Desarrollo de un programa de Eco-Driving a nivel operativo con monitoreo a bordo OBD II en el Área Metropolitana de Bucaramanga

Las estrategias de Eco-Driving buscan controlar y reducir el consumo innecesario de combustible mediante la toma de decisiones antes y durante la conducción. Para desarrollar las estrategias primero se realizó una campaña de monitoreo en condiciones reales de carreta para una muestra de 6 vehículos...

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Autores:: Angulo Sanchez, Laura Valentina

Tipo de recurso:: Trabajo de grado de pregrado

Fecha de publicación:: 2024

Institución:: Universidad Autónoma de Bucaramanga - UNAB

Repositorio:: Repositorio UNAB

Idioma:: spa

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dc.title.spa.fl_str_mv	Desarrollo de un programa de Eco-Driving a nivel operativo con monitoreo a bordo OBD II en el Área Metropolitana de Bucaramanga
dc.title.translated.spa.fl_str_mv	Development of an Eco-Driving program at an operational level with on-board OBD II monitoring in the metropolitan area of Bucaramanga
title	Desarrollo de un programa de Eco-Driving a nivel operativo con monitoreo a bordo OBD II en el Área Metropolitana de Bucaramanga
spellingShingle	Desarrollo de un programa de Eco-Driving a nivel operativo con monitoreo a bordo OBD II en el Área Metropolitana de Bucaramanga Driving styles Eco-driving Random forest Vehicle driving Energetic resources Energy conservation Automobiles Mecatrónica Conducción de vehículos Recursos energéticos Conservación de la energía Automóviles Estilos de conducción Conducción ecológica PCA Kmeans Random forest
title_short	Desarrollo de un programa de Eco-Driving a nivel operativo con monitoreo a bordo OBD II en el Área Metropolitana de Bucaramanga
title_full	Desarrollo de un programa de Eco-Driving a nivel operativo con monitoreo a bordo OBD II en el Área Metropolitana de Bucaramanga
title_fullStr	Desarrollo de un programa de Eco-Driving a nivel operativo con monitoreo a bordo OBD II en el Área Metropolitana de Bucaramanga
title_full_unstemmed	Desarrollo de un programa de Eco-Driving a nivel operativo con monitoreo a bordo OBD II en el Área Metropolitana de Bucaramanga
title_sort	Desarrollo de un programa de Eco-Driving a nivel operativo con monitoreo a bordo OBD II en el Área Metropolitana de Bucaramanga
dc.creator.fl_str_mv	Angulo Sanchez, Laura Valentina
dc.contributor.advisor.none.fl_str_mv	Maradey Lázaro, Jessica Gissella Huertas Cardozo, José Ignacio
dc.contributor.author.none.fl_str_mv	Angulo Sanchez, Laura Valentina
dc.contributor.cvlac.spa.fl_str_mv	Angulo Sanchez, Laura Valentina [1004924368] Maradey Lázaro, Jessica Gissella [0000040553] Huertas Cardozo, José Ignacio [0000057398]
dc.contributor.googlescholar.spa.fl_str_mv	Huertas Cardozo, José Ignacio [es&oi=ao]
dc.contributor.orcid.spa.fl_str_mv	Maradey Lázaro, Jessica Gissella [0000-0003-2319-1965] Huertas Cardozo, José Ignacio [0000-0003-4508-6453]
dc.contributor.apolounab.spa.fl_str_mv	Maradey Lázaro, Jessica Gissella [jessica-gissella-maradey-lázaro]
dc.subject.keywords.spa.fl_str_mv	Driving styles Eco-driving Random forest Vehicle driving Energetic resources Energy conservation Automobiles
topic	Driving styles Eco-driving Random forest Vehicle driving Energetic resources Energy conservation Automobiles Mecatrónica Conducción de vehículos Recursos energéticos Conservación de la energía Automóviles Estilos de conducción Conducción ecológica PCA Kmeans Random forest
dc.subject.lemb.spa.fl_str_mv	Mecatrónica Conducción de vehículos Recursos energéticos Conservación de la energía Automóviles
dc.subject.proposal.spa.fl_str_mv	Estilos de conducción Conducción ecológica PCA Kmeans Random forest
