2021222324252623 of 39
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Evaluating Multi-task Curriculum Learning for Forecasting Energy Consumption in Electric Heavy-duty Vehicles
Halmstad University, Sweden.ORCID iD: 0000-0002-3034-6630
Halmstad University, Sweden.ORCID iD: 0000-0002-7796-5201
Volvo Group, Sweden.ORCID iD: 0009-0007-7976-2874
RISE Research Institutes of Sweden, Digital Systems, Data Science.ORCID iD: 0000-0003-3272-4145
2024 (English)In: Embracing Human-Aware AI in Industry 2024, CEUR-WS , 2024, Vol. 3765Conference paper, Published paper (Refereed)
Abstract [en]

Accurate energy consumption prediction is crucial for optimising the operation of electric commercial heavy-duty vehicles, particularly for efficient route planning, refining charging strategies, and ensuring optimal truck configuration for specific tasks. This study investigates the application of multi-task curriculum learning to enhance machine learning models for forecasting the energy consumption of various onboard systems in electric vehicles. Multi-task learning, unlike traditional training approaches, leverages auxiliary tasks to provide additional training signals, which has been shown to enhance predictive performance in many domains. By further incorporating curriculum learning, where simpler tasks are learned before progressing to more complex ones, neural network training becomes more efficient and effective. We evaluate the suitability of these methodologies in the context of electric vehicle energy forecasting, examining whether the combination of multi-task learning and curriculum learning enhances algorithm generalisation, even with limited training data. We primarily focus on understanding the efficacy of different curriculum learning strategies, including sequential learning and progressive continual learning, using complex, real-world industrial data. Our research further explores a set of auxiliary tasks designed to facilitate the learning process by targeting key consumption characteristics projected into future time frames. The findings illustrate the potential of multi-task curriculum learning to advance energy consumption forecasting, significantly contributing to the optimisation of electric heavy-duty vehicle operations. This work offers a novel perspective on integrating advanced machine learning techniques to enhance energy efficiency in the exciting field of electromobility. 

Place, publisher, year, edition, pages
CEUR-WS , 2024. Vol. 3765
Series
CEUR Workshop Proceedings, E-ISSN 1613-0073 ; 3765
Keywords [en]
Charging strategies; Commercial heavy-duty vehicle; Curriculum learning; Energy consumption forecasting; Energy consumption prediction; Energy-consumption; Heavy duty vehicles; Multi tasks; Multitask learning; Route planning; Curricula
National Category
Computer Sciences
Identifiers
URN: urn:nbn:se:ri:diva-76044Scopus ID: 2-s2.0-85206261149OAI: oai:DiVA.org:ri-76044DiVA, id: diva2:1909413
Conference
Workshop on Embracing Human-Aware AI in Industry 5.0 (HAII5.0 2024) co-located with the 27TH EUROPEAN CONFERENCE ON ARTIFICIAL INTELLIGENCE (ECAI 2024)
Funder
Knowledge FoundationVinnova
Note

The work was carried out with support from the Knowledge Foundation and Vinnova (Sweden's innovation agency) through the Vehicle Strategic Research and Innovation Programme FFI.

Available from: 2024-10-30 Created: 2024-10-30 Last updated: 2024-10-30Bibliographically approved

Open Access in DiVA

fulltext(1066 kB)4 downloads
File information
File name FULLTEXT01.pdfFile size 1066 kBChecksum SHA-512
9f5bde9484c494925da7d4e83ffe527e87de2e0533d4f4fdec5e8b00f650967bf4951fc4ed20bc92eca4220bd1c3e62083034f909a670052d1e6d623aaf62411
Type fulltextMimetype application/pdf

Other links

Scopushttps://ceur-ws.org/Vol-3765/Camera_Ready_Paper-07.pdf

Authority records

Pashami, Sepideh

Search in DiVA

By author/editor
Fan, YuantaoNowaczyk, SławomirWang, ZhenkanPashami, Sepideh
By organisation
Data Science
Computer Sciences

Search outside of DiVA

GoogleGoogle Scholar
Total: 4 downloads
The number of downloads is the sum of all downloads of full texts. It may include eg previous versions that are now no longer available

urn-nbn

Altmetric score

urn-nbn
Total: 48 hits
2021222324252623 of 39
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf