Range Prediction for EVs via Crowd-Sourcing

Drivers of electric vehicles (EVs) need an accurate energy prediction in order to prevent running out of battery. We introduce a cloud-based system using crowd-sourced speed profiles for the energy prediction, since they consider the individual driving behaviour and the prevailing traffic congestion. In this paper, we focus on the modular cloud-based energy prediction system which provides three prediction values with various degrees of accuracy and complexity for different user groups. We realise a prototypical driving range prediction before the start of a trip within an application for a mobile device.

[1]  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.

[2]  Markus Lienkamp,et al.  Energy Prediction for EVs Using Support Vector Regression Methods , 2014, IEEE Conf. on Intelligent Systems.

[3]  Stefan Funke,et al.  Optimal Route Planning for Electric Vehicles in Large Networks , 2011, AAAI.

[4]  Phil Blythe,et al.  Use of ITS to overcome barriers to the introduction of electric vehicles in the North East of England , 2012 .

[5]  Phil Blythe,et al.  Routing systems to extend the driving range of electric vehicles , 2013 .

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

[7]  Silviu-Iulian Niculescu,et al.  Energy optimal real-time navigation system: Application to a hybrid electrical vehicle , 2013, ITSC.

[8]  Vitor Monteiro,et al.  Dynamic range prediction for an electric vehicle , 2013, 2013 World Electric Vehicle Symposium and Exhibition (EVS27).

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

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

[11]  Nils J. Nilsson,et al.  A Formal Basis for the Heuristic Determination of Minimum Cost Paths , 1968, IEEE Trans. Syst. Sci. Cybern..

[12]  Markus Lienkamp,et al.  Driver- and situation-specific impact factors for the energy prediction of EVs based on crowd-sourced speed profiles , 2014, 2014 IEEE Intelligent Vehicles Symposium Proceedings.

[13]  Tingting Mu,et al.  Context-Aware and Energy-Driven Route Optimization for Fully Electric Vehicles via Crowdsourcing , 2013, IEEE Transactions on Intelligent Transportation Systems.

[14]  A. Knoll,et al.  The software car: Building ICT architectures for future electric vehicles , 2012, 2012 IEEE International Electric Vehicle Conference.

[15]  Edsger W. Dijkstra,et al.  A note on two problems in connexion with graphs , 1959, Numerische Mathematik.

[16]  Ying Tan,et al.  An Intelligent Multifeature Statistical Approach for the Discrimination of Driving Conditions of a Hybrid Electric Vehicle , 2011, IEEE Transactions on Intelligent Transportation Systems.

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

[18]  Peter Sanders,et al.  Exact Routing in Large Road Networks Using Contraction Hierarchies , 2012, Transp. Sci..

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

[20]  Peter Conradi,et al.  Dynamic Cruising Range Prediction for Electric Vehicles , 2011 .

[21]  João Ferreira Green Route Planner , 2014 .

[22]  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.

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

[24]  Dominik Karbowski,et al.  Using trip information for PHEV fuel consumption minimization , 2013, 2013 World Electric Vehicle Symposium and Exhibition (EVS27).