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Machine Learning Based Technology for Reducing Engine Starting Vibration of Hybrid Vehicles
Technical Paper
2019-01-1450
ISSN: 0148-7191, e-ISSN: 2688-3627
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English
Abstract
Engine starting vibration of hybrid vehicle with Toyota hybrid system has variations even in the same vehicle, and a large vibration that occurs rarely may cause stress to the passengers. The contribution analysis based on the vibration theory and statistical analysis has been done, but the primary factor of the rare large vibration has not been clarified because the number of factors is enormous. From this background, we apply machine learning that can reproduce multivariate and complicated relationships to analysis of variation factors of engine starting vibration. Variations in magnitude of the exciting force such as motor torque for starting the engine and in-cylinder pressure of the engine and timing of these forces are considered as factors of the variations. In addition, there are also nonlinear factors such as backlash of gears as a factor of variations. For the variation factor analysis, it is difficult to measure the physical quantities mentioned above from experiments, because of the high time load of installing measuring sensors and lack of measurement technology for some factors. On the other hand, it is possible to extract many factors related to the magnitude and timing of exciting forces by analyzing the command value of the ECU. Therefore, in this research, analysis data is narrowed down to the ECU command value, and the efficiency of analysis is improved by changing processing method of many data for analysis. In addition, a random forest, which is one type of machine learning, was used to contribution analysis of an enormous number of ECU command values to the engine starting vibration. As a result, new factors were extracted, and we calculated the quantitative influence of the factors on the variations of the vibration by analytical model. This result verified the method to reduce variations in vibration by controlling extracted factors by the random forest.
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Citation
Shimode, K., Ishizaki, K., and Komada, M., "Machine Learning Based Technology for Reducing Engine Starting Vibration of Hybrid Vehicles," SAE Technical Paper 2019-01-1450, 2019, https://doi.org/10.4271/2019-01-1450.Data Sets - Support Documents
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References
- Yoshihisa, J., Keisuke, I., and Masahito, M. , “Development to Improve Engine Starting Vibration in Hybrid Vehicle with Low Number of Cylinders,” TOYOTA Technical Review 60(230), 2014.
- Satoshi, W., Yuji, M., and Yosuke, O. , “Development of Engine Restart Vibration after Idling Stop Using Model Based Development Process,” Proceedings of JSAE no. 71-16, May 2016.
- Masashi, K., Takayoshi, Y., Kouji, T., Hiroshi, A. et al. , “Noise and Vibration Reduction Technology in New Generation Hybrid Vehicle Development,” Proceedings of JSAE no. 10-04, May 2004.
- The International Organization for Standardization (ISO) , “Mechanical Vibration and Shock - Evaluation of Human Exposure to Whole-Body Vibration，Part1: General Requirements,” ISO 2631-1, May 1997.
- The International Organization for Standardization (ISO) , “Mechanical Vibration and Shock - Evaluation of Human Exposure to Whole-Body Vibration，Part2: Vibration in Buildings (1 Hz to 80Hz),” ISO 2631-2, April 2003.
- Hirano, Y. , Hajimete no Pata-N Ninsiki [Pattern Recognition for Beginners] (Tokyo: Morikita Publishing, 2017).
- Research Institute of Systems Planning, Inc , Python Ni Yoru Kikai Gakushuu Nyuumon [Introduction to Machine Learning by Python] (Tokyo: Ohmsha, 2016).
- Kento, S., Keisuke, I., Masashi, K. , “Variation Analysis for Engine Starting Vibration Based on Machine Learning,” Society of Automotive Engineers of Japan, Inc., Scientific Lecture Proceeding, No. 110-18, 2018
- Society of Automotive Engineers of Japan , Zidousya Gizyutu Handobukku Kiso Riron Hen [Automotive Technology Handbook Vol.1] (Tokyo, 2015).
- Yoshikazu, I. , Zidousya you Gasorin Enzin Sekkei no Youtei [The Essence of Gasoline Engine Design for Automobiles] (Tokyo: Sankaido Publishing, 2002).