Next Article in Journal
Switched Control Strategies of Aggregated Commercial HVAC Systems for Demand Response in Smart Grids
Next Article in Special Issue
Multi-Objective Optimization Considering Battery Degradation for a Multi-Mode Power-Split Electric Vehicle
Previous Article in Journal
A Study on the Matching Relationship of Polymer Molecular Weight and Reservoir Permeability in ASP Flooding for Duanxi Reservoirs in Daqing Oil Field
Previous Article in Special Issue
Optimal Scheduling for Electric Vehicle Charging under Variable Maximum Charging Power
Article Menu
Issue 7 (July) cover image

Export Article

Open AccessArticle
Energies 2017, 10(7), 952; doi:10.3390/en10070952

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

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.
Received: 15 June 2017 / Revised: 2 July 2017 / Accepted: 3 July 2017 / Published: 9 July 2017
View Full-Text   |   Download PDF [3482 KB, uploaded 14 July 2017]   |  

Abstract

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
Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

Scifeed alert for new publications

Never miss any articles matching your research from any publisher
  • Get alerts for new papers matching your research
  • Find out the new papers from selected authors
  • Updated daily for 49'000+ journals and 6000+ publishers
  • Define your Scifeed now

SciFeed Share & Cite This Article

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.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Energies EISSN 1996-1073 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top