An improved vehicle to the grid method with battery longevity management in a microgrid application

Qingqing Yang, Jianwei Li, Wanke Cao , Shuangqi Li, Jie Lin, Da Huo, Hongwen He

Research output: Contribution to journalArticlepeer-review

5 Citations (Scopus)
5 Downloads (Pure)

Abstract

This paper proposed an improved vehicle-to-grid (V2G) scheduling approach for the frequency control with the advantage of protecting the batteries hence saving the battery lifetime during grid connected service. The proposed methodology is improved in two ways. Firstly, to give a prediction of the available electric vehicle (EV) battery capacity in the control time-step for the V2G service, a deep learning based prediction is developed. Secondly, this study advances the previous V2G method by adding the quantitative analysis of the battery cycle life into the V2G optimization. The accurate prediction of the schedulable battery capacity based on the LSTM algorithm is shown very effective in the power system frequency control. Also, compared with the previous method that without battery lifetime control, the proposed method benefits in the reduction of charge/discharge cycles.

Original languageEnglish
Article number117374
JournalEnergy
Volume198
Early online date12 Mar 2020
DOIs
Publication statusPublished - 1 May 2020

Bibliographical note

Funding Information:
This work was supported by the National Nature Science Foundation of China with Grant Number 51807008 and Grant Number U1864202 .

Publisher Copyright:
© 2020 Elsevier Ltd

Copyright:
Copyright 2020 Elsevier B.V., All rights reserved.

Funder

National Nature Science Foundation of China with Grant Number 51807008 and Grant Number U1864202

Keywords

  • Deep learning
  • Electric vehicles
  • Frequency control
  • Microgrid
  • Vehicle to the grid

ASJC Scopus subject areas

  • Civil and Structural Engineering
  • Building and Construction
  • Pollution
  • Mechanical Engineering
  • Industrial and Manufacturing Engineering
  • Electrical and Electronic Engineering

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