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Eco-driving applications to freight trains

Dissertation (MEng (Electrical Engineering))--University of Pretoria, 2022.

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Other Authors: Ye, Xianming
Format: Thesis
Language:English
Published: University of Pretoria 2022
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access_status_str Open Access
author2 Ye, Xianming
author_browse Ye, Xianming
author_facet Ye, Xianming
collection Thesis
dc_rights_str_mv © 2022 University of Pretoria. All rights reserved. The copyright in this work vests in the University of Pretoria. No part of this work may be reproduced or transmitted in any form or by any means, without the prior written permission of the University of Pretoria.
description Dissertation (MEng (Electrical Engineering))--University of Pretoria, 2022.
format Thesis
id oai:repository.up.ac.za:2263/86665
institution University of Pretoria (South Africa)
language English
last_indexed 2026-06-10T12:38:12.930Z
license_str Other — see source repository
provenance_str_mv Harvested via OAI-PMH from UPSpace — University of Pretoria Institutional Repository
publishDate 2022
publishDateRange 2022
publishDateSort 2022
publisher University of Pretoria
publisherStr University of Pretoria
record_format dspace
source_str UPSpace — University of Pretoria Institutional Repository
spelling oai:repository.up.ac.za:2263/86665 Eco-driving applications to freight trains Ye, Xianming u15033156@tuks.co.za George, Nevin Rail roads Rail road trains Energy saver Operational costs South Africa UCTD Dissertation (MEng (Electrical Engineering))--University of Pretoria, 2022. The South African rail industry is seeking a critical measure to monitor and save energy usage in the freight rail sector. The rail industry is experiencing increasing operational costs and high energy consuming driving records along various coal lines. Eco-driving is a modern and efficient way of driving that emphasizes fuel efficiency, speed, and safety. This study provides an algorithm to find the optimal trajectory for a freight train hauling a load over a specific distance. Optimised speed profile is composed of optimal acceleration, coasting, and deceleration. The Freight Eco-Driving Energy Optimiser (FEDEO) solution is not yet applied to freight trains globally, especially in Africa. In this study, the eco-driving strategy of a freight train is formulated as an optimisation problem, whose objective function is the energy cost. The decision variables are the tractive and braking effort notches, and the speed, acceleration and distance limits are formulated as constraints. The formulated eco-driving problem is solved by Mixed-Integer Non-Linear Programming (MINLP) from the Opti- Solver toolbox. The FEDEO algorithm is applied to a train consisting of eight 19E locomotives with two-hundred CCR-9 wagons, over a distance of 90.64 km. The results show up to 34.76% reduction in energy costs. Transnet Engineering Electrical, Electronic and Computer Engineering MEng (Electrical Engineering) Unrestricted 2022-08-03T09:06:54Z 2022-08-03T09:06:54Z 2022-09-07 2022-07-27 Dissertation * S2022 https://repository.up.ac.za/handle/2263/86665 DOI: 10.25403/UPresearchdata.20424384 10.25403/UPresearchdata.20424384 en © 2022 University of Pretoria. All rights reserved. The copyright in this work vests in the University of Pretoria. No part of this work may be reproduced or transmitted in any form or by any means, without the prior written permission of the University of Pretoria. application/pdf University of Pretoria
spellingShingle Rail roads
Rail road trains
Energy saver
Operational costs
South Africa
UCTD
Eco-driving applications to freight trains
title Eco-driving applications to freight trains
title_full Eco-driving applications to freight trains
title_fullStr Eco-driving applications to freight trains
title_full_unstemmed Eco-driving applications to freight trains
title_short Eco-driving applications to freight trains
title_sort eco driving applications to freight trains
topic Rail roads
Rail road trains
Energy saver
Operational costs
South Africa
UCTD
url https://repository.up.ac.za/handle/2263/86665