description	Las estrategias de Eco-Driving buscan controlar y reducir el consumo innecesario de combustible mediante la toma de decisiones antes y durante la conducción. Para desarrollar las estrategias primero se realizó una campaña de monitoreo en condiciones reales de carreta para una muestra de 6 vehículos durante 6 meses, el objetivo de la campaña fue recolectar datos de variables de manejo como la velocidad, aceleración, RPM, consumo de combustible, entre otros, mediante un dispositivo OBD II. Para analizar los estilos de conducción, se implementaron dos modelos de aprendizaje no supervisado: Kmeans y DBSCAN. Utilizando las métricas del coeficiente de silueta y el índice de Davies Boulton, se determinó que el método de mayor calidad para la clasificación es Kmeans cuando se itera con las variables de velocidad promedio y aceleración positiva máxima. Los resultados indicaron la existencia de 3 grupos o clústeres que corresponden a los estilos calmado, normal y agresivo. A partir de esta clasificación, se identificaron las características distintivas de cada estilo, lo que permitió plantear estrategias de Ecodriving a nivel operativo, es decir relacionadas directamente con el comportamiento del conductor en carretera. Finalmente, para estimar el ahorro potencial derivado de estas estrategias, se entrenaron cinco modelos de regresión con aprendizaje supervisado. Cada modelo se evaluó utilizando índices de error como MSE, RMSE y R2, Los resultados indicaron que el modelo óptimo para la estimación es Random forest debido a que tiene un RMSE de 4,99 L/100Km siendo el valor más bajo de error y un coeficiente de determinación de 0.91 siendo el valor más alto obtenido, lo cual es indicador de una alta precisión del modelo entrenado. Según los resultados, se concluyó que el Ecodriving posibilita una reducción de hasta el 30% en el consumo de combustible. Esto se traduce en un ahorro económico para el conductor y una disminución en la producción de emisiones contaminantes.
publishDate	2024
dc.date.accessioned.none.fl_str_mv	2024-01-18T15:27:33Z
dc.date.available.none.fl_str_mv	2024-01-18T15:27:33Z
dc.date.issued.none.fl_str_mv	2024-01-17
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dc.type.local.spa.fl_str_mv	Trabajo de Grado
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dc.language.iso.spa.fl_str_mv	spa
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Public Health, 2022. Y. Yao, X. Zhao, C. Liu, J. Rong, Y. Zhang, Z. Dong y Y. Su, Vehicle Fuel Consumption Prediction Method Based on Driving Behavior Data Collected from Smartphones, 2020. A. G. Çapraz, P. Özel, M. Şevkli y Ö. F. Beyca, Fuel Consumption Models Applied to Automobiles Using Real-time Data: A Comparison of Statistical Models, Procedia Comput, 2016, p. pp. 774–781. S. Wickramanayake and H. M. N. Dilum Bandara, «"Fuel consumption prediction of fleet vehicles using Machine Learning: A comparative study,» de 2016 Moratuwa Engineering Research Conference (MERCon), Moratuwa, Sri Lanka, 2016. G. Li, «Machine learning in fuel consumption prediction of aircraft,» de Cogn. Inform. (ICCI), Beijing, China, 2010. F. Schockenhoff, H. Nehse y M. Lienkamp, «Maneuver-based objectification of user comfort affecting aspects of driving style of autonomous vehicle concepts,» Applied Sciences, 2020. P. Jardin, I. Moisidis, S. S. Zetina y S. 