Research on shifting process control of automatic transmission

Author:

Zou Wujun,Wang Ye,Zhong Chaojie,Song Zhenchuan,Li Shenlong

Abstract

AbstractThe shift quality of an automatic transmission directly affects the human-perceived comfort and the durability of the automatic transmission. In general, the inconsistency caused by manufacturing errors, life-cycle changes, or other changes in hydraulic characteristics are the main reason affecting the shift quality, which should be compensated by adaptive control in the shifting process. In this paper, we first provide an in-depth analysis of the relationship between proportional solenoid current, clutch pressure, speed and torque in the shifting process control. Then we propose two efficient adaptive control strategies for the torque phase and inertia phase, respectively. Both algorithms are tested and verified on a riot utility vehicle. The experimental results indicate that the adaptive control strategies proposed in this paper can effectively compensate the engine flare and the clutch tie-up of the torque phase, and keep the inertia phase within a proper time range.

Publisher

Springer Science and Business Media LLC

Subject

Multidisciplinary

Reference22 articles.

1. Song, X. Design, Modeling, and Control of Automotive Power Transmission Systems (University of Minnesota, 2011).

2. Shuichi, Y., Itsuro, K. & Tomoaki, S. Slip control during inertia phase of clutch-to-clutch shift using model-free self-tuning proportional-integral-derivative control. Proc. Inst. Mech. Eng. Part D J. Automob. Eng. 234(9), 2279–2290 (2022).

3. Gao, B., Chen, H., Hu, Y. & Sanada, K. Nonlinear feedforward–feedback control of clutch-to-clutch shift technique. Veh. Syst. Dyn. 49(12), 1895–1911 (2011).

4. Meng, F., Tao, G. & Chen, H. Smooth shift control of an automatic transmission for heavy-duty vehicles. Neurocomputing 159, 197–206 (2015).

5. Kawakami, T., Ide, T., Moriyama, E., Hoki, K. & Muramatsu, M. Development of artificial intelligence to classify quality of transmission shift control using deep convolutional neural networks. IEEE Trans. Veh. Technol. 69(12), 16168–16172 (2020).

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