With the growing application of electric vehicles (EVs), it is of great significance to have a deep understanding of EV users driving and charging patterns for charging forecasting. However, the rapid growth scale of EV taxis with charging patterns that are closely coupled with human behaviours of temporal–spatial charging choices was not compatible with most previous coordinated strategies. Unlike the majority of existing approaches, a large volume of second-level EV global positioning system (GPS) data was used to study the behaviour patterns of EV users. In practise, a mobile crowdsensing system that records GPS data and transmits information to the server was deployed in a fleet of electric-taxi cabs in Shenzhen, China, making it possible to record the exact behaviour of each vehicle. Travelling and charging statuses of EVs were recorded and analysed into different characteristics of behaviour for each user. The load forecast methods proved to be more effective with more knowledge of both history and real-time data.

References

1. 1)
  - 23. Korolko, N., Sahinoglu, Z., Nikovski, D.: ‘Modeling and forecasting self-similar power load due to EV fast chargers’. IEEE Trans. Smart Grid., 2016, 7, pp. 1620–1629.
2. 2)
  - 6. Li, Z., Guo, Q., Sun, H., et al: ‘A new real-time smart-charging method considering expected electric vehicle fleet connections’, IEEE Trans. Power Syst., 2014, 29, pp. 3114–3115.
3. 3)
  - 12. Rolink, J., Rehtanz, C.: ‘Large-scale modeling of grid-connected electric vehicles’, IEEE Trans. Power Deliv., 2013, 28, pp. 894–902.
4. 4)
  - 21. Ashtari, A., Bibeau, E., Shahidinejad, S., et al: ‘PEV charging profile prediction and analysis based on vehicle usage data’, IEEE Trans. Smart Grid., 2012, 3, pp. 341–350.
5. 5)
  - 33. ‘LBS, Baidu Map Cloud API’, http://developer.baidu.com/map/lbs-cloud.htm, accessed 1 March 2016.
6. 6)
  - 25. Tian, Z., Jung, T., Wang, Y., et al: ‘Real-time charging station recommendation system for electric-vehicle taxis’. IEEE Trans. Intell. Transp., 2016, 17, pp. 3098–3109.
7. 7)
  - 36. ‘Vehicle profile of BYD E6’. Available at http://www.bydauto.com.cn/carparam-e6.html/, accessed 10 October 2016.
8. 8)
  - 7. Binetti, G., Davoudi, A., Naso, D., et al: ‘Scalable real-time electric vehicles charging with discrete charging rates’, IEEE Trans. Smart Grid, 2015, 6, pp. 2211–2220.
9. 9)
  - 32. Ganti, R. K., Ye, F., Lei, H.: ‘Mobile crowdsensing: current state and future challenges’. IEEE Communications Magazine, 2011, 49, pp. 32–39.
10. 10)
  - 34. ‘Open Street Map’, http://www.openstreetmap.org/, accessed 1 March 2016.
11. 11)
  - 11. Sundstrom, O., Corradi, O., Binding, C.: ‘Toward electric vehicle trip prediction for a charging service provider’. IEEE Int. Electric Vehicle Conf. (IEVC), 2012, pp. 1–6.
12. 12)
  - 35. ‘Test on BYD E6 electric taxis under Shenzhen traffic conditions’, http://www.autohome.com/drive/201407/822634-all.html, accessed 1 March 2016.
13. 13)
  - 2. Lund, H., Kempton, W.: ‘Integration of renewable energy into the transport and electricity sectors through V2G’, Energy Policy, 2008, 9, pp. 3578–3587.
14. 14)
  - 28. Guo, Q., Xin, S., Sun, H., Li, , Z., Zhang, B.: ‘Rapid-charging navigation of electric vehicles based on real-time power systems and traffic data’. IEEE Trans. Smart Grid., 2014, 5, pp. 1969–1979.
15. 15)
  - 16. Majidpour, M., Qiu, C., Chu, P., et al: ‘Forecasting the EV charging load based on customer profile or station measurement?’, Appl. Energy, 2016, 163, pp. 134–141.
16. 16)
  - 14. Xydas, S., Marmaras, C.E., Cipcigan, L.M., et al: ‘Electric vehicle load forecasting using data mining methods’. IET Hybrid and Electric Vehicles Conf. (HEVC), 2013, pp. 1–6.
17. 17)
  - 10. Kuran, M., Viana, A., Iannone, L., et al: ‘A smart parking lot management system for scheduling the recharging of electric vehicles’, IEEE Trans. Smart Grid, 2015, 6, pp. 2942–2953.
18. 18)
