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Clutch control of a hybrid electrical vehicle based on neuron-adaptive PID algorithm

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Abstract

This paper presents the detailed analysis of a pneumatic clutch actuator with an artificial intelligence control algorithm. The low cost of pneumatic actuator makes it an advantage in automotive applications, but fast and precise control is difficult on account of its time-varying and nonlinear character. In order to achieve good performance and save cost, the development of a fast, accurate, and inexpensive automatic pneumatic actuator clutch system for a single-axle parallel hybrid electrical is important. Targeted for the working principle of electric-drive automated mechanical transmission parallel hybrid system for shifting and clutch dynamic control during operation mode switch course, the design adopts single neuron-adaptive PID-based clutch operation process, proposed the clutch actuator piston non-contact cylinder lock control method to overcome time-varying and nonlinear characteristics’ impact present in the pneumatic actuator closed-loop control, and through the effective control of segment displacement self-diagnosis during the clutch operation process, performs variable clutch control and improve the actuator fault tolerance. Experiments show that the proposed control algorithm is feasible, which has effectively reduced the torque shock and clutch friction work during clutch engaging process.

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Acknowledgements

This work is supported by the National High Technology Research and Development Program of China (Grant No. 2011AA11A260).

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Authors and Affiliations

  1. School of Automotive Engineering, Wuhan University of Technology, Wuhan, 430070, China

    Bin Huang, Sen Wu, Song Huang & Xiang Fu

  2. DongFeng Motor Corp Technology Center, Wuhan, 430056, China

    Song Huang & Yunyun Yang

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  1. Bin Huang
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  2. Sen Wu
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  3. Song Huang
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  4. Xiang Fu
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  5. Yunyun Yang
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Correspondence to Xiang Fu.

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Huang, B., Wu, S., Huang, S. et al. Clutch control of a hybrid electrical vehicle based on neuron-adaptive PID algorithm. Cluster Comput 22 (Suppl 5), 12091–12099 (2019). https://doi.org/10.1007/s10586-017-1562-4

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  • DOI: https://doi.org/10.1007/s10586-017-1562-4

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