Intelligent and adaptive fleet energy management strategy for hybrid electric buses

Jon Ander Lopez-Ibarra; Nerea Goitia-Zabaleta; Haritza Camblong; Aitor Milo; Haizea Gaztanaga

doi:10.1109/VPPC46532.2019.8952368

Intelligent and adaptive fleet energy management strategy for hybrid electric buses

Jon Ander Lopez-Ibarra
, Nerea Goitia-Zabaleta
, Haritza Camblong
, Aitor Milo
, Haizea Gaztanaga

IKERLAN

Producción científica: Capítulo del libro/informe/acta de congreso › Contribución a la conferencia › revisión exhaustiva

4 Citas (Scopus)

Resumen

Learning based energy management strategies are promising methods, due to the high adaptability and the capability to learn from historical data. Widening the scope to a whole fleet, allows to learn from a more extend data-base and wider range of operating conditions. This paper aims to develop a methodology for improving the overall energetic efficiency of a fleet. In this regard, the main contribution lyes on the development of an intelligent decision maker, with the goal to design improved and adapted energy management strategies for each bus operating on a predefined route. The intelligent decision maker is driven by an adaptive neuro-fuzzy inference system technique that learns from the optimal operation optimized with dynamic programming. The obtained results in the developed EMS have shown similarities with the dynamic programming operation, reaching close fuel consumptions, 0.01% of difference and improvements up to 16% of fuel consumption against the initial EMS.

Idioma original	Inglés
Título de la publicación alojada	2019 IEEE Vehicle Power and Propulsion Conference, VPPC 2019 - Proceedings
Editorial	Institute of Electrical and Electronics Engineers Inc.
ISBN (versión digital)	9781728112497
DOI	https://doi.org/10.1109/VPPC46532.2019.8952368
Estado	Publicada - oct 2019
Publicado de forma externa	Sí
Evento	2019 IEEE Vehicle Power and Propulsion Conference, VPPC 2019 - Hanoi, Vietnam Duración: 14 oct 2019 → 17 oct 2019

Serie de la publicación

Nombre	2019 IEEE Vehicle Power and Propulsion Conference, VPPC 2019 - Proceedings

Conferencia

Conferencia	2019 IEEE Vehicle Power and Propulsion Conference, VPPC 2019
País/Territorio	Vietnam
Ciudad	Hanoi
Período	14/10/19 → 17/10/19

ODS de las Naciones Unidas

Este resultado contribuye a los siguientes Objetivos de Desarrollo Sostenible

ODS 7: Energía asequible y no contaminante

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10.1109/VPPC46532.2019.8952368

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Lopez-Ibarra, J. A., Goitia-Zabaleta, N., Camblong, H., Milo, A., & Gaztanaga, H. (2019). Intelligent and adaptive fleet energy management strategy for hybrid electric buses. En 2019 IEEE Vehicle Power and Propulsion Conference, VPPC 2019 - Proceedings Artículo 8952368 (2019 IEEE Vehicle Power and Propulsion Conference, VPPC 2019 - Proceedings). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/VPPC46532.2019.8952368

Lopez-Ibarra, Jon Ander ; Goitia-Zabaleta, Nerea ; Camblong, Haritza et al. / Intelligent and adaptive fleet energy management strategy for hybrid electric buses. 2019 IEEE Vehicle Power and Propulsion Conference, VPPC 2019 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2019. (2019 IEEE Vehicle Power and Propulsion Conference, VPPC 2019 - Proceedings).

@inproceedings{9622b849a58247b7946ab121bddd8607,

title = "Intelligent and adaptive fleet energy management strategy for hybrid electric buses",

abstract = "Learning based energy management strategies are promising methods, due to the high adaptability and the capability to learn from historical data. Widening the scope to a whole fleet, allows to learn from a more extend data-base and wider range of operating conditions. This paper aims to develop a methodology for improving the overall energetic efficiency of a fleet. In this regard, the main contribution lyes on the development of an intelligent decision maker, with the goal to design improved and adapted energy management strategies for each bus operating on a predefined route. The intelligent decision maker is driven by an adaptive neuro-fuzzy inference system technique that learns from the optimal operation optimized with dynamic programming. The obtained results in the developed EMS have shown similarities with the dynamic programming operation, reaching close fuel consumptions, 0.01\% of difference and improvements up to 16\% of fuel consumption against the initial EMS.",

keywords = "Dynamic programming, Fleet energy management, Hybrid electric bus, Intelligent decision maker, Neuro-fuzzy",

