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

Juan José Molina Campoverde

doi:10.1007/978-3-030-42520-3_7

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 proceeding › Conference contribution › peer-review

3 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 language	English
Title of host publication	Applied Technologies - 1st International Conference, ICAT 2019, Proceedings
Editors	Miguel Botto-Tobar, Marcelo Zambrano Vizuete, Pablo Torres-Carrión, Sergio Montes León, Guillermo Pizarro Vásquez, Benjamin Durakovic
Publisher	Springer
Pages	80-91
Number of pages	12
ISBN (Print)	9783030425197
DOIs	https://doi.org/10.1007/978-3-030-42520-3_7
State	Published - 1 Jan 2020
Externally published	Yes
Event	1st International Conference on Applied Technologies, ICAT 2019 - Quito, Ecuador Duration: 3 Dec 2019 → 5 Dec 2019

Publication series

Name	Communications in Computer and Information Science
Volume	1194 CCIS
ISSN (Print)	1865-0929
ISSN (Electronic)	1865-0937

Conference

Conference	1st International Conference on Applied Technologies, ICAT 2019
Country/Territory	Ecuador
City	Quito
Period	3/12/19 → 5/12/19

Keywords

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

Access to Document

10.1007/978-3-030-42520-3_7

Cite this

Molina Campoverde, J. J. (2020). Driving Mode Estimation Model Based in Machine Learning Through PID’s Signals Analysis Obtained From OBD II. In M. Botto-Tobar, M. Zambrano Vizuete, P. Torres-Carrión, S. Montes León, G. Pizarro Vásquez, & B. Durakovic (Eds.), Applied Technologies - 1st International Conference, ICAT 2019, Proceedings (pp. 80-91). (Communications in Computer and Information Science; Vol. 1194 CCIS). Springer. https://doi.org/10.1007/978-3-030-42520-3_7

Molina Campoverde, Juan José. / Driving Mode Estimation Model Based in Machine Learning Through PID’s Signals Analysis Obtained From OBD II. Applied Technologies - 1st International Conference, ICAT 2019, Proceedings. editor / Miguel Botto-Tobar ; Marcelo Zambrano Vizuete ; Pablo Torres-Carrión ; Sergio Montes León ; Guillermo Pizarro Vásquez ; Benjamin Durakovic. Springer, 2020. pp. 80-91 (Communications in Computer and Information Science).

@inproceedings{8ff01c9179534db8bd5c8eec2ef5c5ec,

title = "Driving Mode Estimation Model Based in Machine Learning Through PID{\textquoteright}s Signals Analysis Obtained From OBD II",

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.",

keywords = "Eco drive, Fuel consumption, K-means, OBD II, Random forest, S-Golay",

author = "{Molina Campoverde}, {Juan Jos{\'e}}",

year = "2020",

month = jan,

day = "1",

doi = "10.1007/978-3-030-42520-3_7",

language = "English",

isbn = "9783030425197",

series = "Communications in Computer and Information Science",

publisher = "Springer",

pages = "80--91",

editor = "Miguel Botto-Tobar and {Zambrano Vizuete}, Marcelo and Pablo Torres-Carri{\'o}n and {Montes Le{\'o}n}, Sergio and {Pizarro V{\'a}squez}, Guillermo and Benjamin Durakovic",

booktitle = "Applied Technologies - 1st International Conference, ICAT 2019, Proceedings",

note = "1st International Conference on Applied Technologies, ICAT 2019 ; Conference date: 03-12-2019 Through 05-12-2019",

}

Molina Campoverde, JJ 2020, Driving Mode Estimation Model Based in Machine Learning Through PID’s Signals Analysis Obtained From OBD II. in M Botto-Tobar, M Zambrano Vizuete, P Torres-Carrión, S Montes León, G Pizarro Vásquez & B Durakovic (eds), Applied Technologies - 1st International Conference, ICAT 2019, Proceedings. Communications in Computer and Information Science, vol. 1194 CCIS, Springer, pp. 80-91, 1st International Conference on Applied Technologies, ICAT 2019, Quito, Ecuador, 3/12/19. https://doi.org/10.1007/978-3-030-42520-3_7

Driving Mode Estimation Model Based in Machine Learning Through PID’s Signals Analysis Obtained From OBD II. / Molina Campoverde, Juan José.
Applied Technologies - 1st International Conference, ICAT 2019, Proceedings. ed. / Miguel Botto-Tobar; Marcelo Zambrano Vizuete; Pablo Torres-Carrión; Sergio Montes León; Guillermo Pizarro Vásquez; Benjamin Durakovic. Springer, 2020. p. 80-91 (Communications in Computer and Information Science; Vol. 1194 CCIS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Driving Mode Estimation Model Based in Machine Learning Through PID’s Signals Analysis Obtained From OBD II

AU - Molina Campoverde, Juan José

PY - 2020/1/1

Y1 - 2020/1/1

N2 - 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.

AB - 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.

KW - Eco drive

KW - Fuel consumption

KW - K-means

KW - OBD II

KW - Random forest

KW - S-Golay

UR - http://www.scopus.com/inward/record.url?scp=85082387601&partnerID=8YFLogxK

UR - https://www.mendeley.com/catalogue/c366279c-e530-3103-ad9a-18c3dcc0f8fd/

U2 - 10.1007/978-3-030-42520-3_7

DO - 10.1007/978-3-030-42520-3_7

M3 - Conference contribution

AN - SCOPUS:85082387601

SN - 9783030425197

T3 - Communications in Computer and Information Science

SP - 80

EP - 91

BT - Applied Technologies - 1st International Conference, ICAT 2019, Proceedings

A2 - Botto-Tobar, Miguel

A2 - Zambrano Vizuete, Marcelo

A2 - Torres-Carrión, Pablo

A2 - Montes León, Sergio

A2 - Pizarro Vásquez, Guillermo

A2 - Durakovic, Benjamin

PB - Springer

T2 - 1st International Conference on Applied Technologies, ICAT 2019

Y2 - 3 December 2019 through 5 December 2019

ER -

Molina Campoverde JJ. Driving Mode Estimation Model Based in Machine Learning Through PID’s Signals Analysis Obtained From OBD II. In Botto-Tobar M, Zambrano Vizuete M, Torres-Carrión P, Montes León S, Pizarro Vásquez G, Durakovic B, editors, Applied Technologies - 1st International Conference, ICAT 2019, Proceedings. Springer. 2020. p. 80-91. (Communications in Computer and Information Science). doi: 10.1007/978-3-030-42520-3_7

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

Abstract

Publication series

Conference

Keywords

Access to Document

Other files and links

Fingerprint

Cite this