Optimal Electrical Vehicle Charging Planning and Routing Using Real-Time Trained Energy Prediction With Physics-Based Powertrain Model

Han, D; Zamee, MA; Choi, G; Kim, T; Won, D

Optimal Electrical Vehicle Charging Planning and Routing Using Real-Time Trained Energy Prediction With Physics-Based Powertrain Model

Han, D Zamee, MA Choi, G Kim, T Won, D

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Publisher:: IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Publication Type:: Journal Article
Citation:: IEEE Access, 2024, 12, (99), pp. 123250-123266
Issue Date:: 2024-01-01

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Field	Value	Language
dc.contributor.author	Han, D
dc.contributor.author	Zamee, MA
dc.contributor.author	Choi, G
dc.contributor.author	Kim, T
dc.contributor.author	Won, D
dc.date.accessioned	2024-10-16T09:13:50Z
dc.date.available	2024-10-16T09:13:50Z
dc.date.issued	2024-01-01
dc.identifier.citation	IEEE Access, 2024, 12, (99), pp. 123250-123266
dc.identifier.issn	2169-3536
dc.identifier.issn	2169-3536
dc.identifier.uri	http://hdl.handle.net/10453/181426
dc.description.abstract	Electric vehicles (EVs) are rising in popularity due to technological developments and the nation's decarbonization efforts, which are enticing drivers to move away from gasoline-dependent transportation. However, a major challenge in EV charging is optimal day-ahead charging planning under time-varying conditions, such as fluctuating charging prices and traffic conditions. Additionally, these conditions can lead to uncertain energy consumption while driving an EV. Predicting the energy consumption of an electric vehicle is complicated because it is influenced by more factors than a typical battery, such as driving habits and traffic conditions. This paper proposes an optimal EV charging planning strategy using an artificial intelligence-based energy consumption prediction model to address this problem. The energy consumption prediction model, based on long short-term memory (LSTM), was developed and validated against other artificial intelligence models. The LSTM-based model uses real-time training, making it suitable for uncertain scenarios encountered by EVs. The predicted results and traffic information are considered as weight factors in solving the EV charging planning and routing optimization problem. The optimization problem is formulated as mixed integer linear programming. Simulation studies under various driving and traffic conditions validate the proposed optimal charging planning and routing method. Since travel time can vary according to driving characteristics and traffic conditions over the same driving distance, the results confirm that the proposed method can predict energy consumption more accurately than other methods. It also selects routes with lower energy consumption relative to total driving time and provides stable operation.
dc.language	English
dc.publisher	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
dc.relation.ispartof	IEEE Access
dc.relation.isbasedon	10.1109/ACCESS.2024.3438993
dc.rights	info:eu-repo/semantics/openAccess
dc.subject	08 Information and Computing Sciences, 09 Engineering, 10 Technology
dc.subject.classification	40 Engineering
dc.subject.classification	46 Information and computing sciences
dc.title	Optimal Electrical Vehicle Charging Planning and Routing Using Real-Time Trained Energy Prediction With Physics-Based Powertrain Model
dc.type	Journal Article
utslib.citation.volume	12
utslib.for	08 Information and Computing Sciences
utslib.for	09 Engineering
utslib.for	10 Technology
pubs.organisational-group	University of Technology Sydney
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology/School of Electrical and Data Engineering
utslib.copyright.status	open_access	*
dc.rights.license	This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC BY-NC-ND 4.0). To view a copy of this license, visit https://creativecommons.org/licenses/by-nc-nd/4.0/
dc.date.updated	2024-10-16T09:13:48Z
pubs.issue	99
pubs.publication-status	Published
pubs.volume	12
utslib.citation.issue	99

Abstract:

Electric vehicles (EVs) are rising in popularity due to technological developments and the nation's decarbonization efforts, which are enticing drivers to move away from gasoline-dependent transportation. However, a major challenge in EV charging is optimal day-ahead charging planning under time-varying conditions, such as fluctuating charging prices and traffic conditions. Additionally, these conditions can lead to uncertain energy consumption while driving an EV. Predicting the energy consumption of an electric vehicle is complicated because it is influenced by more factors than a typical battery, such as driving habits and traffic conditions. This paper proposes an optimal EV charging planning strategy using an artificial intelligence-based energy consumption prediction model to address this problem. The energy consumption prediction model, based on long short-term memory (LSTM), was developed and validated against other artificial intelligence models. The LSTM-based model uses real-time training, making it suitable for uncertain scenarios encountered by EVs. The predicted results and traffic information are considered as weight factors in solving the EV charging planning and routing optimization problem. The optimization problem is formulated as mixed integer linear programming. Simulation studies under various driving and traffic conditions validate the proposed optimal charging planning and routing method. Since travel time can vary according to driving characteristics and traffic conditions over the same driving distance, the results confirm that the proposed method can predict energy consumption more accurately than other methods. It also selects routes with lower energy consumption relative to total driving time and provides stable operation.

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http://hdl.handle.net/10453/181426