A Simplified Fuel Efficient Predictive Cruise Control Approach

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

Abstract

Adaptive cruise control (ACC) systems allow a safe and reliable driving by adapting the velocity of the vehicle to velocity setpoints and the distance from preceding vehicles. This substantially reduces the effort of the driver especially in heavy traffic conditions. However, standard ACC systems do not necessarily take in account comfort and fuel efficiency. Recently some work has been done of the latter aspect. This paper extends previous works for CI engines by incorporating a prediction model of the surrounding traffic and a simplified control law capable for real time use in experiments. The prediction model itself uses sinusoidal functions as the traffic measurements often show periodic behavior and is adapted in every sample instant with respect to the predecessor's velocity. Furthermore, the controlled vehicle is forced to stay within a specific inter-vehicle distance corridor to avoid collisions and ensure safe driving. The main advantage of the proposed approach is a simple and fast real time capable implementation, not only for a specific engine type but for a wide range of engines. Simulation and experimental results show great potential concerning an increase in fuel economy in the range of 10% and also reduction of emissions like NOx and particulate matter compared to the preceding vehicle.
Original languageEnglish
Title of host publicationSAE World Congress
Number of pages7
DOIs
Publication statusPublished - 14 Apr 2015

Publication series

NameSAE Technical Papers

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

  1. SDG 3 - Good Health and Well-being
    SDG 3 Good Health and Well-being

Fields of science

  • 206002 Electro-medical engineering
  • 207109 Pollutant emission
  • 202 Electrical Engineering, Electronics, Information Engineering
  • 202027 Mechatronics
  • 202034 Control engineering
  • 203027 Internal combustion engines
  • 206001 Biomedical engineering

JKU Focus areas

  • Mechatronics and Information Processing

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