author = "Lopez-Ibarra, \{Jon Ander\} and Nerea Goitia-Zabaleta and Haritza Camblong and Aitor Milo and Haizea Gaztanaga",

note = "Publisher Copyright: {\textcopyright} 2019 IEEE.; 2019 IEEE Vehicle Power and Propulsion Conference, VPPC 2019 ; Conference date: 14-10-2019 Through 17-10-2019",

year = "2019",

month = oct,

doi = "10.1109/VPPC46532.2019.8952368",

language = "English",

series = "2019 IEEE Vehicle Power and Propulsion Conference, VPPC 2019 - Proceedings",

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Lopez-Ibarra, JA, Goitia-Zabaleta, N, Camblong, H, Milo, A & Gaztanaga, H 2019, Intelligent and adaptive fleet energy management strategy for hybrid electric buses. En 2019 IEEE Vehicle Power and Propulsion Conference, VPPC 2019 - Proceedings., 8952368, 2019 IEEE Vehicle Power and Propulsion Conference, VPPC 2019 - Proceedings, Institute of Electrical and Electronics Engineers Inc., 2019 IEEE Vehicle Power and Propulsion Conference, VPPC 2019, Hanoi, Vietnam, 14/10/19. https://doi.org/10.1109/VPPC46532.2019.8952368

Intelligent and adaptive fleet energy management strategy for hybrid electric buses. / Lopez-Ibarra, Jon Ander; Goitia-Zabaleta, Nerea; Camblong, Haritza et al.
2019 IEEE Vehicle Power and Propulsion Conference, VPPC 2019 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2019. 8952368 (2019 IEEE Vehicle Power and Propulsion Conference, VPPC 2019 - Proceedings).

Producción científica: Capítulo del libro/informe/acta de congreso › Contribución a la conferencia › revisión exhaustiva

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T1 - Intelligent and adaptive fleet energy management strategy for hybrid electric buses

AU - Lopez-Ibarra, Jon Ander

AU - Goitia-Zabaleta, Nerea

AU - Camblong, Haritza

AU - Milo, Aitor

AU - Gaztanaga, Haizea

PY - 2019/10

Y1 - 2019/10

N2 - Learning based energy management strategies are promising methods, due to the high adaptability and the capability to learn from historical data. Widening the scope to a whole fleet, allows to learn from a more extend data-base and wider range of operating conditions. This paper aims to develop a methodology for improving the overall energetic efficiency of a fleet. In this regard, the main contribution lyes on the development of an intelligent decision maker, with the goal to design improved and adapted energy management strategies for each bus operating on a predefined route. The intelligent decision maker is driven by an adaptive neuro-fuzzy inference system technique that learns from the optimal operation optimized with dynamic programming. The obtained results in the developed EMS have shown similarities with the dynamic programming operation, reaching close fuel consumptions, 0.01% of difference and improvements up to 16% of fuel consumption against the initial EMS.

AB - Learning based energy management strategies are promising methods, due to the high adaptability and the capability to learn from historical data. Widening the scope to a whole fleet, allows to learn from a more extend data-base and wider range of operating conditions. This paper aims to develop a methodology for improving the overall energetic efficiency of a fleet. In this regard, the main contribution lyes on the development of an intelligent decision maker, with the goal to design improved and adapted energy management strategies for each bus operating on a predefined route. The intelligent decision maker is driven by an adaptive neuro-fuzzy inference system technique that learns from the optimal operation optimized with dynamic programming. The obtained results in the developed EMS have shown similarities with the dynamic programming operation, reaching close fuel consumptions, 0.01% of difference and improvements up to 16% of fuel consumption against the initial EMS.

KW - Dynamic programming

KW - Fleet energy management

KW - Hybrid electric bus

KW - Intelligent decision maker

KW - Neuro-fuzzy

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Lopez-Ibarra JA, Goitia-Zabaleta N, Camblong H, Milo A, Gaztanaga H. Intelligent and adaptive fleet energy management strategy for hybrid electric buses. En 2019 IEEE Vehicle Power and Propulsion Conference, VPPC 2019 - Proceedings. Institute of Electrical and Electronics Engineers Inc. 2019. 8952368. (2019 IEEE Vehicle Power and Propulsion Conference, VPPC 2019 - Proceedings). doi: 10.1109/VPPC46532.2019.8952368

Intelligent and adaptive fleet energy management strategy for hybrid electric buses

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