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

doi:10.1016/j.energy.2020.117374

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

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41 Citations (Scopus)

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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 language	English
Article number	117374
Journal	Energy
Volume	198
Early online date	12 Mar 2020
DOIs	https://doi.org/10.1016/j.energy.2020.117374
Publication status	Published - 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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10.1016/j.energy.2020.117374

Post-PrintAccepted author manuscript, 1.68 MBLicence: CC BY-NC-ND

Cite this

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title = "An improved vehicle to the grid method with battery longevity management in a microgrid application",

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.",

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AU - Yang, Qingqing

AU - Li, Jianwei

AU - Cao , Wanke

AU - Li, Shuangqi

AU - Lin, Jie

AU - Huo, Da

AU - He, Hongwen

N1 - 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.

PY - 2020/5/1

Y1 - 2020/5/1

N2 - 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.

AB - 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.

KW - Deep learning

KW - Electric vehicles

KW - Frequency control

KW - Microgrid

KW - Vehicle to the grid

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JF - Energy

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ER -

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

Abstract

Bibliographical note

Funder

Keywords

ASJC Scopus subject areas

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