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Nikulin, «Driving style identification with unsupervised learning,» International Conference on Machine Learning and Data Mining in Pattern Recognition, 2016. K. Li, L. Jin, Y. Jiang, H. Xian y L. Gao, «Effects of driver behavior style differences and individual differences on driver sleepiness detection,» Advances in Mechanical Engineering, 2015. K. M. Sentoff, L. Aultman-Hall y B. A. Holmén, «Implications of driving style and road grade for accurate vehicle activity data and emissions estimates,» Transportation Research Part D: Transport and Environment, 2015. O. Derbel y R. Landry, «Driving style assessment based on the GPS data and fuzzy inference systems,» 12th International Multi-Conference on Systems, Signals & Devices, 2015. D. Johnson y M. Trivedi, «Driving style recognition using a smartphone as a sensor platform,» 14th International IEEE Conference on Intelligent Transportation Systems, 2011 T. Felstead, M. McDonald y M. Fowkes, «Driving style extremes and potential vehicle emission effects,» Proceedings of the Institution of Civil Engineers-Transport, 2009. J. Gallus, U. Kirchner, R. Vogt y T. Benter, «Impact of driving style and road grade on gaseous exhaust emissions of passenger vehicles measured by a Portable Emission Measurement System (PEMS),» Transportation Research Part D: Transport and Environment, 2017. Y. Feng, S. Pickering, E. Chappell, P. Iravani y C. Brace, «A support vector clustering based approach for driving style classification,» International Journal of Machine Learning and Computing, 2019. W. Han, W. Wang, X. Li y J. Xi, «Statistical-based approach for driving style recognition using Bayesian probability with kernel density estimation,» IET Intelligent Transport Systems, 2019. Y. Feng, S. Pickering, E. Chappell, P. Iravani y C. Brace, «Driving Style Modelling with Adaptive Neuro-Fuzzy Inference System and Real Driving Data,» Conference on Applied Human Factors and Ergonomics, 2018. G. Li, S. E. Li, B. Cheng y P. Green, «Estimation of driving style in naturalistic highway traffic using maneuver transition probabilities,» Transportation Research Part C: Emerging Technologies, 2017. M. Karaduman y H. Eren, «Deep learning based traffic direction sign detection and determining driving style,» 2017 International Conference on Computer Science and Engineering, 2017. F. Jiménez, J. C. Amarillo, J. E. Naranjo, F. Serradilla y A. Díaz, «Energy consumption estimation in electric vehicles considering driving style,,» IEEE 18th International Conference on Intelligent Transportation Systems, 2015. C. Deng, C. Wu, N. Lyu y Z. Huang, «Driving style recognition method using braking characteristics based on hidden Markov model,» PLOS ONE, 2017. Y. Shi, N. Cui y Y. Du, «Energy Management Strategy based on Driving Style Recognition for Plug-in Hybrid Electric Bus,» 2020 39th Chinese Control Conference, 2020. Mensing, F., Bideaux, E., Trigui, R., Ribet, J., & Jeanneret, B., «Mensing, F., Bideaux, E., Trigui, R., Ribet, J., & Jeanneret, B. (2014). Eco-driving: An economic or ecologic driving style? Transportation Research Part C: Emerging Technologies, 38, 110–121. doi:10.1016/j.trc.2013.10.013 ,» Transportation Research Part C: Emerging Technologies, 2014. Mayakuntla, S. K., & Verma, A, «A novel methodology for construction of driving cycles for Indian cities,» Transportation Research Part D: Transport and Environment, 2018. H. Ullerichs, L. Poulsgaard y P. Sørensen, Clustering Based On Driving Style Using Hot Paths. S. J. Navarro & R. A., Desarrollo de un ciclo de conducción bajo condiciones reales en el área metropolitana de Bucaramanga, 2021 Y.Huanga, E. C. Y. Ng, J.L Zhou, N.C. Surawskia, E.F Chanb, & G. Hong, «Eco-driving technology for sustainable road transport: A review,» 2018. T. Colombo, G. Panzani, S. Savaresi y P. Paparo, «Absolute driving style estimation for ground vehicles,» IEEE conference on control technology and applications, 2017.
