Personalized Prediction of Vehicle Energy Consumption Based on Participatory Sensing

The advent of abundant on-board sensors and electronic devices in vehicles populates the paradigm of participatory sensing to harness crowd-sourced data gathering for intelligent transportation applications, such as distance-to-empty prediction and eco-routing. While participatory sensing can provide diverse driving data, there lacks a systematic study of effective utilization of the data for personalized prediction. There are considerable challenges on how to interpolate the missing data from a sparse data set, which often arises from participatory sensing. This paper presents and compares various approaches for personalized vehicle energy consumption prediction, including a blackbox framework that identifies driver/vehicle/environment-dependent factors and a collaborative filtering approach based on matrix factorization. Furthermore, a case study of distance-to-empty prediction for electric vehicles by participatory sensing data is conducted and evaluated empirically, which shows that our approaches can significantly improve the prediction accuracy.

[1]  Sid Chi-Kin Chau,et al.  A social approach for predicting distance-to-empty in vehicles , 2014, e-Energy.

[2]  Torsten Bertram,et al.  A Model-Based Approach for Predicting the Remaining Driving Range in Electric Vehicles , 2013 .

[3]  Andrew Campbell,et al.  The Rise of People-Centric Sensing , 2008, IEEE Internet Computing.

[4]  Kanok Boriboonsomsin,et al.  Impacts of Road Grade on Fuel Consumption and Carbon Dioxide Emissions Evidenced by Use of Advanced Navigation Systems , 2009 .

[5]  Chi-Kin Chau,et al.  Data extraction from electric vehicles through OBD and application of carbon footprint evaluation , 2016 .

[6]  H. Bozdogan.  Model selection and Akaike's Information Criterion (AIC): The general theory and its analytical extensions , 1987 .

[7]  Martin Fellendorf,et al.  Estimating energy consumption for routing algorithms , 2012, 2012 IEEE Intelligent Vehicles Symposium.

[8]  Austin Louis Oehlerking.  StreetSmart : modeling vehicle fuel consumption with mobile phone sensor data through a participatory sensing framework , 2011 .

[9]  Kang G. Shin,et al.  Real-time prediction of battery power requirements for electric vehicles , 2013, 2013 ACM/IEEE International Conference on Cyber-Physical Systems (ICCPS).

[10]  Erik Wilhelm,et al.  A Participatory Sensing Approach for Personalized Distance-to-Empty Prediction and Green Telematics , 2015, e-Energy.

[11]  M. Abou Zeid,et al.  A statistical model of vehicle emissions and fuel consumption , 2002, Proceedings. The IEEE 5th International Conference on Intelligent Transportation Systems.

[12]  Simon Mayer,et al.  CLOUDTHINK: A SCALABLE SECURE PLATFORM FOR MIRRORING TRANSPORTATION SYSTEMS IN THE CLOUD , 2015 .

[13]  Chi-Kin Chau,et al.  On the privacy of crowd-sourced data collection for distance-to-empty prediction and eco-routing , 2016 .

[14]  Yehuda Koren,et al.  Matrix Factorization Techniques for Recommender Systems , 2009, Computer.

[15]  Matthew J. Barth,et al.  Arterial roadway energy/emissions estimation using modal-based trajectory reconstruction , 2011, 2011 14th International IEEE Conference on Intelligent Transportation Systems (ITSC).

[16]  Khaled M. Elbassioni,et al.  Fuel minimization of plug-in hybrid electric vehicles by optimizing drive mode selection , 2016, e-Energy.

[17]  Eva Ericsson,et al.  Independent driving pattern factors and their influence on fuel-use and exhaust emission factors , 2001 .

[18]  Victor C. M. Leung,et al.  Social drive: a crowdsourcing-based vehicular social networking system for green transportation , 2013, DIVANet '13.

[19]  Matthew J. Barth,et al.  Eco-Routing Navigation System Based on Multisource Historical and Real-Time Traffic Information , 2012, IEEE Transactions on Intelligent Transportation Systems.

[20]  Khaled M. Elbassioni,et al.  Drive Mode Optimization and Path Planning for Plug-In Hybrid Electric Vehicles , 2016, IEEE Transactions on Intelligent Transportation Systems.

[21]  Tarek F. Abdelzaher,et al.  GreenGPS: a participatory sensing fuel-efficient maps application , 2010, MobiSys '10.

[22]  Anupam Joshi,et al.  StreetSmart Traffic: Discovering and Disseminating Automobile Congestion Using VANET's , 2007, 2007 IEEE 65th Vehicular Technology Conference - VTC2007-Spring.

[23]  Markus Lienkamp,et al.  A system for cloud-based deviation prediction of propulsion energy consumption for EVs , 2013, Proceedings of 2013 IEEE International Conference on Vehicular Electronics and Safety.

[24]  Martin Leucker,et al.  Efficient Energy-Optimal Routing for Electric Vehicles , 2011, AAAI.

[25]  Markus Lienkamp,et al.  Range Prediction for EVs via Crowd-Sourcing , 2014, 2014 IEEE Vehicle Power and Propulsion Conference (VPPC).

[26]  Philip Chan,et al.  Toward accurate dynamic time warping in linear time and space , 2007, Intell. Data Anal..

[27]  Markus Lienkamp,et al.  A modular and dynamic approach to predict the energy consumption of electric vehicles , 2013 .

[28]  Peter J. Haas,et al.  Large-scale matrix factorization with distributed stochastic gradient descent , 2011, KDD.

[29]  Angelos Amditis,et al.  Online prediction of an electric vehicle remaining range based on regression analysis , 2014, 2014 IEEE International Electric Vehicle Conference (IEVC).

[30]  Hai Yu,et al.  Driving pattern identification for EV range estimation , 2012, 2012 IEEE International Electric Vehicle Conference.