temporary signal and the energy of that signal is carried out to extract certain charact
Publikováno v:
2018 IEEE International Autumn Meeting on Power, Electronics and Computing (ROPEC).
This paper presents a comparison of different machine learning techniques for classification of the unbalance and damage Niquel-Metal Hydride (Ni-MH) battery cells used in hybrid electric vehicles (HEV) and electric vehicles (EV). The implemented lin