Optimizing wireless charging locations for battery electric bus transit with a genetic algorithm

Gang Chen, Dawei Hu, Steven Chien, Lei Guo, Mingzheng Liu

Research output: Contribution to journalArticlepeer-review

18 Scopus citations


Electrifying bus transit has been deemed as an effective way to reduce the emissions of transit vehicles. However, some concerns about on-board battery hinder its further development. Recently, dynamic wireless power transfer (DWPT) technologies have been developed, which enable buses to charge in-motion and overcome the drawback (short service range) with opportunity charging. This paper proposes a mathematic model which optimizes the locations for DWPT devices deployed at stops and size of battery capacity for battery electric buses (BEB) in a multi-route network, which considers the battery’s service life, depth of discharge and weight. A tangible solution algorithm based on a genetic algorithm (GA) is developed to find the optimal solution. A case study based on the bus network from Xi’an China is conducted to investigate the relationship among optimized costs, greenhouse gas (GHG) emissions, battery service life, size of the battery capacity and the number of DWPT devices. The results demonstrated that a bus network powered by DWPT shows better performance in both costs (a 43.3% reduction) and emissions (a 14.4% reduction) compared to that with stationary charging at bus terminals.

Original languageEnglish (US)
Article number8971
Pages (from-to)1-20
Number of pages20
JournalSustainability (Switzerland)
Issue number21
StatePublished - Nov 1 2020

All Science Journal Classification (ASJC) codes

  • Computer Science (miscellaneous)
  • Environmental Science (miscellaneous)
  • Geography, Planning and Development
  • Energy Engineering and Power Technology
  • Hardware and Architecture
  • Management, Monitoring, Policy and Law
  • Computer Networks and Communications
  • Renewable Energy, Sustainability and the Environment


  • Dynamic wireless power transfer
  • Electric bus
  • Genetic algorithm
  • Transportation network planning


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