Energy Consumption Model and Charging Station Placement for Electric Vehicles

Zonggen Yi, Peter H. Bauer

Abstract

A detailed energy consumption model is introduced for electric vehicles (EVs), that takes into account all tractive effort components, regenerative braking, and parasitic power users. Based on this model a software tool for EV reachable range estimation (EVRE) is developed and implemented. This software tool uses real driving distances and elevation data from Google Maps and can therefore much more accurate predict the reachable range of a given EV than the typical Euclidean distance models. Furthermore, an optimization model for the placement of charging stations to maximize the number of reachable households under energy constraints is established using EVRE. These results are illustrated by a number of examples involving the cities of New York City, Boulder Colorado, and South Bend, Indiana. The developed methodology can easily incorporate additional constraints such as popular destinations, preferred parking, driver habits, available power infrastructure, etc. to initially reduce the search space for optimal charging station placement.

References

  1. Andrews, M., Dogru, M. K., Hobby, J. D., Jin, Y., and Tucci, G. H. (2013). Modeling and optimization for electric vehicle charging infrastructure.
  2. Chen, T. D., Kockelman, K. M., Khan, M., and Modeler, T. (2013). The electric vehicle charging station location problem: A parking-based assignment method for seattle. In Transportation Research Board 92nd Annual Meeting, number 13-1254.
  3. Dickerman, L. and Harrison, J. (2010). A new car, a new grid. Power and Energy Magazine, IEEE, 8(2):55-61.
  4. Feng, L., Ge, S., and Liu, H. (2012). Electric vehicle charging station planning based on weighted voronoi diagram. In Power and Energy Engineering Conference (APPEEC), 2012 Asia-Pacific, pages 1-5. IEEE.
  5. Frade, I., Ribeiro, A., Gonc¸alves, G., and Antunes, A. P. (2011). Optimal location of charging stations for electric vehicles in a neighborhood in lisbon, portugal. Transportation Research Record: Journal of the Transportation Research Board, 2252(1):91-98.
  6. Ge, S., Feng, L., and Liu, H. (2011). The planning of electric vehicle charging station based on grid partition method. In Electrical and Control Engineering (ICECE), 2011 International Conference on, pages 2726-2730. IEEE.
  7. Google. Google maps javascript api v3. https://developers.google.com/maps/documentation /javascript/tutorial.
  8. Hayes, J. G., de Oliveira, R. P. R., Vaughan, S., and Egan, M. G. (2011). Simplified electric vehicle power train models and range estimation. In Vehicle Power and Propulsion Conference (VPPC), 2011 IEEE, pages 1- 5. IEEE.
  9. Ip, A., Fong, S., and Liu, E. (2010). Optimization for allocating bev recharging stations in urban areas by using hierarchical clustering. In Advanced Information Management and Service (IMS), 2010 6th International Conference on, pages 460-465. IEEE.
  10. Koyanagi, F., Uriu, Y., and Yokoyama, R. (2001). Possibility of fuel cell fast charger and its arrangement problem for the infrastructure of electric vehicles. In Power Tech Proceedings, 2001 IEEE Porto, volume 4, pages 6-pp. IEEE.
  11. Koyanagi, F. and Yokoyama, R. (2010). A priority order solution of ev recharger installation by domain division approach. In Universities Power Engineering Conference (UPEC), 2010 45th International, pages 1-8. IEEE.
  12. Lam, A., Leung, Y.-W., and Chu, X. (2013). Electric vehicle charging station placement. In Smart Grid Communications (SmartGridComm), 2013 IEEE International Conference on, pages 510-515. IEEE.
  13. Momtazpour, M., Butler, P., Hossain, M. S., Bozchalui, M. C., Ramakrishnan, N., and Sharma, R. (2012). Coordinated clustering algorithms to support charging infrastructure design for electric vehicles. In Proceedings of the ACM SIGKDD International Workshop on Urban Computing, pages 126-133. ACM.
  14. Prins, R., Hurlbrink, R., and Winslow, L. (2012). Electric vehicle energy usage modeling and measurement. International Journal of Modern Engineering, 13(1):5- 12.
  15. Sweda, T. and Klabjan, D. (2011). An agent-based decision support system for electric vehicle charging infrastructure deployment. In Vehicle Power and Propulsion Conference (VPPC), 2011 IEEE, pages 1-5. IEEE.
  16. Wang, Z., Liu, P., Cui, J., Xi, Y., and Zhang, L. (2013). Research on quantitative models of electric vehicle charging stations based on principle of energy equivalence. Mathematical Problems in Engineering, 2013.
  17. Xi, X., Sioshansi, R., and Marano, V. (2013). Simulationoptimization model for location of a public electric vehicle charging infrastructure. Transportation Research Part D: Transport and Environment, 22:60-69.
Download


Paper Citation


in Harvard Style

Yi Z. and H. Bauer P. (2014). Energy Consumption Model and Charging Station Placement for Electric Vehicles . In Proceedings of the 3rd International Conference on Smart Grids and Green IT Systems - Volume 1: SMARTGREENS, ISBN 978-989-758-025-3, pages 150-156. DOI: 10.5220/0004859601500156


in Bibtex Style

@conference{smartgreens14,
author={Zonggen Yi and Peter H. Bauer},
title={Energy Consumption Model and Charging Station Placement for Electric Vehicles},
booktitle={Proceedings of the 3rd International Conference on Smart Grids and Green IT Systems - Volume 1: SMARTGREENS,},
year={2014},
pages={150-156},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004859601500156},
isbn={978-989-758-025-3},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 3rd International Conference on Smart Grids and Green IT Systems - Volume 1: SMARTGREENS,
TI - Energy Consumption Model and Charging Station Placement for Electric Vehicles
SN - 978-989-758-025-3
AU - Yi Z.
AU - H. Bauer P.
PY - 2014
SP - 150
EP - 156
DO - 10.5220/0004859601500156