This content is not included in your SAE MOBILUS subscription, or you are not logged in.
Drivability Evaluation Model of Engine Start Based on Principal Component Analysis and Support Vector Regression
ISSN: 0148-7191, e-ISSN: 2688-3627
Published April 02, 2019 by SAE International in United States
This content contains downloadable datasetsAnnotation ability available
Aiming at the problem that the evaluation model had proposed by researchers to evaluate the drivability of a vehicle in the process of engine start to exist poor stability and poor accuracy. In this paper, a drivability evaluation model combined with principal component analysis and support vector regression is proposed. In this evaluation model, the principal component analysis is adapted to determine the input index of evaluation model, and the drivability evaluation model is built on the basis of support vector regression. The experimental results demonstrate that the drivability evaluation model is proposed by this paper has higher accuracy and stability than the model using the BP neural network. This method can be as well extended to other evaluation models, with higher theoretical guidance and application value in practical issues.
CitationHuang, W., Liu, J., and Ma, Y., "Drivability Evaluation Model of Engine Start Based on Principal Component Analysis and Support Vector Regression," SAE Technical Paper 2019-01-0932, 2019, https://doi.org/10.4271/2019-01-0932.
Data Sets - Support Documents
|[Unnamed Dataset 1]|
|[Unnamed Dataset 2]|
|[Unnamed Dataset 3]|
|[Unnamed Dataset 4]|
|[Unnamed Dataset 5]|
- Council C, Incorporated , 2013 CRC Intermediate-Temperature E15 Cold-Start and Warm-Up Vehicle Drivability Program [R] (Alpharetta: CRC Performance Committee of the Coordinating Research Council), 2014.
- Baustian, J. and Wolf, L. , “Cold-Start/Warm-Up Vehicle Performance and Driveability Index for Gasolines Containing Isobutanol [J],” SAE International Journal of Fuels & Lubricants 5(3):1300-1309, 2012, doi:10.4271/2012-01-1741.
- Jewitt, C.H., Gibbs, L.M., and Evans, B. , “Gasoline Driveability Index, Ethanol Content and Cold-Start/Warm-Up Vehicle Performance [J],” SAE Technical Paper 2005-01-3864 , 2005, doi:10.4271/2005-01-3864.
- Wellmann, T., Govindswamy, K., Orzechowski, J. et al. , “Influence of Automatic Engine Stop/Start Systems on Vehicle NVH and Launch Performance[J],” SAE Int. J. Engines 8(4):1938-1946, 2015, doi:10.4271/2015-01-2183.
- Zhang, L., Ye, Y., and Yu, Z. , “Experimental Investigation Into Starting Noise and Vibration of an Engine Used in Hybrid Electric Vehicle[J],” Automobile Technology, 2009, doi:10.3969/j.issn.1000-3703.2009.03. 008.
- Chang, W.S., Kim, H., Kim, M.K. et al. , “Development of an Evaluation Method for Quantitative Drivability in Heavy-Duty Vehicles[J],” Journal of Mechanical Science & Technology 28(5):1615-1621, 2014, doi:10.1007/s12206-014-0107-5.
- Sun, J. , “Research on Driving Performance Evaluation and Simulation Method [D],” Jilin University, 2017.
- Ahn, S.J. and Griffin, M.J. , “Effects of Frequency, Magnitude, Damping, and Direction on the Discomfort of Vertical Whole-Body Mechanical Shocks [J],” Journal of Sound & Vibration 311(1):485-497, 2008, doi:10.1016/j.jsv.2007.09.026.
- Director, S.E.S.D. , “Review of the State of Development of Advanced Vehicle Control Systems (AVCS) [J],” Vehicle System Dynamics 24(6):551-595, 1995, doi:10.1080/0042311950896 9108.
- Sharma, B. , “CAE Driven Multi-Disciplinary Optimization of Vehicle Systems and Sub Systems[C],” SAE Technical Paper 2014-01-0804 , 2014, doi:10.4271/2014-01-0804.
- Wu, W.J. and Xu, Y. , “Correlation Analysis of Visual Verbs' Subcategorization Based on Pearson's Correlation Coefficient [C],” in 2010 International Conference on Machine Learning and Cybernetics. IEEE, 2010, 2042-2046, doi:10.1109/ICMLC.2010.5580507.
- Han, S., Zhang, W., Li, P. et al. , “Characterization of Aromatic Liquor by Gas Chromatography and Principal Component Analysis[J],” Analytical Letters 50(5):10, 2017, doi:10.1080/00032 719.2016.1196365.
- Chelgani, S.C., Shahbazi, B., and Hadavandi, E. , “Support Vector Regression Modeling of Coal Flotation Based on Variable Importance Measurements by Mutual Information Method [J],” Measurement 114:102-108, 2018, doi:10.1016/j.measurement.2017.09.025.
- Zirui, L., Gernot, R., Müller, P. et al. , “Using Support Vector Regression to Estimate Valence Level from EEG [C],” in IEEE International Conference on Systems, Man, and Cybernetics, IEEE, 2017, 002558-002563, doi:10.1109/SMC.2016.7844624.
- Díaz, S., Carta, J.A., and Matías, J.M. , “Comparison of Several Measure-Correlate-Predict Models Using Support Vector Regression Techniques to Estimate Wind Power Densities. A Case Study [J],” Energy Conversion & Management 140:334-354, 2017, doi:10.1016/j.enconman.2017.02.064.