Sensing and control technologies for dynamic prediction of battery-driven electric locomotives

Sakura Mukhopadhyay; Sara Deilami; Foad Taghizadeh

doi:10.1109/ICST62759.2024.10992159

Sensing and control technologies for dynamic prediction of battery-driven electric locomotives

Sakura Mukhopadhyay, Sara Deilami, Foad Taghizadeh

School of Engineering

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

Abstract

In this paper, an energy balance approach has been proposed to determine the maximum distance travelled by the lithium-ion battery driven electric locomotive during catenary-free operation for any railway environment. Through an energy balance approach, an algorithm has been developed of maximum distance the battery can deliver as a function of several variables and further optimized proposing an algorithm to maximize the distance. This paper further reviews and presents the sensors and control technologies available to measure the rail parameters and aid in producing dynamic live prediction.

Original language	English
Title of host publication	2024 17th International Conference on Sensing Technology (ICST)
Place of Publication	Piscataway, NJ
Publisher	Institute of Electrical and Electronics Engineers (IEEE)
Pages	307-312
Number of pages	6
ISBN (Electronic)	9798350374827
ISBN (Print)	9798350374834
DOIs	https://doi.org/10.1109/ICST62759.2024.10992159
Publication status	Published - 2024
Event	17th International Conference on Sensing Technology, ICST 2024 - Sydney, Australia Duration: 9 Dec 2024 → 11 Dec 2024

Publication series

Name
ISSN (Print)	2156-8065
ISSN (Electronic)	2156-8073

Conference

Conference	17th International Conference on Sensing Technology, ICST 2024
Country/Territory	Australia
City	Sydney
Period	9/12/24 → 11/12/24

Keywords

Electric train
battery driven locomotive
energy balance train modelling
rail electrification
sensing technology
control technologies

Access to Document

10.1109/ICST62759.2024.10992159

Cite this

@inproceedings{01cd44522c1a424fa53c416e42cf2e1a,

title = "Sensing and control technologies for dynamic prediction of battery-driven electric locomotives",

abstract = "In this paper, an energy balance approach has been proposed to determine the maximum distance travelled by the lithium-ion battery driven electric locomotive during catenary-free operation for any railway environment. Through an energy balance approach, an algorithm has been developed of maximum distance the battery can deliver as a function of several variables and further optimized proposing an algorithm to maximize the distance. This paper further reviews and presents the sensors and control technologies available to measure the rail parameters and aid in producing dynamic live prediction.",

keywords = "Electric train, battery driven locomotive, energy balance train modelling, rail electrification, sensing technology, control technologies",

author = "Sakura Mukhopadhyay and Sara Deilami and Foad Taghizadeh",

year = "2024",

doi = "10.1109/ICST62759.2024.10992159",

language = "English",

isbn = "9798350374834",

publisher = "Institute of Electrical and Electronics Engineers (IEEE)",

pages = "307--312",

booktitle = "2024 17th International Conference on Sensing Technology (ICST)",

address = "United States",

note = "17th International Conference on Sensing Technology, ICST 2024 ; Conference date: 09-12-2024 Through 11-12-2024",

}

Mukhopadhyay, S , Deilami, S & Taghizadeh, F 2024, Sensing and control technologies for dynamic prediction of battery-driven electric locomotives. in 2024 17th International Conference on Sensing Technology (ICST). Institute of Electrical and Electronics Engineers (IEEE), Piscataway, NJ, pp. 307-312, 17th International Conference on Sensing Technology, ICST 2024, Sydney, Australia, 9/12/24. https://doi.org/10.1109/ICST62759.2024.10992159

Sensing and control technologies for dynamic prediction of battery-driven electric locomotives. / Mukhopadhyay, Sakura ; Deilami, Sara ; Taghizadeh, Foad.
2024 17th International Conference on Sensing Technology (ICST). Piscataway, NJ: Institute of Electrical and Electronics Engineers (IEEE), 2024. p. 307-312.

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

TY - GEN

T1 - Sensing and control technologies for dynamic prediction of battery-driven electric locomotives

AU - Mukhopadhyay, Sakura

AU - Deilami, Sara

AU - Taghizadeh, Foad

PY - 2024

Y1 - 2024

N2 - In this paper, an energy balance approach has been proposed to determine the maximum distance travelled by the lithium-ion battery driven electric locomotive during catenary-free operation for any railway environment. Through an energy balance approach, an algorithm has been developed of maximum distance the battery can deliver as a function of several variables and further optimized proposing an algorithm to maximize the distance. This paper further reviews and presents the sensors and control technologies available to measure the rail parameters and aid in producing dynamic live prediction.

AB - In this paper, an energy balance approach has been proposed to determine the maximum distance travelled by the lithium-ion battery driven electric locomotive during catenary-free operation for any railway environment. Through an energy balance approach, an algorithm has been developed of maximum distance the battery can deliver as a function of several variables and further optimized proposing an algorithm to maximize the distance. This paper further reviews and presents the sensors and control technologies available to measure the rail parameters and aid in producing dynamic live prediction.

KW - Electric train

KW - battery driven locomotive

KW - energy balance train modelling

KW - rail electrification

KW - sensing technology

KW - control technologies

UR - https://www.scopus.com/pages/publications/105006441944

U2 - 10.1109/ICST62759.2024.10992159

DO - 10.1109/ICST62759.2024.10992159

M3 - Conference proceeding contribution

AN - SCOPUS:105006441944

SN - 9798350374834

SP - 307

EP - 312

BT - 2024 17th International Conference on Sensing Technology (ICST)

PB - Institute of Electrical and Electronics Engineers (IEEE)

CY - Piscataway, NJ

T2 - 17th International Conference on Sensing Technology, ICST 2024

Y2 - 9 December 2024 through 11 December 2024

ER -

Sensing and control technologies for dynamic prediction of battery-driven electric locomotives

Abstract

Publication series

Conference

Keywords

Access to Document

Other files and links

Fingerprint

Cite this