Applications of AI-Algorithms for Optimizing the Range and Minimizing the Risk of Electric Vehicles

Lara Sophie van Haentjens, Ruben Ruiz Torrubiano, Michael Reiner

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Climate change and the associated effects on the environment are steadily increasing the need for alternative solutions, whether in mobility or in the economy. CO2 is the main cause of the greenhouse effect, which is emitted by the transport sector. These emissions are a major contributor to global warming and increase the risk of extreme weather events, the rise of marine play and other environmental impacts. Gases emitted today are far above 1990 levels, with an upward trend of up to a third by 2050. One solution to curb greenhouse gases and slow global warming is the use of battery-powered electric vehicles. Apart from the fact that electric vehicles are emission-free, the tax savings, for example, as well as the charging infrastructure are becoming increasingly attractive for the population. In the course of this, artificial intelligence plays an essential role and shapes everyone’s life more and more every day. The question of how vehicles can be made safer for occupants and external road users arises again and again. Artificial intelligence, with its multitude of variants and possibilities, is a ray of hope for making traffic safer in general. This paper is not only intended to show the advantages of electromobility in connection with artificial intelligence. A key factor for the end user is the range of these electric vehicles. The aim is to show how range can be optimized and where we currently stand in the electric car industry.

Original languageEnglish
Title of host publicationSystems, Software and Services Process Improvement - 31st European Conference, EuroSPI 2024, Proceedings
EditorsMurat Yilmaz, Paul Clarke, Andreas Riel, Richard Messnarz, Christian Greiner, Thomas Peisl
PublisherSpringer Science and Business Media Deutschland GmbH
Pages51-61
Number of pages11
ISBN (Print)9783031711411
DOIs
Publication statusPublished - Sept 2024
Event31st European Conference on Systems, Software and Services Process Improvement, EuroSPI 2024 - Munich, Germany
Duration: 4 Sept 20246 Sept 2024

Publication series

NameCommunications in Computer and Information Science
Volume2180 CCIS
ISSN (Print)1865-0929
ISSN (Electronic)1865-0937

Conference

Conference31st European Conference on Systems, Software and Services Process Improvement, EuroSPI 2024
Country/TerritoryGermany
CityMunich
Period4/09/246/09/24

Keywords

  • artificial intelligence
  • electromobility
  • range optimization
  • risk minimization

Research fields

  • Machine Learning

ÖFOS 2012 - Austrian Fields of Study

  • 102001 Artificial intelligence

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