Development of an Algorithm Capable of Classifying the Starting, Gear Change and Engine Brake Variables of a Vehicle by Analyzing OBD II Signals

Rivera Campoverde Néstor Diego, Paúl Andrés Molina Campoverde, Gina Pamela Quirola Novillo, Andrea Karina Naula Bermeo

Resultado de la investigación: Capítulo del libro/informe/acta de congresoContribución de conferenciarevisión exhaustiva

Resumen

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 acquired through the OBD-II connector of a vehicle, by means of a data logger device capable of collecting the information and writing it to a data file. Later the data is post processed in MATLAB® using the unsupervised learning method k-means. Finally, the results obtained by the algorithm are able to discriminate the erroneous values of the signals by means of a multivariate statistical analysis that classifies the study variables: start, gear change and engine brake.

Idioma originalInglés
Título de la publicación alojadaSystems and Information Sciences - Proceedings of ICCIS 2020
EditoresMiguel Botto-Tobar, Willian Zamora, Johnny Larrea Plúa, José Bazurto Roldan, Alex Santamaría Philco
EditorialSpringer Science and Business Media Deutschland GmbH
Páginas121-133
Número de páginas13
ISBN (versión impresa)9783030591939
DOI
EstadoPublicada - 2021
Evento1st International Conference on Systems and Information Sciences, ICCIS 2020 - Manta, Ecuador
Duración: 27 jul 202029 jul 2020

Serie de la publicación

NombreAdvances in Intelligent Systems and Computing
Volumen1273 AISC
ISSN (versión impresa)2194-5357
ISSN (versión digital)2194-5365

Conferencia

Conferencia1st International Conference on Systems and Information Sciences, ICCIS 2020
País/TerritorioEcuador
CiudadManta
Período27/07/2029/07/20

Nota bibliográfica

Publisher Copyright:
© 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG.

Copyright:
Copyright 2020 Elsevier B.V., All rights reserved.

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