Centralized Charging Strategy and Scheduling Algorithm for Electric Vehicles Under a Battery Swapping Scenario

Centralized charging of electric vehicles (EVs) based on battery swapping is a promising strategy for their large-scale utilization in power systems. The most outstanding feature of this strategy is that EV batteries can be replaced within a short time and can be charged during off-peak periods or o...

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Bibliographic Details
Published in:IEEE transactions on intelligent transportation systems Vol. 17; no. 3; pp. 659 - 669
Main Authors: Qi Kang, JiaBao Wang, MengChu Zhou, Ammari, Ahmed Chiheb
Format: Journal Article
Language:English
Published: New York IEEE 01.03.2016
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Algorithm design and analysis
Algorithms
Batteries
Battery swap
centralized charging
Charging
Dynamics
Electric batteries
Electric power systems
Electric utilities
electric vehicle
Electric vehicles
genetic algorithm
Genetic algorithms
Heuristic algorithms
particle swarm optimization
Power demand
Stations
Strategy
Vehicles
genetic algorithm
Battery swap
centralized charging
particle swarm optimization
electric vehicle
ISSN:1524-9050, 1558-0016
Online Access:Get full text
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Summary:Centralized charging of electric vehicles (EVs) based on battery swapping is a promising strategy for their large-scale utilization in power systems. The most outstanding feature of this strategy is that EV batteries can be replaced within a short time and can be charged during off-peak periods or on low electric price and scheduled in any battery swap station. This paper proposes a novel centralized charging strategy of EVs under the battery swapping scenario by considering optimal charging priority and charging location (station or bus node in a power system) based on spot electric price. In this strategy, a population-based heuristic approach is designed to minimize total charging cost, as well as to reduce power loss and voltage deviation of power networks. We introduce a dynamic crossover and adaptive mutation strategy into a hybrid algorithm of particle swarm optimization and genetic algorithm. The resulting algorithm and several others are executed on an IEEE 30-bus test system, and the results suggest that the proposed one is effective and promising for optimal EV centralized charging.
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ISSN:1524-9050
1558-0016
DOI:10.1109/TITS.2015.2487323