Design and Evaluation of a Throttle Controller for Common Rail Diesel Engines

Abstract

Currently, engine remapping is widely utilized to enhance the performance of diesel and petrol engines. Given that a substantial amount of data must be collected during the optimization process, a data acquisition system is necessary to organize and retrieve data automatically. This research focuses on the design, development, and testing of a data acquisition system for controlling and measuring the throttle pedal in diesel vehicles equipped with a common rail system. The system is designed to manage the Throttle Position Sensor (TPS) and monitor vehicle operational parameters in real-time. The proposed device employs Arduino as the primary hardware, with TechStream software for vehicle data acquisition, and a web-based application developed using the Python programming language, and displays data in real-time in the form of graphs and tables. From the test results, it can be concluded that the developed acquisition system produces precise and sensitive throttle position settings. Its operation is also straightforward, safe, and requires a relatively short amount of time.

Country : Indonesia

1 Dedy Novindra2 Nazaruddin Sinaga3 Eflita Yohana

  1. Department of Mechanical Engineering, Faculty of Engineering, Diponegoro University, Jalan Prof. Soedarto, Tembalang, Semarang 50275, Central Java, Indonesia
  2. Department of Mechanical Engineering, Faculty of Engineering, Diponegoro University, Jalan Prof. Soedarto, Tembalang, Semarang 50275, Central Java, Indonesia
  3. Department of Mechanical Engineering, Faculty of Engineering, Diponegoro University, Jalan Prof. Soedarto, Tembalang, Semarang 50275, Central Java, Indonesia

