DOI QR코드

DOI QR Code

GPS를 이용한 차량 연료차단 관성주행의 감지에 관한 연구

A Study for Detecting Fuel-cut Driving of Vehicle Using GPS

  • 고광호 (평택대학교 스마트자동차학과)
  • Ko, Kwang-Ho (Division of Smart Automobile, Pyeongtaek University)
  • 투고 : 2019.08.23
  • 심사 : 2019.11.20
  • 발행 : 2019.11.28

초록

대부분의 차량에 적용되어 있는 연료차단(fuel-cut) 관성주행은 변속기어 체결 상태에서 가속페달을 방치할 때 자동으로 작동하게 된다. 이 때 연료분사가 일시적으로 중단되므로 연비 향상 효과가 상당하다. 본 연구에서는 GPS를 이용하여 측정된 차속, 가속도, 도로구배 등의 신호를 바탕으로 하는 연료차단 관성주행 감지법을 제안하였다. 관성 주행시 작용하는 주행저항력에 의해 계산되는 가속도값과 GPS에서 실시간으로 측정되는 가속도값을 비교하는 방식이다. 실도로 주행 데이터를 측정하여 이 감지법을 평가한 결과 약 80% 수준의 정확도를 얻을 수 있었다. 도로구배가 다소 큰 12km 정도의 국도를 16분 동안 주행하면서 측정한 약 9,600개의 속도, 가속도, 도로구배 및 연료소모량 데이터에 감지법을 적용하여 얻은 결과이다. 인젝터 분사파형 분석을 위한 배선작업 등이 불필요하여 간단하게 연료차단여부를 판정할 수 있는 장점이 있다. 다만, 속도, 가속도 및 도로구배의 변화율이 연료소모량의 변화율에 비해 훨씬 크게 나타나기 때문에 감지법의 오차도 다소 증가하는 것을 알 수 있었다.

The fuel-cut coast-down driving mode is activated when the acceleration pedal is released with transmission gear engaged, and it's a default function for electronic-controlled engine of vehicles. The fuel economy becomes better because fuel injection stops during fuel-cut driving mode. A fuel-cut detection method is suggested in the study and it's based on the speed, acceleration and road gradient data from GPS sensor. It detects fuel-cut driving mode by comparing calculated acceleration and realtime acceleration value. The one is estimated with driving resistance in the condition of fuel-cut driving and the other is from GPS sensor. The detection accuracy is about 80% when the method is verified with road driving data. The result is estimated with 9,600 data set of vehicle speed, acceleration, fuel consumption and road gradient from test driving on the road of 12km during 16 minutes, and the road slope is rather high. It's easy to detect fuel-cut without injector signal obtained by connecting wire. The detection error is from the fact that the variation range of speed, acceleration and road gradient data, used for road resistance force, is larger than the value of fuel consumption data.

키워드

참고문헌

  1. K. H. Ko & S. C. Choi. (2012). A study on the improvement of vehicle fuel economy by fuel-cut driving. Journal of the KAIS, 13(2) , 498-503. UCI : G704-001653.2012.13.2.019
  2. S. K. Kwon, M. K. Kang, Y. H. Seo & S. W. Park. (2018). EFFECTS OF TEST CONDITIONS ON FUEL ECONOMY OF GASOLINE-POWERED VEHICLE. International Journal of Automotive Technology, 19(2), 253-261. DOI : 10.1007/s12239−018−0024−1
  3. O. J. Kweon, C. K. Park & B. S. Oh. (2006). A Study of Increasing Regeneration Energy and Braking Using Super Capacitor(EDLC). Transactions of the KSAE, 14(6), 24-33. UCI : G704-000300.2006.14.6.001
  4. S. C. Choi & T. I. Oh. (2011). Eco-driving Method at Highway including Grade using GPS Altitude data. Journal of KAIS, 12(1), 19-25. UCI : G704-001653.2011.12.1.053
  5. K. H. Ko & S. H. Jeong, I. K. Yoo, S. H. Lee & J. W. Kim. (2010). An Experimental Study on Reduction of CO2 Exhausted Emission by using Fuel-cut Function of Vehicles. Transactions of KSAE, 18(1), 86-92. UCI : G704-000300.2010.18.1.007
  6. K. H. Ko. (2010). The Change Rate of Fuel Consumption for Different IRI of Paved Roads. International journal of highway engineering, 12(1), 55-59. UCI : G704-001414.2010.12.1.009
  7. D. K. Kang & M. S. Kim. (2016). Determination of the road load on electric two-wheelers using the torque-current relationship of the drive motor. Journal of mechanical science and technology, 30(9), 4023-4029. UCI : G704-000058.2016.30.9.030 https://doi.org/10.1007/s12206-016-0815-0
  8. U. K. Lee. (2009). A proposition for new vehicle dynamic performance index. Journal of Mechanical Science and Technology, 23(4), 889-893 UCI : G704-000058.2009.23.4.043 https://doi.org/10.1007/s12206-009-0307-6
  9. J. U. Cho & M. S. Han. (2010). Flow Analysis on the Outside of Automotive Body. Journal of KSMPE, 9(1), 55-60. UCI : G704-001745.2010.9.1.013
  10. S. Sato, T. Yamamoto & Y. Ogawa. (2009). Research on Measurement Method of Road Gradient and Altitude by On-Road Driving, Transactions of the SAEJ, 39(5), 163-168. DOI : https://doi.org/10.4271/2009-01-1116
  11. H. L. Lin, Q. Yu, X. L. Zhang. (2011). A New Computational Model about Vehicle's Sliding Resistance Coefficients, Advanced Materials Research, 228(229), 60-65. DOI : 10.4028/www.scientific.net/AMR.228-229.60
  12. Z. Zhang, Y. C. Zhang & J. Li. (2010). Vehicles Aerodynamics while Crossing each other on Road Based on Computational Fluid Dynamics, Applied mechanics and materials, 29(32), 1344-1349. DOI : 10.4028/www.scientific.net/AMM.29-32.1344
  13. E. Gardetto, T. Bagian & J. Lindner. (2005). High-mileage study of on-board diagnostic emissions, Journal of the Air & Waste Management Association, 55(10), 1480-1485 DOI : 10.1080/10473289.2005.10464745
  14. K. T. Kim, Y. K. Ju & J. N. Jeon. (2018). A Detection Algorithm for Modulation Types of Radar Signals Using Autocorrelation Comparison Ranges, Journal of Convergence for Information Technology, 8(5), 137-143 DOI : 10.22156/CS4SMB.2018.8.5.137
  15. J. S. Lim. (2019). A Design of Small Size Sensor Data Acquisition and Transmission System , Journal of Convergence for Information Technology, 9(1), 136-141 DOI : 10.22156/CS4SMB.2019.9.1.136