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Deep Learning-Based Energy Management of an All-electric City Bus with Wireless Power Transfer

Publikation: Bidrag til tidsskrift/Konferencebidrag i tidsskrift /Bidrag til avis › Tidsskriftartikel › Forskning › peer review

Standard

Deep Learning-Based Energy Management of an All-electric City Bus with Wireless Power Transfer. / Rafiei Foroushani, Mehdi; Griffiths, Matthew Peter; Boudjadar, Jalil et al.
I: IEEE Access, Bind 9, 9380640, 2021, s. 43981-43990.

Publikation: Bidrag til tidsskrift/Konferencebidrag i tidsskrift /Bidrag til avis › Tidsskriftartikel › Forskning › peer review

Harvard

Rafiei Foroushani, M, Griffiths, MP, Boudjadar, J & Khooban, MH 2021, 'Deep Learning-Based Energy Management of an All-electric City Bus with Wireless Power Transfer', IEEE Access, bind 9, 9380640, s. 43981-43990. https://doi.org/10.1109/ACCESS.2021.3066300

APA

Rafiei Foroushani, M., Griffiths, M. P., Boudjadar, J., & Khooban, M. H. (2021). Deep Learning-Based Energy Management of an All-electric City Bus with Wireless Power Transfer. IEEE Access, 9, 43981-43990. artikel 9380640. https://doi.org/10.1109/ACCESS.2021.3066300

CBE

Rafiei Foroushani M, Griffiths MP, Boudjadar J, Khooban MH. 2021. Deep Learning-Based Energy Management of an All-electric City Bus with Wireless Power Transfer. IEEE Access. 9:43981-43990. https://doi.org/10.1109/ACCESS.2021.3066300

MLA

Rafiei Foroushani, Mehdi et al. "Deep Learning-Based Energy Management of an All-electric City Bus with Wireless Power Transfer". IEEE Access. 2021, 9. 43981-43990. https://doi.org/10.1109/ACCESS.2021.3066300

Vancouver

Rafiei Foroushani M, Griffiths MP, Boudjadar J, Khooban MH. Deep Learning-Based Energy Management of an All-electric City Bus with Wireless Power Transfer. IEEE Access. 2021;9:43981-43990. 9380640. doi: 10.1109/ACCESS.2021.3066300

Author

Rafiei Foroushani, Mehdi ; Griffiths, Matthew Peter ; Boudjadar, Jalil et al. / Deep Learning-Based Energy Management of an All-electric City Bus with Wireless Power Transfer. I: IEEE Access. 2021 ; Bind 9. s. 43981-43990.

Bibtex

@article{9138324a11644d4b8b7ffcfe3c311e27,
title = "Deep Learning-Based Energy Management of an All-electric City Bus with Wireless Power Transfer",
abstract = "Fuel cell-based hybrid electric vehicles are one of the most promising options to achieve zero-emission city buses. Efficient Energy Management (EM) plays a critical role to make such buses more efficient and practical. In this research, an available all-electric bus consisting of fuel cell (FC) and battery is considered and the efficiency of adding a Wireless Power Transfer (WPT) system to it is assessed. The proposed WPT system is only capable to receive energy in bus stations and use it to supply loads or charge the battery. To this end, the actual data of a city bus, its route and load profile were collected and utilized to ensure a realistic assessment. A full mathematical model of the energy system as well as the constraints governing the management issue is extracted and a Deep Deterministic Policy Gradient (DDPG) method is used to optimally manage the energy flows for the entire journey. All models are implemented in MATLAB software and the efficiency of the proposed system is investigated from economic and technical aspects. The results illustrate a high efficiency for the proposed WPT technique to be used in actual all-electric city buses.",
keywords = "Hybrid energy system, and all-electric city bus, deep learning, energy management, wireless power transfer",
author = "{Rafiei Foroushani}, Mehdi and Griffiths, {Matthew Peter} and Jalil Boudjadar and Khooban, {Mohammad Hassan}",
year = "2021",
doi = "10.1109/ACCESS.2021.3066300",
language = "English",
volume = "9",
pages = "43981--43990",
journal = "IEEE Access",
issn = "2169-3536",
publisher = "Institute of Electrical and Electronics Engineers",

}

RIS

TY - JOUR

T1 - Deep Learning-Based Energy Management of an All-electric City Bus with Wireless Power Transfer

AU - Rafiei Foroushani, Mehdi

AU - Griffiths, Matthew Peter

AU - Boudjadar, Jalil

AU - Khooban, Mohammad Hassan

PY - 2021

Y1 - 2021

N2 - Fuel cell-based hybrid electric vehicles are one of the most promising options to achieve zero-emission city buses. Efficient Energy Management (EM) plays a critical role to make such buses more efficient and practical. In this research, an available all-electric bus consisting of fuel cell (FC) and battery is considered and the efficiency of adding a Wireless Power Transfer (WPT) system to it is assessed. The proposed WPT system is only capable to receive energy in bus stations and use it to supply loads or charge the battery. To this end, the actual data of a city bus, its route and load profile were collected and utilized to ensure a realistic assessment. A full mathematical model of the energy system as well as the constraints governing the management issue is extracted and a Deep Deterministic Policy Gradient (DDPG) method is used to optimally manage the energy flows for the entire journey. All models are implemented in MATLAB software and the efficiency of the proposed system is investigated from economic and technical aspects. The results illustrate a high efficiency for the proposed WPT technique to be used in actual all-electric city buses.

AB - Fuel cell-based hybrid electric vehicles are one of the most promising options to achieve zero-emission city buses. Efficient Energy Management (EM) plays a critical role to make such buses more efficient and practical. In this research, an available all-electric bus consisting of fuel cell (FC) and battery is considered and the efficiency of adding a Wireless Power Transfer (WPT) system to it is assessed. The proposed WPT system is only capable to receive energy in bus stations and use it to supply loads or charge the battery. To this end, the actual data of a city bus, its route and load profile were collected and utilized to ensure a realistic assessment. A full mathematical model of the energy system as well as the constraints governing the management issue is extracted and a Deep Deterministic Policy Gradient (DDPG) method is used to optimally manage the energy flows for the entire journey. All models are implemented in MATLAB software and the efficiency of the proposed system is investigated from economic and technical aspects. The results illustrate a high efficiency for the proposed WPT technique to be used in actual all-electric city buses.

KW - Hybrid energy system

KW - and all-electric city bus

KW - deep learning

KW - energy management

KW - wireless power transfer

U2 - 10.1109/ACCESS.2021.3066300

DO - 10.1109/ACCESS.2021.3066300

M3 - Journal article

VL - 9

SP - 43981

EP - 43990

JO - IEEE Access

JF - IEEE Access

SN - 2169-3536

M1 - 9380640

ER -