A Deep Learning Method for Monitoring Vehicle Energy Consumption with GPS Data
A monitoring method for energy consumption of vehicles is proposed in the study. It is necessary to have parameters estimating fuel economy with GPS data obtained while driving in the proposed method. The parameters are trained by fuel consumption data measured with a data logger for the reference c...
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description | A monitoring method for energy consumption of vehicles is proposed in the study. It is necessary to have parameters estimating fuel economy with GPS data obtained while driving in the proposed method. The parameters are trained by fuel consumption data measured with a data logger for the reference cars. The data logger is equipped with a GPS sensor and OBD connection capability. The GPS sensor measures vehicle speed, acceleration rate and road gradient. The OBD connector gathers the fuel consumption signaled from OBD port built in the car. The parameters are trained by a 5-layer deep-learning construction with input data (speed, acceleration, gradient) and labels (fuel consumption data) in the typical classification approach. The number of labels is about 6–8 and the number of neurons for hidden layers increases in proportionate to the label numbers. There are about 160–200 parameters. The parameters are calibrated to consider the wide range of fuel efficiency and deterioration degree in age for various test cars. The calibration factor is made from the certified fuel economy and model year taken from the car registration form. The error range of the estimated fuel economy from the measured value is about −6% to +7% for the eight test cars. It is accurate enough to capture the vehicle dynamics for using the input and output data in point-to-point classification style for training steps. Further, it is simple enough to hit fuel economy of the other test cars because fuel economy is a kind of averaged value of fuel consumption for the time period or driven distance for monitoring steps. You can predict or monitor energy consumption for any vehicle with the GPS-measured speed/acceleration/gradient data by the pre-trained parameters and calibration factors of the reference vehicles according to fuel types such as gasoline, diesel and electric. The proposed method requires just a GPS sensor that is cheap and common, and the calculating procedure is so simple that you can monitor energy consumption of various vehicles in real-time with ease. However, it does not consider weight, weather and auxiliary changes and these effects will be addressed in the future works with a monitoring service system under preparation. |
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It is necessary to have parameters estimating fuel economy with GPS data obtained while driving in the proposed method. The parameters are trained by fuel consumption data measured with a data logger for the reference cars. The data logger is equipped with a GPS sensor and OBD connection capability. The GPS sensor measures vehicle speed, acceleration rate and road gradient. The OBD connector gathers the fuel consumption signaled from OBD port built in the car. The parameters are trained by a 5-layer deep-learning construction with input data (speed, acceleration, gradient) and labels (fuel consumption data) in the typical classification approach. The number of labels is about 6–8 and the number of neurons for hidden layers increases in proportionate to the label numbers. There are about 160–200 parameters. The parameters are calibrated to consider the wide range of fuel efficiency and deterioration degree in age for various test cars. The calibration factor is made from the certified fuel economy and model year taken from the car registration form. The error range of the estimated fuel economy from the measured value is about −6% to +7% for the eight test cars. It is accurate enough to capture the vehicle dynamics for using the input and output data in point-to-point classification style for training steps. Further, it is simple enough to hit fuel economy of the other test cars because fuel economy is a kind of averaged value of fuel consumption for the time period or driven distance for monitoring steps. You can predict or monitor energy consumption for any vehicle with the GPS-measured speed/acceleration/gradient data by the pre-trained parameters and calibration factors of the reference vehicles according to fuel types such as gasoline, diesel and electric. The proposed method requires just a GPS sensor that is cheap and common, and the calculating procedure is so simple that you can monitor energy consumption of various vehicles in real-time with ease. However, it does not consider weight, weather and auxiliary changes and these effects will be addressed in the future works with a monitoring service system under preparation.