ADAPTIVE NEURAL NETWORK BASED FUZZY CONTROL FOR A SMART IDLE STOP AND GO VEHICLE CONTROL SYSTEM

Cited 6 time in webofscience Cited 0 time in scopus
  • Hit : 226
  • Download : 0
Idle stop and go (ISG) is a low cost but very effective technology to improve fuel efficiency and reduce engine emissions by preventing unnecessary engine idling. In this study, a new method is developed to improve the performance of conventional ISG by monitoring traffic conditions. To estimate frontal traffic conditions, an ultra-sonic ranging sensor is employed. Several fuzzy logic algorithms are developed to determine whether the engine idling is on or off. The algorithms are evaluated experimentally using various data gathered in real areas with traffic congestion. The evaluation results show that the method developed can reduce the chance of false application of ISG significantly while improving fuel efficiency up to 15%.
Publisher
KOREAN SOC AUTOMOTIVE ENGINEERS-KSAE
Issue Date
2012-08
Language
English
Article Type
Article
Citation

INTERNATIONAL JOURNAL OF AUTOMOTIVE TECHNOLOGY, v.13, no.5, pp.791 - 799

ISSN
1229-9138
DOI
10.1007/s12239-012-0079-3
URI
http://hdl.handle.net/10203/100548
Appears in Collection
ME-Journal Papers(저널논문)
Files in This Item
There are no files associated with this item.
This item is cited by other documents in WoS
⊙ Detail Information in WoSⓡ Click to see webofscience_button
⊙ Cited 6 items in WoS Click to see citing articles in records_button

qr_code

  • mendeley

    citeulike


rss_1.0 rss_2.0 atom_1.0