상세정보
학술지
Adaptive Neural Network Based Fuzzy Control for a Smart Idle Stop and Go Vehicle Control System
- 저자
- 조광현, 최세범, 최성호, 손명희
- 발행일
- 201208
- 출처
- International Journal of Automotive Technology, v.13 no.5, pp.791-799
- ISSN
- 1229-9138
- 출판사
- 한국자동차공학회
- 초록
- 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%. © 2012 The Korean Society of Automotive Engineers and Springer-Verlag Berlin Heidelberg.
- 키워드
- Adaptive network fuzzy inference system, Clustering, Fuzzy inference system, Hybrid method, Idle stop and go system
- KSP 제안 키워드
- Adaptive neural network, Engine emissions, Fuel efficiency, Low-cost, Stop and go vehicle, Traffic congestion, Ultra-Sonic, Vehicle control system, adaptive network fuzzy inference system, fuzzy control, fuzzy logic(FL)