Corroborative Evaluation of the Real-World Energy Saving Potentials of InfoRich Eco-Autonomous Driving (iREAD) System

Raj Rajkumar, Junfeng Zhao, Chen Fang Chang, Jeffrey Gonder

Research output: Contribution to conferencePaperpeer-review

4 Scopus Citations


There has been an increasing interest in exploring the potential to reduce energy consumption of future connected and automated vehicles. People have extensively studied various eco-driving implementations that leverage preview information provided by on-board sensors and connectivity, as well as the control authority enabled by automation. Quantitative real-world evaluation of eco-driving benefits is a challenging task. The standard regulatory driving cycles used for measuring exhaust emissions and fuel economy are not truly representative of real-world driving, nor for capturing how connectivity and automation might influence driving trajectories. To adequately consider real-world driving behavior and potential "off-cycle" impacts, this paper presents four collaborative evaluation methods: Large-scale simulation, in-depth simulation, vehicle-in-the-loop testing, and vehicle road testing. These four approaches, spanning simulation and testing aspects, evaluate real-world fuel economy benefits with different ranges and resolutions. The large-scale simulations leverage an extensive real-world driving database to assess overall eco-driving benefits across a range of road network and driving scenarios. The real-world driving data are further leveraged to generate representative driving routes for deeper evaluation. Based on the representative routes, in-depth simulation relying on high-fidelity models investigates how different traffic scenarios can impact the eco-driving performance. The vehicle-in-the-loop setup reinforces the in-depth simulations by conducting tests with an actual vehicle operated on a chassis dynamometer; the measured energy savings were indeed found to agree with the in-depth simulation savings estimates. Finally, limited but representative road testing with the fully integrated vehicle will be conducted to demonstrate the eco-driving capability and conclude the overall evaluation regimen.

Original languageAmerican English
Number of pages9
StatePublished - 14 Apr 2020
EventSAE 2020 World Congress Experience, WCX 2020 - Detroit, United States
Duration: 21 Apr 202023 Apr 2020


ConferenceSAE 2020 World Congress Experience, WCX 2020
Country/TerritoryUnited States

Bibliographical note

Publisher Copyright:
© 2020 SAE International; General Motors; National Renewable Energy Laboratory.

NREL Publication Number

  • NREL/CP-5400-75912

Other Report Number

  • SAE Technical Paper No. 2020-01-0588


  • ARPA-E
  • automated vehicle
  • CAV
  • eco-driving
  • FASTSim
  • large-scale simulation
  • TSDC


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