Dynamic Pricing for Electric Vehicle Extreme Fast Charging

Cheng Fang, Haibing Lu, Yuan Hong, Shan Liu, Jasmine Chang

Research output: Contribution to journalArticlepeer-review

43 Scopus citations

Abstract

Significant developments and advancement pertaining to electric vehicle (EV) technologies, such as extreme fast charging (XFC), have been witnessed in the last decade. However, there are still many challenges to the wider deployment of EVs. One of the major barriers is its availability of fast charging stations. A possible solution is to build a fast charging sharing system, by encouraging small business owners or even householders to install and share their fast charging devices, by reselling electricity energy sourced from traditional utility companies or their own solar grid. To incentivize such a system, a smart dynamic pricing scheme is needed to facilitate those growing markets with fast charging stations. The pricing scheme is expected to take into account the dynamics intertwined with pricing, demand, and environment factors, in an effort to maximize the long-term profit with the optimal price. To this end, this paper formulates the problem of dynamic pricing for fast charging as a Markov decision process and accordingly proposes several algorithmic schemes for different applications. Experimental study is conducted with useful and interesting insights.

Original languageEnglish (US)
Article number9057557
Pages (from-to)531-541
Number of pages11
JournalIEEE Transactions on Intelligent Transportation Systems
Volume22
Issue number1
DOIs
StatePublished - Jan 2021

All Science Journal Classification (ASJC) codes

  • Automotive Engineering
  • Mechanical Engineering
  • Computer Science Applications

Keywords

  • Fast charging
  • XFC
  • dynamic pricing
  • reinforcement learning
  • renewable energy

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