Driving Mode Estimation Model Based in Machine Learning Through PID’s Signals Analysis Obtained From OBD II

Juan José Molina Campoverde

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Scopus citations

Abstract

In this paper a driving mode estimation model based in machine learning architecture is presented. With the statistic method, Random Forest, the highest inference of driving variables is determined through the best attributes for a training model based in OBD II data. Engine sensors variables are obtained with the aim of explaining the behavior of the PID signals in relation to the driving mode of a person, according to specific consumption and engine performance, characterizing the signals behavior in relation to the different driving modes. The investigation consists of 4 power tests in the dynamometer bank at 25%, 50%, 75% and 100% throttle valve opening to determine the relationship between engine performance and normal vehicle circulation, through the engine most influential variables like MAP, TPS, VSS, Ax and each the transmission ratio infer in the fuel consumption study and engine performance. In this study Random Forest is used achieving an accuracy rate of 0.98905.

Original languageEnglish
Title of host publicationApplied Technologies - 1st International Conference, ICAT 2019, Proceedings
EditorsMiguel Botto-Tobar, Marcelo Zambrano Vizuete, Pablo Torres-Carrión, Sergio Montes León, Guillermo Pizarro Vásquez, Benjamin Durakovic
PublisherSpringer
Pages80-91
Number of pages12
ISBN (Print)9783030425197
DOIs
StatePublished - 1 Jan 2020
Externally publishedYes
Event1st International Conference on Applied Technologies, ICAT 2019 - Quito, Ecuador
Duration: 3 Dec 20195 Dec 2019

Publication series

NameCommunications in Computer and Information Science
Volume1194 CCIS
ISSN (Print)1865-0929
ISSN (Electronic)1865-0937

Conference

Conference1st International Conference on Applied Technologies, ICAT 2019
Country/TerritoryEcuador
CityQuito
Period3/12/195/12/19

Keywords

  • Eco drive
  • Fuel consumption
  • K-means
  • OBD II
  • Random forest
  • S-Golay

Fingerprint

Dive into the research topics of 'Driving Mode Estimation Model Based in Machine Learning Through PID’s Signals Analysis Obtained From OBD II'. Together they form a unique fingerprint.

Cite this