Battery Protective Electric Vehicle Charging Management in Renewable Energy System

Shuangqi Li, Pengfei Zhao, Chenghong Gu, Jianwei Li, Shuang Cheng, Minghao Xu

Research output: Contribution to journalArticlepeer-review

21 Citations (SciVal)
311 Downloads (Pure)


The adoption of grid-connected electric vehicles (GEVs) brings a bright prospect for promoting renewable energy. An efficient vehicle-to-grid (V2G) scheduling scheme that can deal with renewable energy volatility and protect vehicle batteries from fast aging is indispensable to enable this benefit. This article develops a novel V2G scheduling method for consuming local renewable energy in microgrids by using a mixed learning framework. It is the first attempt to integrate battery protective targets in GEVs charging management in renewable energy systems. Battery safeguard strategies are derived via an offline soft-run scheduling process, where V2G management is modeled as a constrained optimization problem based on estimated microgrid and GEVs states. Meanwhile, an online V2G regulator is built to facilitate the real-time scheduling of GEVs' charging. The extreme learning machine (ELM) algorithm is used to train the established online regulator by learning rules from soft-run strategies. The online charging coordination of GEVs is realized by the ELM regulator based on real-time sampled microgrid frequency. The effectiveness of the developed models is verified on a U.K. microgrid with actual energy generation and consumption data. This article can effectively enable V2G to promote local renewable energy with battery aging mitigated, thus economically benefiting EV owns and microgrid operators, and facilitating decarbonization at low costs.

Original languageEnglish
Pages (from-to)1312-1321
Number of pages10
JournalIEEE Transactions on Industrial Informatics
Issue number2
Early online date20 Jun 2022
Publication statusPublished - 1 Feb 2023


  • Artificial intelligence
  • battery aging mitigation
  • electric vehicle
  • microgrid
  • renewable energy
  • vehicle to grid

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Information Systems
  • Computer Science Applications
  • Electrical and Electronic Engineering


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