| ADAPTIVE NEURAL NETWORK BASED FUZZY CONTROL FOR A SMART IDLE STOP AND GO VEHICLE CONTROL SYSTEM |
| K. CHO1, S. B. CHOI1, S. CHOI2, M. SON3 |
1KAIST
2Hyundai Motor Company
3Electronics & Telecommunications Research Institue |
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| ABSTRACT |
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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 |
| Key Words:
Idle stop and go system, Fuzzy inference system, Clustering, Adaptive network fuzzy inference system, Hybrid method |
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