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

Producción científica: Capítulo del libro/informe/acta de congreso › Contribución de conferencia › revisión exhaustiva

5 Citas (Scopus)

Resumen

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.

Idioma original	Inglés
Título de la publicación alojada	Applied Technologies - 1st International Conference, ICAT 2019, Proceedings
Editores	Miguel Botto-Tobar, Marcelo Zambrano Vizuete, Pablo Torres-Carrión, Sergio Montes León, Guillermo Pizarro Vásquez, Benjamin Durakovic
Editorial	Springer
Páginas	80-91
Número de páginas	12
ISBN (versión impresa)	9783030425197
DOI	https://doi.org/10.1007/978-3-030-42520-3_7
Estado	Publicada - 1 ene. 2020
Publicado de forma externa	Sí
Evento	1st International Conference on Applied Technologies, ICAT 2019 - Quito, Ecuador Duración: 3 dic. 2019 → 5 dic. 2019

Serie de la publicación

Nombre	Communications in Computer and Information Science
Volumen	1194 CCIS
ISSN (versión impresa)	1865-0929
ISSN (versión digital)	1865-0937

Conferencia

Conferencia	1st International Conference on Applied Technologies, ICAT 2019
País/Territorio	Ecuador
Ciudad	Quito
Período	3/12/19 → 5/12/19

Nota bibliográfica

Publisher Copyright:
© 2020, Springer Nature Switzerland AG.

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

Otros archivos y enlaces

Citar esto

Molina Campoverde, J. J. (2020). Driving Mode Estimation Model Based in Machine Learning Through PID’s Signals Analysis Obtained From OBD II. En 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}}",

note = "Publisher Copyright: {\textcopyright} 2020, Springer Nature Switzerland AG.; 1st International Conference on Applied Technologies, ICAT 2019 ; Conference date: 03-12-2019 Through 05-12-2019",

year = "2020",

month = jan,

day = "1",

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

language = "English",

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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",

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Molina Campoverde, JJ 2020, Driving Mode Estimation Model Based in Machine Learning Through PID’s Signals Analysis Obtained From OBD II. En 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).

Producción científica: Capítulo del libro/informe/acta de congreso › Contribución de conferencia › revisión exhaustiva

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

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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. En Botto-Tobar M, Zambrano Vizuete M, Torres-Carrión P, Montes León S, Pizarro Vásquez G, Durakovic B, editores, 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

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