Analysis of the Battery Energy Estimation Model in SUMO Compared with Actual Analysis of Battery Energy Consumption

Aso Validi, Walter Morales Alvarez, Cristina Olaverri-Monreal

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

Abstract

Electric vehicles (EVs) are considered a key alternative transportation for improving energy efficiency and reducing CO2 emissions in the traffic sector. To promote the use of these vehicles a reliable, real-world close evaluation in terms of energy consumption and range is crucial. One of the most efficient and frequently adopted microscopic traffic simulation tools, simulation of urban mobility (SUMO), implements an energy estimation model that relies on vehicle and road characteristics. We conduct a comparative analysis of SUMO’s estimated energy consumption and state of charge (SOC) of a simulated battery electric vehicle (BEV) and the energy consumption of an actual 2020 Toyota RAV4 Hybrid LE AWD. Results showed that the energy consumption model in SUMO delivers different results than the ones obtained from the real world driving experiments. These findings are discussed in this paper.
Original languageEnglish
Title of host publication16th Iberian Conference on Information Systems and Technologies
Number of pages6
Publication statusPublished - 2021

Fields of science

  • 303 Health Sciences
  • 303008 Ergonomics
  • 201306 Traffic telematics
  • 202031 Network engineering
  • 202036 Sensor systems
  • 202038 Telecommunications
  • 202040 Transmission technology
  • 203 Mechanical Engineering
  • 211908 Energy research
  • 211911 Sustainable technologies
  • 102 Computer Sciences
  • 102001 Artificial intelligence
  • 102002 Augmented reality
  • 102003 Image processing
  • 102013 Human-computer interaction
  • 102015 Information systems
  • 102019 Machine learning
  • 102021 Pervasive computing
  • 102024 Usability research
  • 102026 Virtual reality
  • 102029 Practical computer science
  • 102034 Cyber-physical systems
  • 501026 Psychology of perception
  • 501 Psychology
  • 501025 Traffic psychology
  • 201305 Traffic engineering
  • 202 Electrical Engineering, Electronics, Information Engineering
  • 202003 Automation
  • 202030 Communication engineering
  • 202034 Control engineering
  • 202035 Robotics
  • 202037 Signal processing
  • 202041 Computer engineering
  • 203004 Automotive technology
  • 211902 Assistive technologies
  • 211909 Energy technology
  • 211917 Technology assessment
  • 501030 Cognitive science

JKU Focus areas

  • Digital Transformation
  • Sustainable Development: Responsible Technologies and Management

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