PriMera Scientific Engineering (ISSN: 2834-2550)

Review Article

Volume 6 Issue 1

Machine Learning Based Torque Monitoring Algorithm for Preventing Unintended Acceleration and Deceleration in Vehicles

Byunggun Kim*, Eunsang Park, Doin Kwon, and Hyunki Shin

December 19, 2024

View PDF

Abstract

The primary purpose of E-GAS monitoring is to ensure the functional stability of the electronic controller in relation to vehicle torque. At level 2 of the E-GAS monitoring concept, the calculation of permissible torque typically relies on formula-based models that simplify real vehicle behavior, accompanied by complex calibration to enhance accuracy. However, this approach often fails to adequately account for the diverse driving scenarios encountered by the vehicle. To address this limitation, this study proposes an algorithm for calculating permissible torque using machine learning at level 2 of the E-GAS monitoring concept. The effectiveness of the algorithm is validated through the analysis of real-world vehicle driving data, confirming its practicality and applicability.

Keywords: E-GAS Monitoring Concept; Torque Monitoring; Machine Learning; Electric Vehicle; Functional Safety; Vehicle Control Unit (VCU)

References

  1. Standardized e-gas monitoring concept for gasoline and diesel engine control units version 6.0. Technical report (2020).
  2. Yi F, Zhang W and Zhou W. “Functional safety design for torque control of a pure electric vehicle”. In: 2021 9th International Symposium on Next Generation Electronics (ISNE), IEEE (2021): 1-4.
  3. Xin Z., et a. “Research of driving torque monitoring for electric vehicle”. In: 2021 IEEE International Conference on Emergency Science and Information Technology (ICESIT), IEEE (2021): 31-34.
  4. Ji Y and Lee H. “The LSTM-based engine clutch engagement/disengagement anomaly detection algorithm for p2 HEV”. Trans. Korean Soc. Autom. Eng 29.12 (2021): 1133-1146.
  5. Lee D., et a. “Anomaly detection in rotating motor using two-level LSTM”. In: Proceedings of KIIT Conference (2021): 425-428.
  6. Lee Y-K, Hong S-C and Hong J-K. “LSTM-based drone's anomal motor vibration detection system”. J. Inf. Technol. Archit 17.4 (2020): 315-321.