</description><identifier>ISSN: 2071-1050</identifier><identifier>EISSN: 2071-1050</identifier><identifier>DOI: 10.3390/su132011331</identifier><language>eng</language><publisher>Basel: MDPI AG</publisher><subject>Acceleration ; Automobiles ; Calibration ; Classification ; Deep learning ; Diesel fuels ; Driver behavior ; Driving ability ; Economics ; Electric vehicles ; Electricity ; Energy consumption ; Energy efficiency ; Fuel consumption ; Fuel economy ; Gasoline ; Global positioning systems ; GPS ; Labels ; Machine learning ; Monitoring ; Monitoring systems ; Parameter estimation ; Sensors ; Spatial data ; Traffic ; Traffic speed ; Velocity</subject><ispartof>Sustainability, 2021-10, Vol.13 (20), p.11331</ispartof><rights>2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c298t-e3891244a777e22bb9579d22db4e5c0b2e7881ee8b244874709a456f18cc5fc93</citedby><cites>FETCH-LOGICAL-c298t-e3891244a777e22bb9579d22db4e5c0b2e7881ee8b244874709a456f18cc5fc93</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,780,784,27924,27925</link.rule.ids></links><search><creatorcontrib>Ko, Kwangho</creatorcontrib><creatorcontrib>Lee, Tongwon</creatorcontrib><creatorcontrib>Jeong, Seunghyun</creatorcontrib><title>A Deep Learning Method for Monitoring Vehicle Energy Consumption with GPS Data</title><title>Sustainability</title><description>A monitoring method for energy consumption of vehicles is proposed in the study. It is necessary to have parameters estimating fuel economy with GPS data obtained while driving in the proposed method. The parameters are trained by fuel consumption data measured with a data logger for the reference cars. The data logger is equipped with a GPS sensor and OBD connection capability. The GPS sensor measures vehicle speed, acceleration rate and road gradient. The OBD connector gathers the fuel consumption signaled from OBD port built in the car. The parameters are trained by a 5-layer deep-learning construction with input data (speed, acceleration, gradient) and labels (fuel consumption data) in the typical classification approach. The number of labels is about 6–8 and the number of neurons for hidden layers increases in proportionate to the label numbers. There are about 160–200 parameters. The parameters are calibrated to consider the wide range of fuel efficiency and deterioration degree in age for various test cars. The calibration factor is made from the certified fuel economy and model year taken from the car registration form. The error range of the estimated fuel economy from the measured value is about −6% to +7% for the eight test cars. It is accurate enough to capture the vehicle dynamics for using the input and output data in point-to-point classification style for training steps. Further, it is simple enough to hit fuel economy of the other test cars because fuel economy is a kind of averaged value of fuel consumption for the time period or driven distance for monitoring steps. You can predict or monitor energy consumption for any vehicle with the GPS-measured speed/acceleration/gradient data by the pre-trained parameters and calibration factors of the reference vehicles according to fuel types such as gasoline, diesel and electric. The proposed method requires just a GPS sensor that is cheap and common, and the calculating procedure is so simple that you can monitor energy consumption of various vehicles in real-time with ease. However, it does not consider weight, weather and auxiliary changes and these effects will be addressed in the future works with a monitoring service system under preparation.</description><subject>Acceleration</subject><subject>Automobiles</subject><subject>Calibration</subject><subject>Classification</subject><subject>Deep learning</subject><subject>Diesel fuels</subject><subject>Driver behavior</subject><subject>Driving ability</subject><subject>Economics</subject><subject>Electric vehicles</subject><subject>Electricity</subject><subject>Energy consumption</subject><subject>Energy efficiency</subject><subject>Fuel consumption</subject><subject>Fuel economy</subject><subject>Gasoline</subject><subject>Global positioning systems</subject><subject>GPS</subject><subject>Labels</subject><subject>Machine learning</subject><subject>Monitoring</subject><subject>Monitoring systems</subject><subject>Parameter estimation</subject><subject>Sensors</subject><subject>Spatial data</subject><subject>Traffic</subject><subject>Traffic speed</subject><subject>Velocity</subject><issn>2071-1050</issn><issn>2071-1050</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2021</creationdate><recordtype>article</recordtype><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><recordid>eNpNkE1LAzEQhoMoWGpP_oGAR1nNx6ZJjqWtVWhV8OO6ZLOz7ZY2WZMs0n_vlnroXN7h5WEGHoRuKXngXJPH2FHOCKWc0ws0YETSjBJBLs_2azSKcUv66SFNxwP0OsEzgBYvwQTXuDVeQdr4Ctc-4JV3TfLh2H7DprE7wHMHYX3AU-9it29T4x3-bdIGL94_8Mwkc4OuarOLMPrPIfp6mn9On7Pl2-JlOllmlmmVMuBKU5bnRkoJjJWlFlJXjFVlDsKSkoFUigKosoeUzCXRJhfjmiprRW01H6K70902-J8OYiq2vguuf1kwoXJBOeWip-5PlA0-xgB10YZmb8KhoKQ4OivOnPE_FM1cQQ</recordid><startdate>20211014</startdate><enddate>20211014</enddate><creator>Ko, Kwangho</creator><creator>Lee, Tongwon</creator><creator>Jeong, Seunghyun</creator><general>MDPI