A stochastic method for prediction of the power demand at high rate EV chargers
A stochastic method for prediction of the power demand at high rate EV chargers
High rate (<100kW) electric vehicle chargers (HREVCs) are crucial for achieving the benefits of reduced CO2 and particulate emissions promised by electric vehicles by enabling journey distances greater than the range of the vehicle. A method for predicting the expected demand pattern at these HREVCs is presented in this paper. This is critical to planning a network of chargers. This novel method uses freely available traffic flow data and travel patterns extracted from the open street map combined with a novel EV battery capacity prediction method, to find future HREVC usage patterns in the UK and their dependence on location and EV characteristics. This planning method can be replicated to find HREVC power demand for any location on the strategic road network in the UK and can be used in analysis of the role of high rate EV charging in the wider energy system.
1-36
Hilton, George
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Kiaee, Mahdi
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Bryden, Thomas, Samuel
451e1fd4-25ab-4771-9e69-0598acf6d626
Dimitrov, Borislav
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Cruden, Andrew
ed709997-4402-49a7-9ad5-f4f3c62d29ab
Mortimer, Alan
6c7de6c9-6926-4830-a8eb-6cf38d28287a
Hilton, George
fd332562-ee82-4b62-b99c-0d0ee2e06ca1
Kiaee, Mahdi
1d965346-f270-4093-b4d8-6348c0f8ec95
Bryden, Thomas, Samuel
451e1fd4-25ab-4771-9e69-0598acf6d626
Dimitrov, Borislav
7a128e82-8621-4ffb-8390-77f7153d5d3a
Cruden, Andrew
ed709997-4402-49a7-9ad5-f4f3c62d29ab
Mortimer, Alan
6c7de6c9-6926-4830-a8eb-6cf38d28287a
Hilton, George, Kiaee, Mahdi, Bryden, Thomas, Samuel, Dimitrov, Borislav, Cruden, Andrew and Mortimer, Alan
(2018)
A stochastic method for prediction of the power demand at high rate EV chargers.
IEEE Transactions on Transportation Electrification, .
(doi:10.1109/TTE.2018.2831003).
Abstract
High rate (<100kW) electric vehicle chargers (HREVCs) are crucial for achieving the benefits of reduced CO2 and particulate emissions promised by electric vehicles by enabling journey distances greater than the range of the vehicle. A method for predicting the expected demand pattern at these HREVCs is presented in this paper. This is critical to planning a network of chargers. This novel method uses freely available traffic flow data and travel patterns extracted from the open street map combined with a novel EV battery capacity prediction method, to find future HREVC usage patterns in the UK and their dependence on location and EV characteristics. This planning method can be replicated to find HREVC power demand for any location on the strategic road network in the UK and can be used in analysis of the role of high rate EV charging in the wider energy system.
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Accepted/In Press date: 24 April 2018
e-pub ahead of print date: 27 April 2018
Identifiers
Local EPrints ID: 420342
URI: http://eprints.soton.ac.uk/id/eprint/420342
ISSN: 2332-7782
PURE UUID: 1c5e32b4-f9cc-472d-bfa9-c96981dc6515
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Date deposited: 04 May 2018 16:30
Last modified: 16 Mar 2024 06:33
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Contributors
Author:
George Hilton
Author:
Mahdi Kiaee
Author:
Thomas, Samuel Bryden
Author:
Borislav Dimitrov
Author:
Alan Mortimer
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