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

Qi Kang, Jiabao Wang, Mengchu Zhou, Ahmed Chiheb Ammari

Research output: Contribution to journalArticlepeer-review

252 Scopus citations


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.

Original languageEnglish (US)
Article number7330009
Pages (from-to)659-669
Number of pages11
JournalIEEE Transactions on Intelligent Transportation Systems
Issue number3
StatePublished - Mar 2016

All Science Journal Classification (ASJC) codes

  • Automotive Engineering
  • Mechanical Engineering
  • Computer Science Applications


  • Battery swap
  • centralized charging
  • electric vehicle
  • genetic algorithm
  • particle swarm optimization


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