Electric vehicle charge scheduling using an artificial neural network

Sayidul Morsalin; Khizir Mahmud; Graham Town

doi:10.1109/ISGT-Asia.2016.7796398

Electric vehicle charge scheduling using an artificial neural network

Sayidul Morsalin, Khizir Mahmud, Graham Town

School of Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference proceeding contribution › peer-review

45 Citations (Scopus)

Abstract

With the integration of EVs into the power grid, smart metering using machine-to-machine (M2M) communication is likely to play an important role in real-time energy management and control. Smart devices embedded with advanced metering infrastructure (AMI) can forecast the energy demand as well as perform energy pricing in real time. In this paper, an artificial neural network (ANN) based intelligent decision-making system is presented that utilises data logged by an M2M AMI for EV charge scheduling and load management. The ANN was trained using household power consumption and EV energy demand data, and was used to decide when a vehicle should charge (G2V), or could discharge (V2G).

Original language	English
Title of host publication	2016 IEEE Innovative Smart Grid Technologies - Asia, ISGT-Asia 2016
Place of Publication	Piscataway, NJ
Publisher	Institute of Electrical and Electronics Engineers (IEEE)
Pages	276-280
Number of pages	5
ISBN (Electronic)	9781509043033
DOIs	https://doi.org/10.1109/ISGT-Asia.2016.7796398
Publication status	Published - 22 Dec 2016
Event	2016 IEEE Innovative Smart Grid Technologies - Asia, ISGT-Asia 2016 - Melbourne, Australia Duration: 28 Nov 2016 → 1 Dec 2016

Other

Other	2016 IEEE Innovative Smart Grid Technologies - Asia, ISGT-Asia 2016
Country/Territory	Australia
City	Melbourne
Period	28/11/16 → 1/12/16

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10.1109/ISGT-Asia.2016.7796398

Cite this

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title = "Electric vehicle charge scheduling using an artificial neural network",

abstract = "With the integration of EVs into the power grid, smart metering using machine-to-machine (M2M) communication is likely to play an important role in real-time energy management and control. Smart devices embedded with advanced metering infrastructure (AMI) can forecast the energy demand as well as perform energy pricing in real time. In this paper, an artificial neural network (ANN) based intelligent decision-making system is presented that utilises data logged by an M2M AMI for EV charge scheduling and load management. The ANN was trained using household power consumption and EV energy demand data, and was used to decide when a vehicle should charge (G2V), or could discharge (V2G).",

author = "Sayidul Morsalin and Khizir Mahmud and Graham Town",

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Morsalin, S, Mahmud, K & Town, G 2016, Electric vehicle charge scheduling using an artificial neural network. in 2016 IEEE Innovative Smart Grid Technologies - Asia, ISGT-Asia 2016., 7796398, Institute of Electrical and Electronics Engineers (IEEE), Piscataway, NJ, pp. 276-280, 2016 IEEE Innovative Smart Grid Technologies - Asia, ISGT-Asia 2016, Melbourne, Australia, 28/11/16. https://doi.org/10.1109/ISGT-Asia.2016.7796398

Electric vehicle charge scheduling using an artificial neural network. / Morsalin, Sayidul; Mahmud, Khizir; Town, Graham.
2016 IEEE Innovative Smart Grid Technologies - Asia, ISGT-Asia 2016. Piscataway, NJ: Institute of Electrical and Electronics Engineers (IEEE), 2016. p. 276-280 7796398.

Research output: Chapter in Book/Report/Conference proceeding › Conference proceeding contribution › peer-review

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AB - With the integration of EVs into the power grid, smart metering using machine-to-machine (M2M) communication is likely to play an important role in real-time energy management and control. Smart devices embedded with advanced metering infrastructure (AMI) can forecast the energy demand as well as perform energy pricing in real time. In this paper, an artificial neural network (ANN) based intelligent decision-making system is presented that utilises data logged by an M2M AMI for EV charge scheduling and load management. The ANN was trained using household power consumption and EV energy demand data, and was used to decide when a vehicle should charge (G2V), or could discharge (V2G).

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Electric vehicle charge scheduling using an artificial neural network

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