Abstract
The burden on the consumer for the price of fuel for classic vehicles is the root cause for the emergence of the fast growing trend in the power driven vehicles or electric vehicles. Less acceptance of electric vehicles by the customers and the hesitancy to replace traditional fuel powered vehicles by considering the economic factor is a major concern that existing in the current scenario. Therefore, for the proper balancing of the load with respect to the power available among different neighbouring charging stations in a given area, a load scheduling algorithm is used. The optimal route planner for the electric vehicles reaching the charging station is identified and then the power carried by each feeder is calculated by cumulative power of all the charging stations. The identification of the possible route is performed by the spatial network analysis which will be executing at remote analyzer. The location, state of charge, and other details of the electric vehicle through telemetry is used to find the best charging station for the particular vehicle in view of the cost, distance and the time. The performance of the technique is evaluated with and without optimization by considering the logical constraints; and the results are presented.
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Acknowledgments
The authors would like to acknowledge and express the deepest gratitude to management of Faculty of Engineering, Christ (Deemed to be University) for providing freedom to work in the laboratory.
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Appendices
Appendices
A Glossary
B Remote Analyzer Application
Remote Analyzer application is developed using Matlab app designer. The charging station map, charging station details and electric vehicle dynamic details are given in Figs. 4, 5, 6 and 7 respectively.
Charging stations map
Electric vehicles map
Charging station details
Electric vehicle dynamic details
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Venkataswamy, R., Joseph, T.M. (2020). Optimal Charging Strategy for Spatially Distributed Electric Vehicles in Power System by Remote Analyser. In: Auer, M., Ram B., K. (eds) Cyber-physical Systems and Digital Twins. REV2019 2019. Lecture Notes in Networks and Systems, vol 80. Springer, Cham. https://doi.org/10.1007/978-3-030-23162-0_36
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DOI: https://doi.org/10.1007/978-3-030-23162-0_36
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