A geometry-based algorithm to provide guidance for electric vehicle charging

Abstract Electric vehicles (EVs) have been regarded as effective options for solving the environmental and energy problems in the field of transportation. However, given the limited driving range and insufficient charging stations, searching and selecting charging stations is an important issue for EV drivers during trips. A smart charging service should be developed to help address the charging issue of EV drivers, and a practical algorithm for charging guidance is required to realise it. This study aims to design a geometry-based algorithm for charging guidance that can be effectively applied in the smart charging service. Geographic research findings and geometric approaches are applied to design the algorithm. The algorithm is practical because it is based on the information from drivers’ charging requests, and its total number of calculations is significantly less than that of the conventional shortest-first algorithm. The algorithm is effective because it considers the consistency of direction trend between the charging route and the destination in addition to the travel distance, which conforms to the travel demands of EV drivers. Moreover, simulation examples are presented to demonstrate the proposed algorithm. Results of the proposed algorithm are compared with those of the other two algorithms, which show that the proposed algorithm can obtain a better selection of charging stations for EV drivers from the perspective of entire travel chains and take a shorter computational time.

[1]  Sigal Kaplan,et al.  Harnessing Big-Data for Estimating the Energy Consumption and Driving Range of Electric Vehicles , 2016 .

[2]  Richard Shearmur,et al.  Comparing alternative approaches to measuring the geographical accessibility of urban health services: Distance types and aggregation-error issues , 2008, International journal of health geographics.

[3]  Zhenpo Wang,et al.  Grid Power Peak Shaving and Valley Filling Using Vehicle-to-Grid Systems , 2013, IEEE Transactions on Power Delivery.

[4]  Nan Jiang,et al.  A network equilibrium analysis on destination, route and parking choices with mixed gasoline and electric vehicular flows , 2014, EURO J. Transp. Logist..

[5]  Jennifer Davis,et al.  The challenge of global water access monitoring: evaluating straight-line distance versus self-reported travel time among rural households in Mozambique. , 2014, Journal of water and health.

[6]  Anita Graser,et al.  Sensitivity analysis for energy demand estimation of electric vehicles , 2016 .

[7]  Soumaya Cherkaoui,et al.  Queuing model for EVs charging at public supply stations , 2013, 2013 9th International Wireless Communications and Mobile Computing Conference (IWCMC).

[8]  Carlos F. Daganzo,et al.  Optimal Transit Service atop Ring-radial and Grid Street Networks: A Continuum Approximation Design Method and Comparisons , 2015 .

[9]  Kevin A. Henry,et al.  A Nationwide Comparison of Driving Distance Versus Straight-Line Distance to Hospitals , 2012, The Professional geographer : the journal of the Association of American Geographers.

[10]  M. Fricker,et al.  Biological solutions to transport network design , 2007, Proceedings of the Royal Society B: Biological Sciences.

[11]  A. Keane,et al.  Optimal Charging of Electric Vehicles in Low-Voltage Distribution Systems , 2012, IEEE Transactions on Power Systems.

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

[13]  Samveg Saxena,et al.  Quantifying EV battery end-of-life through analysis of travel needs with vehicle powertrain models , 2015 .

[14]  G. Sanchez,et al.  Direct Load Control Decision Model for Aggregated EV Charging Points , 2012, IEEE Transactions on Power Systems.

[15]  Mashrur Chowdhury,et al.  Utilizing real-time information transferring potentials to vehicles to improve the fast-charging process in electric vehicles , 2013 .

[16]  D. Sumpter,et al.  Shape and efficiency of wood ant foraging networks , 2008, Behavioral Ecology and Sociobiology.

[17]  Shun-Neng Yang,et al.  Charge scheduling of electric vehicles in highways , 2013, Math. Comput. Model..

[18]  Jun Bi,et al.  On-line estimation of state-of-charge of Li-ion batteries in electric vehicle using the resampling particle filter , 2014 .

[19]  Cinzia Cirillo,et al.  Evaluating policies to reduce greenhouse gas emissions from private transportation , 2016 .

[20]  Yafeng Yin,et al.  Network equilibrium models with battery electric vehicles , 2014 .

[21]  Masashi Miyagawa,et al.  Optimal hierarchical system of a grid road network , 2009, Ann. Oper. Res..

[22]  Bi Ju.  A Correlation Analysis and Modeling for Battery SOC and Driving Mileage of Electric Vehicle , 2015 .

[23]  Yang Zhang,et al.  Transport energy consumption and saving in China , 2014 .

[24]  Zhao Yang Dong,et al.  Electric Vehicle Route Optimization Considering Time-of-Use Electricity Price by Learnable Partheno-Genetic Algorithm , 2015, IEEE Transactions on Smart Grid.

[25]  J. Driesen,et al.  The Impact of Charging Plug-In Hybrid Electric Vehicles on a Residential Distribution Grid , 2010, IEEE Transactions on Power Systems.

[26]  Georgios B. Giannakis,et al.  Scalable Electric Vehicle Charging Protocols , 2015, IEEE Transactions on Power Systems.

[27]  Elise Miller-Hooks,et al.  A Green Vehicle Routing Problem , 2012 .

[28]  Jun Bi,et al.  Optimising route choices for the travelling and charging of battery electric vehicles by considering multiple objectives , 2017, Transportation Research Part D: Transport and Environment.

[29]  Yuichi Kobayashi,et al.  A route search method for electric vehicles in consideration of range and locations of charging stations , 2011, 2011 IEEE Intelligent Vehicles Symposium (IV).

[30]  Hashem Oraee,et al.  Strategic charging method for plugged in hybrid electric vehicles in smart grids; a game theoretic approach , 2013 .