Online Battery Protective Energy Management for Energy-Transportation Nexus

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

Research output: Contribution to journalArticlepeer-review

15 Citations (SciVal)
54 Downloads (Pure)


Grid-connected electric vehicles (GEVs) and energy-transportation nexus bring a bright prospect to improve the penetration of renewable energy and the economy of microgrids (MGs). However, it is challenging to determine optimal vehicle-to-grid (V2G) strategies due to the complex battery aging mechanism and volatile MG states. This article develops a novel online battery anti-aging energy management method for energy-transportation nexus by using a novel deep reinforcement learning (DRL) framework. Based on battery aging characteristic analysis and rain-flow cycle counting technology, the quantification of aging cost in V2G strategies is realized by modeling the impact of number of cycles, depth of discharge, and charge and discharge rate. The established life loss model is used to evaluate battery anti-aging effectiveness of agent actions. The coordination of GEVs charging is modeled as multiobjective learning by using a DRL algorithm. The training objective is to maximize renewable penetration while reducing MG power fluctuations and vehicle battery aging costs. The developed energy-transportation nexus energy management method is verified to be effective in optimal power balancing and battery anti-aging control on a MG in the U.K. This article provides an efficient and economical tool for MG power balancing by optimally coordinating GEVs charging and renewable energy, thus helping promote a low-cost decarbonization transition.

Original languageEnglish
Article number9745763
Pages (from-to)8203-8212
Number of pages10
JournalIEEE Transactions on Industrial Informatics
Issue number11
Early online date31 Mar 2022
Publication statusPublished - 1 Nov 2022


  • Battery aging mitigation
  • deep reinforcement learning (DRL)
  • electric vehicle
  • microgrid (MG)
  • renewable energy
  • transportation electrification
  • vehicle-to-grid (V2G)

ASJC Scopus subject areas

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


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