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Article

Optimal Planning of Charging for Plug-In Electric Vehicles Focusing on Users’ Benefits

by 1, 1,* and 2
1
National Active Distribution Network Technology Research Center, Beijing Jiaotong University, Beijing 100044, China
2
Department of Electrical and Computer Engineering, University of Denver, Denver, CO 80210, USA
*
Author to whom correspondence should be addressed.
Energies 2017, 10(7), 952; https://doi.org/10.3390/en10070952
Received: 15 June 2017 / Revised: 2 July 2017 / Accepted: 3 July 2017 / Published: 9 July 2017
Many electric vehicles’ (EVs) charging strategies were proposed to optimize the operations of the power grid, while few focus on users’ benefits from the viewpoint of EV users. However, low participation is always a problem of those strategies since EV users also need a charging strategy to serve their needs and interests. This paper proposes a method focusing on EV users’ benefits that reduce the cost of battery capacity degradation, electricity cost, and waiting time for different situations. A cost model of battery capacity degradation under different state of charge (SOC) ranges is developed based on experimental data to estimate the cost of battery degradation. The simulation results show that the appropriate planning of the SOC range reduces 80% of the cost of battery degradation, and the queuing theory also reduces over 60% of the waiting time in the busy situations. Those works can also become a premise of charging management to increase the participation. The proposed strategy focusing on EV users’ benefits would not give negative impacts on the power grid, and the grid load is also optimized by an artificial fish swarm algorithm (AFSA) in the solution space of the charging time restricted by EV users’ benefits. View Full-Text
Keywords: electric vehicle; cost model of battery degradation; charging management; optimal scheduling; load control; Monte Carlo electric vehicle; cost model of battery degradation; charging management; optimal scheduling; load control; Monte Carlo
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MDPI and ACS Style

Su, S.; Li, H.; Gao, D.W. Optimal Planning of Charging for Plug-In Electric Vehicles Focusing on Users’ Benefits. Energies 2017, 10, 952. https://doi.org/10.3390/en10070952

AMA Style

Su S, Li H, Gao DW. Optimal Planning of Charging for Plug-In Electric Vehicles Focusing on Users’ Benefits. Energies. 2017; 10(7):952. https://doi.org/10.3390/en10070952

Chicago/Turabian Style

Su, Su, Hao Li, and David W. Gao 2017. "Optimal Planning of Charging for Plug-In Electric Vehicles Focusing on Users’ Benefits" Energies 10, no. 7: 952. https://doi.org/10.3390/en10070952

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