IRJIET, Volume 8, Issue 10, October 2024 pp. 94-104

doi.org/10.47001/IRJIET/2024.810015

References

  1. G. Zamboni and M. Capobianco, “Effects of rail pressure control on fuel consumption, emissions and combustion parameters in a turbocharged diesel engine,” Cogent Eng, vol. 7, no. 1, Jan. 2020, doi: 10.1080/23311916.2020.1724848.
  2. G. Macias-Bobadilla, J. D. Becerra-Ruiz, A. A. Estévez-Bén, and J. Rodríguez-Reséndiz, “Fuzzy control-based system feed-back by OBD-II data acquisition for complementary injection of hydrogen into internal combustion engines,” Int J Hydrogen Energy, vol. 45, no. 51, pp. 26604–26612, Oct. 2020, doi: 10.1016/j.ijhydene.2020.07.084.
  3. J. Xue and X. Jiao, “Speed cascade adaptive control for hybrid electric vehicle using electronic throttle control during car-following process,” ISA Trans, vol. 110, pp. 328–343, Apr. 2021, doi: 10.1016/j.isatra.2020.10.058.
  4. D. Rimpas, A. Papadakis, and M. Samarakou, “OBD-II sensor diagnostics for monitoring vehicle operation and consumption,” in Energy Reports, Elsevier Ltd, Feb. 2020, pp. 55–63. doi: 10.1016/j.egyr.2019.10.018.
  5. M. Bathre and P. K. Das, “Design & implementation of smart power management system for self-powered wireless sensor nodes based on fuzzy logic controller using Proteus & Arduino Mega 2560 microcontroller,” J Energy Storage, vol. 97, Sep. 2024, doi: 10.1016/j.est.2024.112961.
  6. E. Witrant, I. D. Landau, and M. P. Vaillant, “A data-driven control methodology applied to throttle valves,” Control Eng Pract, vol. 139, Oct. 2023, doi: 10.1016/j.conengprac.2023.105634.
  7. A. H. Khalid et al., “Hydrogen port fuel injection: Review of fuel injection control strategies to mitigate backfire in internal combustion engine fuelled with hydrogen,” May 13, 2024, Elsevier Ltd. doi: 10.1016/j.ijhydene.2024.04.087.
  8. Y. Srinivasa Rao and T. Getachew Alenka, “Performance and Emission Analysis of Common Rail Diesel Engine with Microalgae Biodiesel,” Journal of Engineering (United Kingdom), vol. 2022, 2022, doi: 10.1155/2022/7441659.
  9. M. I. Ozmen, A. Yilmaz, C. Baykara, and O. A. Ozsoysal, “Modelling Fuel Consumption and NO Emission of a Medium Duty Truck Diesel Engine with Comparative Time-Series Methods,” IEEE Access, vol. 9, pp. 81202–81209, 2021, doi: 10.1109/ACCESS.2021.3082030.
  10. H. Kim, Y. Jeong, W. Choi, D. H. Lee, and H. J. Jo, “Efficient ECU Analysis Technology Through Structure-Aware CAN Fuzzing,” IEEE Access, vol. 10, pp. 23259–23271, 2022, doi: 10.1109/ACCESS.2022.3151358.
  11. N. Tendikovet al., “Security Information Event Management Data Acquisition and Analysis Methods with Machine Learning Principles,” Results in Engineering, p. 102254, Jun. 2024, doi: 10.1016/j.rineng.2024.102254.
  12. Y. Chen, S. Li, Z. P. Luo, Y. Huang, Q. P. Yuan, and B. J. Xiao, “Design of real-time data acquisition system for POlarimeter- INTerferometer diagnostic,” Fusion Engineering and Design, vol. 129, pp. 83–87, Apr. 2018, doi: 10.1016/j.fusengdes.2018.02.080.
  13. H. K. Kondaveeti, N. K. Kumaravelu, S. D. Vanambathina, S. E. Mathe, and S. Vappangi, “A systematic literature review on prototyping with Arduino: Applications, challenges, advantages, and limitations,” May 01, 2021, Elsevier Ireland Ltd. doi: 10.1016/j.cosrev.2021.100364.
  14. A.S. Kvalsund and D. Winkler, “Development of an Arduino-based, open-control interface for hardware in the loop applications,” HardwareX, vol. 16, Dec. 2023, doi: 10.1016/j.ohx.2023.e00488.
  15. G. A. Fahmy and M. Zorkany, “Design of a memristor-based digital to analogue converter (Dac),” Electronics (Switzerland), vol. 10, no. 5, pp. 1–16, Mar. 2021, doi: 10.3390/electronics10050622.
  16. R. K. Halder et al., “ML-CKDP: Machine learning-based chronic kidney disease prediction with smart web application,” J Pathol Inform, vol. 15, Dec. 2024, doi: 10.1016/j.jpi.2024.100371.
  17. M. De Luca, A. R. Fasolino, and P. Tramontana, “Investigating the robustness of locators in template-based Web application testing using a GUI change classification model,” Journal of Systems and Software, vol. 210, Apr. 2024, doi: 10.1016/j.jss.2023.111932.
  18. R. Tabarés, “HTML5 and the evolution of HTML; tracing the origins of digital platforms,” Technol Soc, vol. 65, May 2021, doi: 10.1016/j.techsoc.2021.101529.
  19. M. Serdar Biçer and B. Diri, “Defect prediction for Cascading Style Sheets,” Applied Soft Computing Journal, vol. 49, pp. 1078–1084, Dec. 2016, doi: 10.1016/j.asoc.2016.05.038.
  20. Premierautotrade, “Testing Accelerator Pedal Position Sensors (APS),” 2024.
  21. S. P. V., U. P. Borole, R. Kadam, J. Khan, H. C. Barshilia, and P. Chowdhury, “A novel AMR based angle sensor with reduced harmonic errors for automotive applications,” Sens Actuators A Phys, vol. 324, Jun. 2021, doi: 10.1016/j.sna.2021.112573.
  22. D. W. Gilbert and O. Trinidad, “Toyota Electronic Throttle Control Investigation Preliminary Report Introduction.”
  23. I.J. Jin, Y. Y. Park, and I. C. Bang, “Heat transfer performance prediction for heat pipe using deep learning based on wick type,” International Journal of Thermal Sciences, vol. 197, Mar. 2024, doi: 10.1016/j.ijthermalsci.2023.108806.
  24. S. Hosseinpour, M. Aghbashlo, M. Tabatabaei, and E. Khalife, “Exact estimation of biodiesel cetane number (CN) from its fatty acid methyl esters (FAMEs) profile using partial least square (PLS) adapted by artificial neural network (ANN),” Energy Convers Manag, vol. 124, pp. 389–398, Sep. 2016, doi: 10.1016/j.enconman.2016.07.027.