Abstract

A low computational complexity and high-accuracy method for clarification of oil temperature in a hydraulic electronically controlled rear differential was developed for implementation to a vehicle control unit. Because the target system uses a motor-driven electronic hydraulic pump to increase hydraulic pressure, a correlation was considered to exist between oil temperature and motor current. Theoretical analysis of the structure of the system demonstrated that a linear model was sufficient for clarification of the state of oil temperature from motor current, and a clarification model for the state of oil temperature using logistic regression was formulated. In addition, in order to balance accuracy and frequency of clarification, a method of calculation of posterior probability when continuous multiple clarifications were executed using only the output of the logistic regression model was developed. The results of application of the method to data obtained from the actual target system confirmed that the proposed method was able to clarify the state of oil temperature with a high degree of accuracy.

References

1. (1) Yamaguchi, N., Hara, J.: Development of Electronic Control 4WD System with Lightweight and Low Friction, 2012 JSAE Annual Congress Proceedings, No. 146-12, p. 19-23 (2012)
2. (2) Balabin, R. M., Safieva, R. Z., Lomakina, E. I.: Near-infrared (NIR) spectroscopy for motor oil classification: From discriminant analysis to support vector machines, Microchemical Journal, Vol. 98, p. 121-128 (2011)
3. (3) Pradhan, M. K., Ramu, T. S.: On-line Monitoring of Temperature in Power Transformers using Optimal Linear Combination of ANNs, Conference Record of the 2004 IEEE International Symposium on Electrical Insulation, p. 70-73 (2004)
4. (4) Tang, W. H., Zeng, H., Nuttall, K. I., Richardson, Z., Simonson, E., Wu, Q. H.: Development of Power Transformer Thermal Models for Oil Temperature Prediction, Real-World Applications of Evolutionary Computing, EvoWorkshops 2000, Lecture Notes in Computer Science, Vol. 1803, p. 195−204 (2000)
5. (5) He, Q., Si, J., Tylavsky, D. J.: Prediction of Top-Oil Temperature for Transformers Using Neural Networks, IEEE Transactions on Power Delivery, Vol. 15, No. 4, p. 1205-1211 (2000)
6. (6) Wang, H., Liu, Y., Griffin, P. J.: Artificial Intelligence in OLTC Fault Diagnosis Using Dissolved Gas-In-Oil Information, Power Engineering Society Summer Meeting, Vol. 4, p. 2422-2427 (2000)
7. (7) Bishop, C. M., Motoda, H., Kurita, T., Higuchi, T., Matsumoto, Y., Murata, N.: Pattern Recognition and Machine Learning, Springer Japan KK, p. 177-208 (2008)

Author (organization or company)

• Hiroki YOSHINO（Automobile R&D Center）

Publication Information

Article of Honda R&D Technical Review Vol.25 No.1 (149 - 155)

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Citation Information