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Accuracy Evaluation by GPS Data Correction for the EV Energy Consumption Database

Published: 28 November 2016 Publication History

Abstract

Electric vehicles (EVs) are expected to be applicable to smart grids because they have large-capacity batteries. It is important that smart grid users be able to estimate surplus battery energy and/or surplus capacity in advance of deploying EVs. We constructed a database, the Energy COnsumption LOG (ECOLOG) Database System, to store vehicle daily logs acquired by smartphones placed in vehicles. The electrical energy consumption is estimated from GPS coordinate data using an EV energy-consumption model. This research specifically examines commuting with a vehicle used for same route every day. We corrected GPS coordinate data by map matching, and input the data to the EV energy consumption model. We regard the remaining battery capacity data acquired by the EV CAN as correct data. Then we evaluate the accuracy of driving energy consumption logs as estimated using the corrected GPS coordinate data.

References

[1]
W. Kempton, J. Tomic, "Vehicle-to-grid power fundamentals: Calculating capacity and net revenue", Journal of Power Sources, Vol. 144, No. 1, pp. 268--279, 2005.
[2]
B. Jansen, C. Binding, O. Sundstrom, D. Gantenbein, "Architecture and Communication of an Electric Vehicle Virtual Power Plant", First IEEE Intl. Conf. Smart Grid Communication (SmartGridComm2010), pp. 149--154, November, 2010.
[3]
D. Kawanuma, Y. Kashiwabara, T. Uemura, T. Tomii, "Data Analysis Framework for Visualizing Correlation of Energy Consumption and Transit Time in Road Sections using the ECOLOG database", International Workshop:MUSICAL16, Hiroshima, 2016 (to appear).
[4]
R. Sanui, S. Hagimoto, T. Tomii, "Estimation and Verification of Air Conditioning Energy Consumption in Winter based on Weather Condition by the Database of EV Energy Consumption Log", (in Japanese) 12th ITS Symposium, 2-2C-01, 2014.
[5]
Y. Zhang, W. Wang, Y. Kobayashi, K. Shirai, "Remaining Driving Range Estimation of Electric Vehicle", Third IEEE International Electric Vehicle Conference (IEVC2012), pp. 1--7, 2012.
[6]
M. Martinez, A. Gardel, A.M. Wefky, F. Espinosa, J.L. Lazaro, I. Bravo, P. Revenga, "Electric Vehicle Consumption Estimation based on Heuristics and MLP Artificial Neural Network", European Electric Vehicle Congress (EEVC), pp. 1--7, 2012.
[7]
V. Manzoni, A. Corti, P.D. Luca, S.M. Savaresi, "Driving Style Estimation via Inertial Measurements", 13th International IEEE Annual Conference on Intelligent Transportation Systems (ITSC 2010), pp. 777--782, 2010.
[8]
D. Sawada, M. Sato, K. Uehara, J. Murai, "iDANS: A Platform for Disseminating Information on a VANET Consisting of Smartphone Nodes", International Conference on ITS Telecommunications (ITST2011), pp. 252--257, 2011.
[9]
J. Paefgen, F. Kehr, Y. Zhai, F. Michahelles, "Driving Behavior Analysis with Smartphones: Insights from a Controlled Field Study", Proc. Tenth ACM SIGMOBILE Conference on Mobile and Ubiquitous Multimedia (MUM'12), 2012, paper#36, pp. 1--8, 2012.
[10]
D. Karbowski, V. Sokolov, A. Rousseau, "Vehicle Energy Management Optimisation through Digital Maps and Connectivity", 22nd ITS World Congress, paprt#1952, pp. 1--10, 2015.
[11]
T. Tomii, S. Hagimoto, N. Fueda, T. Deguchi, M. Idenawa, T. Hayashi, "Long-Term Experiment of the ECOLOG Database Capability of Estimating V2X Effect Replacing with EVs", 20th ITS World Congress, Tokyo, paper#3132, pp. 1--10, 2013.
[12]
Y. Hirota, S. Ogasawara, eds., H. Funato, T. Mihara, Y. Deguchi, T. Hatsuda, "Electric Vehicle Engineering", Tokyo, Morikita Publishing Co. Ltd., 2010.
[13]
Geospatial Information Authority of Japan, "Digital Map 2500(Spatial Data Framework)" http://www.gsi.go.jp/geoinfo/dmap/dm2500sdf/, (Accessed Date: Aug. 11, 2016).
[14]
S. Brakatsoulas, D. Pfoser, R. Salas, C. Wenk, "On Map-Matching Vehicle Tracking Data", Proc. 31st International Conference on Very Large Data Bases, pp. 853--864, 2005.
[15]
"Leaf Spy Pro", Google Play, https://play.google.com/store/apps/details?id=com.Turbo3.Leaf_Spy_Pro, (Accessed Date: Aug. 11, 2016).
[16]
PLX Devices Inc., "PLX Kiwi Bluetooth", http://www.plxdevices.com/product_info.php?id=GSSTBLUETOOTH, (Accessed Date: Aug. 11, 2016).

Cited By

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  • (2024)Design of an Electric Vehicles’ Energy Baseline Map and Application for Energy Consumption AnalysisDatabase Systems for Advanced Applications10.1007/978-981-97-5575-2_9(139-154)Online publication date: 2-Sep-2024
  • (2021)DeepFEC: Energy Consumption Prediction under Real-World Driving Conditions for Smart CitiesProceedings of the Web Conference 202110.1145/3442381.3449983(1880-1890)Online publication date: 19-Apr-2021
  • (2017)Error distribution modeling of embedded sensors on smartphones by using laser ranger2017 IEEE International Conference on Multimedia & Expo Workshops (ICMEW)10.1109/ICMEW.2017.8026218(387-392)Online publication date: Jul-2017

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cover image ACM Other conferences
MOBIQUITOUS 2016: Adjunct Proceedings of the 13th International Conference on Mobile and Ubiquitous Systems: Computing Networking and Services
November 2016
280 pages
ISBN:9781450347594
DOI:10.1145/3004010
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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New York, NY, United States

Publication History

Published: 28 November 2016

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Author Tags

  1. energy
  2. map matching
  3. sensor database
  4. smart grid
  5. vehicle daily log

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MOBIQUITOUS 2016
MOBIQUITOUS 2016: Computing Networking and Services
November 28 - December 1, 2016
Hiroshima, Japan

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Overall Acceptance Rate 26 of 87 submissions, 30%

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Cited By

View all
  • (2024)Design of an Electric Vehicles’ Energy Baseline Map and Application for Energy Consumption AnalysisDatabase Systems for Advanced Applications10.1007/978-981-97-5575-2_9(139-154)Online publication date: 2-Sep-2024
  • (2021)DeepFEC: Energy Consumption Prediction under Real-World Driving Conditions for Smart CitiesProceedings of the Web Conference 202110.1145/3442381.3449983(1880-1890)Online publication date: 19-Apr-2021
  • (2017)Error distribution modeling of embedded sensors on smartphones by using laser ranger2017 IEEE International Conference on Multimedia & Expo Workshops (ICMEW)10.1109/ICMEW.2017.8026218(387-392)Online publication date: Jul-2017

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