ELECTRIC VEHICLE CHARGER MANAGEMENT SYSTEM FOR INTEROPERABLE CHARGING FACILITIES
DOI:
https://doi.org/10.11113/jt.v78.8776Keywords:
Electric vehicle, charging facility management, total operation center, big data processing, interoperabilityAbstract
This paper designs and develops a real-time charger management system which keeps collecting the status information from chargers and converts control messages from the total operation center to a command predefined for charger control. RF cards complete the service chain from electric vehicles to the operation center, making the information flow bidirectional. The extended coverage of the operation control over the charging infrastructure allows an easy payment for the charging fee, based on the membership management and personalized services. A web application is implemented on the digital map of the target city for users to retrieve necessary information from the system and find the best service and chargers. The massive amount of real-time charger monitoring data is being accumulated in the database, and big data analysis will allow us to make intelligent plans for future smart grid city services.Â
References
Balac, N. 2013. Green Machine, Intelligence: Greening And Sustaining Smart Grids. IEEE Intelligent Systems. 50-55.
Rahimi-Eichi, H. and Chow, M. 2014. Big-Data Framework For Electric Vehicle Range Estimation. 40th Annual Conference of the IEEE Industrial Electronics Society. 5628 - 5634.
Ai, M. and Qi, L. 2014. Orderly Charging Based Electric Vehicle Charging Stations’ On-Line Monitoring System. CIRED Workshop.
Park, C. Lee, J., Park, G. and Hyun, J. 2014. Development Of Reservation Recommendation Algorithms For Charging Electric Vehicles In Smart-Grid Cities. International Journal of Smart Home. 8(1): 113-122.
Bhattarai, P., Levesque, M., Maier, M., Bak-Jensen, B. and Radhakrishna, J. 2015. Optimizing Electric Vehicle Coordination Over A Heterogeneous Mesh Network In A Scaled-Down Smart Grid Testbed. IEEE Transactions on Smart Grid. 6(2): 784 – 794.
Qiu, G., Wang, K., Li, S., Dong, J. and Xie, M. 2014. Big Data Technologies In Support Of Real Time Capturing And Understanding Of Electric Vehicle Customers Dynamics. 5th IEEE International Conference on Software Engineering and Service Science. 263 - 267.
Qiu, C., Chung, C. and Chu, C. 2013. Fast Demand Forecast Of Electric Vehicle Charging Stations For Cell Phone Applications. IEEE PES General Meeting Conference & Exposition.
Alahaoon, D. and Yu, X. 2013. Advanced Analytics For Harnessing The Power Of Smart Meter Big Data. IEEE International Workshop on Intelligent Energy Systems. 40-45.
Aji, A., Wang, F., Vo, H., Lee, R., Liu, Q., Zhang, X. and Saltz, J. 2012. Hadoop-GIS: A High Performance Spatial Data Warehousing System Over MapReduce. International Conference on Very Large Data Bases. 1009-1020.
Nissen, S. 2005. Neural Network Made Simple, Software 2.0.
Lee, J., Park, G. and Park, C. 2015. Charging Facility Monitoring Stream Analysis Based On Hadoop For Smart Grids. International Journal of Software Engineering and Its Applications. 9(11): 111-117.
Simmhan, Y., Aman, A., Kumbhare, A., Rongyang, R., Stevens, S., Qunzhi, Z., and Prasanna, V. 2013. Cloud-Based Software Platform For Big Data Analytics In Smart Grids. Computing in Science & Engineering. 15(4): 38-47.
Lee, J., Park, G., Cho, Y., Kim, S. and Jung, J. 2015. Spatio-Temporal Analysis Of State-Of-Charge Streams For Electric Vehicles. 14th ACM/IEEE International Conference on Information Processing in Sensor Networks. 368-369.
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