AG</general><scope>AAYXX</scope><scope>CITATION</scope><scope>4U-</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope></search><sort><creationdate>20211014</creationdate><title>A Deep Learning Method for Monitoring Vehicle Energy Consumption with GPS Data</title><author>Ko, Kwangho ; Lee, Tongwon ; Jeong, Seunghyun</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c298t-e3891244a777e22bb9579d22db4e5c0b2e7881ee8b244874709a456f18cc5fc93</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2021</creationdate><topic>Acceleration</topic><topic>Automobiles</topic><topic>Calibration</topic><topic>Classification</topic><topic>Deep learning</topic><topic>Diesel fuels</topic><topic>Driver behavior</topic><topic>Driving ability</topic><topic>Economics</topic><topic>Electric vehicles</topic><topic>Electricity</topic><topic>Energy consumption</topic><topic>Energy efficiency</topic><topic>Fuel consumption</topic><topic>Fuel economy</topic><topic>Gasoline</topic><topic>Global positioning systems</topic><topic>GPS</topic><topic>Labels</topic><topic>Machine learning</topic><topic>Monitoring</topic><topic>Monitoring systems</topic><topic>Parameter estimation</topic><topic>Sensors</topic><topic>Spatial data</topic><topic>Traffic</topic><topic>Traffic speed</topic><topic>Velocity</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Ko, Kwangho</creatorcontrib><creatorcontrib>Lee, Tongwon</creatorcontrib><creatorcontrib>Jeong, Seunghyun</creatorcontrib><collection>CrossRef</collection><collection>University Readers</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest Central UK/Ireland</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>Access via ProQuest (Open Access)</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><jtitle>Sustainability</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Ko, Kwangho</au><au>Lee, Tongwon</au><au>Jeong, Seunghyun</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>A Deep Learning Method for Monitoring Vehicle Energy Consumption with GPS Data</atitle><jtitle>Sustainability</jtitle><date>2021-10-14</date><risdate>2021</risdate><volume>13</volume><issue>20</issue><spage>11331</spage><pages>11331-</pages><issn>2071-1050</issn><eissn>2071-1050</eissn><abstract>A monitoring method for energy consumption of vehicles is proposed in the study. It is necessary to have parameters estimating fuel economy with GPS data obtained while driving in the proposed method. The parameters are trained by fuel consumption data measured with a data logger for the reference cars. The data logger is equipped with a GPS sensor and OBD connection capability. The GPS sensor measures vehicle speed, acceleration rate and road gradient. The OBD connector gathers the fuel consumption signaled from OBD port built in the car. The parameters are trained by a 5-layer deep-learning construction with input data (speed, acceleration, gradient) and labels (fuel consumption data) in the typical classification approach. The number of labels is about 6–8 and the number of neurons for hidden layers increases in proportionate to the label numbers. There are about 160–200 parameters. The parameters are calibrated to consider the wide range of fuel efficiency and deterioration degree in age for various test cars. The calibration factor is made from the certified fuel economy and model year taken from the car registration form. The error range of the estimated fuel economy from the measured value is about −6% to +7% for the eight test cars. It is accurate enough to capture the vehicle dynamics for using the input and output data in point-to-point classification style for training steps. Further, it is simple enough to hit fuel economy of the other test cars because fuel economy is a kind of averaged value of fuel consumption for the time period or driven distance for monitoring steps. You can predict or monitor energy consumption for any vehicle with the GPS-measured speed/acceleration/gradient data by the pre-trained parameters and calibration factors of the reference vehicles according to fuel types such as gasoline, diesel and electric. The proposed method requires just a GPS sensor that is cheap and common, and the calculating procedure is so simple that you can monitor energy consumption of various vehicles in real-time with ease. However, it does not consider weight, weather and auxiliary changes and these effects will be addressed in the future works with a monitoring service system under preparation.</abstract><cop>Basel</cop><pub>MDPI AG</pub><doi>10.3390/su132011331</doi><oa>free_for_read</oa></addata></record> |
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subjects | Acceleration Automobiles Calibration Classification Deep learning Diesel fuels Driver behavior Driving ability Economics Electric vehicles Electricity Energy consumption Energy efficiency Fuel consumption Fuel economy Gasoline Global positioning systems GPS Labels Machine learning Monitoring Monitoring systems Parameter estimation Sensors Spatial data Traffic Traffic speed Velocity |
title | A Deep Learning Method for Monitoring Vehicle Energy Consumption with GPS Data |
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