Long hauling eco-driving: Heavy-duty trucks operational modes control with integrated road slope preview

Gustavo R. Gonçalves Da Silva; Mircea Lazar

doi:10.23919/ECC55457.2022.9837981

Long hauling eco-driving: Heavy-duty trucks operational modes control with integrated road slope preview

Gustavo R. Gonçalves Da Silva, Mircea Lazar

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

4 Citations (Scopus)

Abstract

In this paper, a complete eco-driving strategy for heavy-duty trucks (HDT) based on a finite number of driving modes with corresponding gear shifting is developed to cope with different route events and with road slope data. The problem is formulated as an optimal control problem with respect to fuel consumption and trip duration, and solved using a Pontryagin minimum principle (PMP) algorithm for a path search problem, such that computations can be carried out online, in real-time. The developed eco-driving assistance system (EDAS) provides a velocity profile and a sequence of driving modes (and gears) recommendation to the driver, without actively controlling the HDT (human in the loop) and, in practice, allows contextual feedback incorporation from the driver for safety. Simulation results show that the developed methodology is able to provide a velocity profile for a complete route based on known road events and slope information while satisfying all truck operational constraints.

Original language	English
Title of host publication	2022 European Control Conference, ECC 2022
Publisher	Institute of Electrical and Electronics Engineers
Pages	1752-1758
Number of pages	7
ISBN (Electronic)	9783907144077
DOIs	https://doi.org/10.23919/ECC55457.2022.9837981
Publication status	Published - 2022
Event	2022 European Control Conference, ECC 2022 - Imperial College London, London, United Kingdom Duration: 12 Jul 2022 → 15 Jul 2022 https://ecc22.euca-ecc.org/

Conference

Conference	2022 European Control Conference, ECC 2022
Abbreviated title	ECC 2022
Country/Territory	United Kingdom
City	London
Period	12/07/22 → 15/07/22
Internet address	https://ecc22.euca-ecc.org/

Bibliographical note

Funding Information:
This research was funded by the European Union’s Horizon 2020 Research and Innovation Programme, Grant Agreement no. 874972, Project LONGRUN.

Funding

This research was funded by the European Union’s Horizon 2020 Research and Innovation Programme, Grant Agreement no. 874972, Project LONGRUN.

Keywords

driving modes
Eco-driving
heavy-duty trucks
optimization
Pontryagin minimum principle

Access to Document

10.23919/ECC55457.2022.9837981

Cite this

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title = "Long hauling eco-driving: Heavy-duty trucks operational modes control with integrated road slope preview",

abstract = "In this paper, a complete eco-driving strategy for heavy-duty trucks (HDT) based on a finite number of driving modes with corresponding gear shifting is developed to cope with different route events and with road slope data. The problem is formulated as an optimal control problem with respect to fuel consumption and trip duration, and solved using a Pontryagin minimum principle (PMP) algorithm for a path search problem, such that computations can be carried out online, in real-time. The developed eco-driving assistance system (EDAS) provides a velocity profile and a sequence of driving modes (and gears) recommendation to the driver, without actively controlling the HDT (human in the loop) and, in practice, allows contextual feedback incorporation from the driver for safety. Simulation results show that the developed methodology is able to provide a velocity profile for a complete route based on known road events and slope information while satisfying all truck operational constraints.",

keywords = "driving modes, Eco-driving, heavy-duty trucks, optimization, Pontryagin minimum principle",

author = "{Gon{\c c}alves Da Silva}, {Gustavo R.} and Mircea Lazar",

note = "Funding Information: This research was funded by the European Union{\textquoteright}s Horizon 2020 Research and Innovation Programme, Grant Agreement no. 874972, Project LONGRUN. ; 2022 European Control Conference, ECC 2022, ECC 2022 ; Conference date: 12-07-2022 Through 15-07-2022",

year = "2022",

doi = "10.23919/ECC55457.2022.9837981",

language = "English",

pages = "1752--1758",

booktitle = "2022 European Control Conference, ECC 2022",

publisher = "Institute of Electrical and Electronics Engineers",

address = "United States",

url = "https://ecc22.euca-ecc.org/",

}

Gonçalves Da Silva, GR & Lazar, M 2022, Long hauling eco-driving: Heavy-duty trucks operational modes control with integrated road slope preview. in 2022 European Control Conference, ECC 2022., 9837981, Institute of Electrical and Electronics Engineers, pp. 1752-1758, 2022 European Control Conference, ECC 2022, London, United Kingdom, 12/07/22. https://doi.org/10.23919/ECC55457.2022.9837981

Long hauling eco-driving: Heavy-duty trucks operational modes control with integrated road slope preview. / Gonçalves Da Silva, Gustavo R.; Lazar, Mircea.
2022 European Control Conference, ECC 2022. Institute of Electrical and Electronics Engineers, 2022. p. 1752-1758 9837981.

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

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AB - In this paper, a complete eco-driving strategy for heavy-duty trucks (HDT) based on a finite number of driving modes with corresponding gear shifting is developed to cope with different route events and with road slope data. The problem is formulated as an optimal control problem with respect to fuel consumption and trip duration, and solved using a Pontryagin minimum principle (PMP) algorithm for a path search problem, such that computations can be carried out online, in real-time. The developed eco-driving assistance system (EDAS) provides a velocity profile and a sequence of driving modes (and gears) recommendation to the driver, without actively controlling the HDT (human in the loop) and, in practice, allows contextual feedback incorporation from the driver for safety. Simulation results show that the developed methodology is able to provide a velocity profile for a complete route based on known road events and slope information while satisfying all truck operational constraints.

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KW - optimization

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Long hauling eco-driving: Heavy-duty trucks operational modes control with integrated road slope preview

Abstract

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Keywords

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