  - 30. Karbassi, A., Barth, M.: ‘Vehicle route prediction and time of arrival estimation techniques for improved transportation system management’. IEEE Intelligent Vehicles Symposium, 2013, pp. 511–516.
19. 19)
  - 4. Wu, D., Aliprantis, D., Ying, L.: ‘On the choice between uncontrolled and controlled charging by owners of PHEVs’, IEEE Trans. Power Deliv., 2011, 26, pp. 2882–2884.
20. 20)
  - 18. Bessa, R., Matos, M.: ‘Global against divided optimization for the participation of an EV aggregator in the day-ahead electricity market. Part II: numerical analysis’, Electr. Power Syst. Res., 2013, 95, pp. 319–329.
21. 21)
  - 19. Hubner, M., Zhao, L., Mirbach, T., et al: ‘Impact of large-scale electric vehicle application on the power supply’. IEEE Electrical Power Energy Conf. (EPEC), 2009, pp. 1–6.
22. 22)
  - 1. Li, Z., Guo, Q., Sun, H., et al: ‘Emission-concerned wind-EV coordination on the transmission grid side with network constraints: concept and case study’, IEEE Trans. Smart Grid, 2013, 4, pp. 1692–1704.
23. 23)
  - 29. Necula, E.: ‘Mining GPS data to learn driver's route patterns’. IEEE International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC), 2014, pp. 264–271.
24. 24)
  - 31. Xu, X., Zhang, P., Zhang, L.: ‘Gotcha: a mobile urban sensing system’, 12th ACM Conference on Embedded Network Sensor Systems, 2014, Available at: http://doi.acm.org/10.1145/2668332.2668374.
25. 25)
  - 20. Alizadeh, M., Scaglione, A., Davies, J., et al: ‘A scalable stochastic model for the electricity demand of electric and plug-in hybrid vehicles’, IEEE Trans. Smart Grid., 2014, 5, pp. 848–860.
26. 26)
  - 13. Aabrandt, A., Andersen, P.B., Pedersen, A.B., et al: ‘Prediction and optimization methods for electric vehicle charging schedules in the EDISON project’, IEEE PES Innov. Smart Grid Technol. (ISGT), Washington, D.C., USA, January 2012, pp. 1–7.
27. 27)
  - 9. Yazdani-Damavandi, M., Moghaddam, M., Haghifam, M.-R., et al: ‘Modeling operational behavior of plug-in electric vehicles’ parking lot in multienergy systems’, IEEE Trans. Smart Grid, 2016, 7, pp. 124–135.
28. 28)
  - 15. Arias, M.B., Bae, S.: ‘Electric vehicle charging demand forecasting model based on big data technologies’, Appl. Energy, 2016, 183, pp. 327–339.
29. 29)
  - 5. Sortomme, E., Hindi, M., MacPherson, S., et al: ‘Coordinated charging of plug-in hybrid electric vehicles to minimize distribution system losses’, IEEE Trans. Smart Grid, 2011, 2, pp. 198–205.
30. 30)
  - 8. He, Y., Venkatesh, B., Guan, L.: ‘Optimal scheduling for charging and discharging of electric vehicles’, IEEE Trans. Smart Grid, 2012, 3, pp. 1095–1105.
31. 31)
  - 22. Majidpour, M., Qiu, C., Chu, P., et al: ‘Fast prediction for sparse time series: demand forecast of EV charging stations for cell phone applications’, IEEE Trans. Indust. Inform., 2015, 11, pp. 242–250.
32. 32)
  - 24. Tian, Z., Wang, Y., Tian, C., et al: ‘Understanding operational and charging patterns of electric vehicle taxis using GPS records’, IEEE Int. Conf. on Intelligent Transportation Systems (ITSC), 2014, pp. 2472–2479.
33. 33)
  - 27. Gang, X., Fenghua, Z., Xiwei, L., et al: ‘Cyber-physical-social system in intelligent transportation’, IEEE/CAA J. Autom. Sin., 2015, 2, pp. 320–333.
34. 34)
  - 17. Qian, K., Zhou, C., Allan, M., et al: ‘Modeling of load demand due to EV battery charging in distribution systems’, IEEE Trans. Power Syst., 2011, 26, pp. 802–810.
35. 35)
  - 3. Masoum, A., Deilami, S., Moses, P., et al: ‘Smart load management of plug-in electric vehicles in distribution and residential networks with charging stations for peak shaving and loss minimisation considering voltage regulation’, IET Gener. Transm. Distrib., 2011, 5, pp. 877–888.
36. 36)
  - 26. Cai, Y., Wang, H., Ye, Q., et al: ‘Analysis of two typical EV business models based on EV taxi demonstrations in China’, IEEE Electric Vehicle Symp. and Exhibition, 2013, pp. 1–6.

EV charging behaviour analysis and modelling based on mobile crowdsensing data

References

Related content