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spelling	Maradey Lázaro, Jessica Gissellad6570851-23e5-44e4-8c29-fd312d351b94Huertas Cardozo, José Ignacio17418668-38f4-44e4-87f6-3705d87a6144Angulo Sanchez, Laura Valentina777f32a4-f157-4b16-a291-279f9b1ea359Angulo Sanchez, Laura Valentina [1004924368]Maradey Lázaro, Jessica Gissella [0000040553]Huertas Cardozo, José Ignacio [0000057398]Huertas Cardozo, José Ignacio [es&oi=ao]Maradey Lázaro, Jessica Gissella [0000-0003-2319-1965]Huertas Cardozo, José Ignacio [0000-0003-4508-6453]Maradey Lázaro, Jessica Gissella [jessica-gissella-maradey-lázaro]Bucaramanga (Santander, Colombia)UNAB Campus Bucaramanga2024-01-18T15:27:33Z2024-01-18T15:27:33Z2024-01-17http://hdl.handle.net/20.500.12749/23191instname:Universidad Autónoma de Bucaramanga - UNABreponame:Repositorio Institucional UNABrepourl:https://repository.unab.edu.coLas estrategias de Eco-Driving buscan controlar y reducir el consumo innecesario de combustible mediante la toma de decisiones antes y durante la conducción. Para desarrollar las estrategias primero se realizó una campaña de monitoreo en condiciones reales de carreta para una muestra de 6 vehículos durante 6 meses, el objetivo de la campaña fue recolectar datos de variables de manejo como la velocidad, aceleración, RPM, consumo de combustible, entre otros, mediante un dispositivo OBD II. Para analizar los estilos de conducción, se implementaron dos modelos de aprendizaje no supervisado: Kmeans y DBSCAN. Utilizando las métricas del coeficiente de silueta y el índice de Davies Boulton, se determinó que el método de mayor calidad para la clasificación es Kmeans cuando se itera con las variables de velocidad promedio y aceleración positiva máxima. Los resultados indicaron la existencia de 3 grupos o clústeres que corresponden a los estilos calmado, normal y agresivo. A partir de esta clasificación, se identificaron las características distintivas de cada estilo, lo que permitió plantear estrategias de Ecodriving a nivel operativo, es decir relacionadas directamente con el comportamiento del conductor en carretera. Finalmente, para estimar el ahorro potencial derivado de estas estrategias, se entrenaron cinco modelos de regresión con aprendizaje supervisado. Cada modelo se evaluó utilizando índices de error como MSE, RMSE y R2, Los resultados indicaron que el modelo óptimo para la estimación es Random forest debido a que tiene un RMSE de 4,99 L/100Km siendo el valor más bajo de error y un coeficiente de determinación de 0.91 siendo el valor más alto obtenido, lo cual es indicador de una alta precisión del modelo entrenado. Según los resultados, se concluyó que el Ecodriving posibilita una reducción de hasta el 30% en el consumo de combustible. Esto se traduce en un ahorro económico para el conductor y una disminución en la producción de emisiones contaminantes.1.INTRODUCCIÓN ............................................................................................ 15 1.1 DEFINICIÓN DEL PROBLEMA ................................................................ 15 1.2 JUSTIFICACIÓN ........................................................................................... 15 2. OBJETIVOS ..................................................................................................... 19 2.1 OBJETIVO GENERAL .................................................................................. 19 2.2 OBJETIVOS ESPECÍFICOS ........................................................................ 19 3. ESTADO DEL ARTE ...................................................................................... 20 4. MARCO TEORICO ......................................................................................... 25 4.2 ESTILOS DE CONDUCCIÓN ....................................................................... 25 4.2.1 Factores que influyen en los estilos de conducción ............................... 25 4.2.2 Tipos de estilos de conducción .............................................................. 25 4.2.3 Tipos de clasificadores de estilos de conducción .................................. 26 4.1 ECO-DRIVING .............................................................................................. 27 4.1.1 Categorías de las guías de Eco-Driving: ................................................ 28 5. METODOLOGÍA ................................................................................................ 31 6. DESARROLLO .................................................................................................. 32 6.1 SELECCIÓN Y CARACTERIZACIÓN TÉCNICA DE LOS VEHÍCULOS ...... 32 6.2 CARACTERIZACIÓN DE LOS CONDUCTORES ........................................ 33 6.3 CAMPAÑA DE MONITOREO ....................................................................... 34 6.3.1 Selección de la ruta ............................................................................... 34 6.3.2 Selección de las variables ..................................................................... 35 6.3.3 Sistema de monitoreo a bordo ............................................................... 35 6.4 REGISTRO DE DATOS ................................................................................ 38 6.4.1 Adquisicion y transmisión de los datos .................................................. 39 6.4.2 Almacenamiento y envío de datos ......................................................... 42 6.5 ELIMINACIÓN DE DATOS ATÍPICOS ......................................................... 43 6.5.1 Filtrado ................................................................................................... 43 6.5.2 Edición ................................................................................................... 44 6.5.3 Adicción ................................................................................................. 45 6.6 METODOLOGÍA PARA LA SELECCIÓN DE PARÁMETROS CARACTERÍSTICOS .......................................................................................... 46 6.6.1 Segmentación ........................................................................................ 48 6.6.2 Calculo de CPS ..................................................................................... 49 6.6.3 Filtrado según correlación de las variables ............................................ 50 6.6.4 Escalado de datos ................................................................................. 51 6.6.5 Aplicación del modelo de selección de parámetros ............................... 51 6.6.6 Selección de características según importancia .................................... 52 6.7 METODOLOGÍA PARA LA CLASIFICACIÓN DE ESTILOS DE CONDUCCIÓN ................................................................................................... 53 6.7.1 Selección de las características para la clasificación............................. 54 6.7.2 Normalización de los datos .................................................................... 56 6.7.3 Construcción del algoritmo usando modelo DBSCAN ........................... 56 6.7.4 Construcción del algoritmo usando modelo K-means ............................ 59 6.7.5 Evaluación de los modelos .................................................................... 62 6.8 METODOLOGÍA PARA LA CUANTIFICACIÓN DEL CONSUMO DE COMBUSTIBLE .................................................................................................. 64 6.8.1 Construcción del modelo de predicción del consumo de combustible ... 65 6.9 POTENCIA ESPECÍFICA DEL VEHÍCULO (VSP) ....................................... 69 6.9.1 Proceso de obtención de obtención del VSP ......................................... 71 6.10 DISTRIBUCIÓN DE FERCUENCIA DE VELOCIDAD ACELERACIÓN (SAFD) ................................................................................................................ 71 6.10.1 Proceso de obtención del diagrama SAFD .......................................... 72 7. ANÁLISIS DE RESULTADOS ........................................................................... 73 7.1 BASE DE DATOS PROYECTO ACTUAL ..................................................... 73 7.2 BASE DE DATOS CONCATENADA ............................................................ 74 7.3 ANÁLISIS DE LA POTENCIA ESPECÍFICA DEL VEHIÍCULO .................... 75 7.4 ANÁLISIS DE LOS DIAGRAMAS SAFD ...................................................... 77 7.4.1 Análisis de la variable velocidad en los diagramas SAFD ..................... 77 7.4.2 Análisis de la variable aceleración en los diagramas SAFD .................. 78 7.4.3 Análisis general de los diagramas SAFD ............................................... 79 7.6 CLASIFICACIÓN DE ESTILOS DE CONDUCCIÓN ..................................... 79 7.6.1 Estilos de conducción de la base de datos concatenada....................... 80 7.6.2 Correlación de los estilos de conducción con el consumo de combustible ........................................................................................................................ 83 7.6.3 Correlación del consumo de combustible y los estilos de conducción para cada conductor ....................................................................................... 84 7.6.4 Correlación de variables con los estilos de conducción ......................... 85 7.7 ESTRATEGIAS DE ECODRIVING ............................................................... 87 7.7.1 Cuantificación del ahorro de las estrategias de Ecodriving .................... 88 8. CONCLUSIONES ........................................................................................... 91 9. RECOMENDACIONES................................................................................... 93 10. ANEXOS ...................................................................................................... 94 10.1 CÓDIGO PARA LA LIMPIEZA DE DATOS ATIPICOS .......................... 94 10.2 CÓDIGO PARA SELECCIÓN DE PARÁMETROS CARACTERÍSTICOS 95 10.3 CODIGO PARA LA CLASIFICACIÓN DE LOS ESTILOS DE CONDUCCIÓN ................................................................................................... 98 10.4 DASHBOARD PARA VISUALIZACIÓN DE LOS ESTILOS DE CONDUCCIÓN POR CONDUCTOR ................................................................ 103 10.5 CÓDIGO PARA LA ESTIMACIÓN/CUANTIFICACIÓN DEL CONSUMO DE COMBUSTIBLE .......................................................................................... 103 10.6 PROGRAMA CON ESTRATEGIAS DE ECO-DRIVING ...................... 110 BIBLIOGRAFÍA ................................................................................................... 111PregradoEco-Driving strategies seek to control and reduce unnecessary fuel consumption by making decisions before and during driving. To develop the strategies, a monitoring campaign was first carried out in real road conditions for a sample of 6 vehicles for 6 months, the goal of the campaign was to collect data on driving variables such as speed, acceleration, RPM, fuel consumption, among others, using an OBD II device. To analyze driving styles, two unsupervised learning models were implemented: Kmeans and DBSCAN. Using the metrics of the silhouette coefficient and the Davies Boulton index, it was determined that the highest quality method for classification is Kmeans when iterated with the variables of average velocity and maximum positive acceleration. The results indicated the existence of 3 groups or clusters corresponding to the styles calm, normal and aggressive. Based on this classification, the distinctive characteristics of each style were identified, which made it possible to propose Ecodriving strategies at the operational level, i.e. directly related to the behaviour of the driver on the road. Finally, to estimate the potential savings derived from these strategies, five regression models with supervised learning were trained. Each model was evaluated using error indices such as MSE, RMSE and R2. The results indicated that the optimal model for estimation is Random forest because it has a RMSE of 4.99 L/100Km being the lowest error value and a coefficient of determination of 0.91 being the highest value obtained, which is an indicator of a high precision of the trained model. According to the results, it was concluded that Ecodriving allows a reduction of up to 30% in fuel consumption. This translates into economic savings for the driver and a reduction in the production of pollutant emissions.Modalidad Presencialapplication/pdfspahttp://creativecommons.org/licenses/by-nc-nd/2.5/co/Abierto (Texto Completo)Atribución-NoComercial-SinDerivadas 2.5 Colombiahttp://purl.org/coar/access_right/c_abf2Desarrollo de un programa de Eco-Driving a nivel operativo con monitoreo a bordo OBD II en el Área Metropolitana de BucaramangaDevelopment of an Eco-Driving program at an operational level with on-board OBD II monitoring in the metropolitan area of BucaramangaIngeniero MecatrónicoUniversidad Autónoma de Bucaramanga UNABFacultad IngenieríaPregrado Ingeniería Mecatrónicainfo:eu-repo/semantics/bachelorThesisTrabajo de Gradohttp://purl.org/coar/resource_type/c_7a1finfo:eu-repo/semantics/acceptedVersionhttp://purl.org/redcol/resource_type/TPDriving stylesEco-drivingRandom forestVehicle drivingEnergetic resourcesEnergy conservationAutomobilesMecatrónicaConducción de vehículosRecursos energéticosConservación de la energíaAutomóvilesEstilos de conducciónConducción ecológicaPCAKmeansRandom forestAgencia Eureopea de Medio Ambiente, «Transporte,» 2020. 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Paparo, «Absolute driving style estimation for ground vehicles,» IEEE conference on control technology and applications, 2017.https://apolo.unab.edu.co/en/persons/jessica-gissella-maradey-l%C3%A1zaroORIGINALTesis.pdfTesis.pdfTesisapplication/pdf4568257https://repository.unab.edu.co/bitstream/20.500.12749/23191/1/Tesis.pdf01f3ee81c6f558a432a0e3356dba0ee5MD51open accessLicencia.pdfLicencia.pdfLicenciaapplication/pdf324040https://repository.unab.edu.co/bitstream/20.500.12749/23191/5/Licencia.pdf5a0315653bde3b5d344d928eea1f2278MD55metadata only accessTHUMBNAILTesis.pdf.jpgTesis.pdf.jpgIM Thumbnailimage/jpeg4552https://repository.unab.edu.co/bitstream/20.500.12749/23191/6/Tesis.pdf.jpg24528a6df2d58a557a2cd3282977de9bMD56open accessLicencia.pdf.jpgLicencia.pdf.jpgIM Thumbnailimage/jpeg12792https://repository.unab.edu.co/bitstream/20.500.12749/23191/7/Licencia.pdf.jpgea23c5d155d391d1e54dea4ecc9653f9MD57metadata only accessLICENSElicense.txtlicense.txttext/plain; charset=utf-8829https://repository.unab.edu.co/bitstream/20.500.12749/23191/4/license.txt3755c0cfdb77e29f2b9125d7a45dd316MD54open access20.500.12749/23191oai:repository.unab.edu.co:20.500.12749/231912024-01-18 22:01:18.856open accessRepositorio Institucional \| Universidad Autónoma de Bucaramanga - UNABrepositorio@unab.edu.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

Desarrollo de un programa de Eco-Driving a nivel operativo con monitoreo a bordo OBD II en el Área Metropolitana de Bucaramanga

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