Performance analysis and optimisation of lock valve for heavy-duty automatic transmission

ABSTRACT

The lock valve plays an important role during normal shift and power failure in heavy-dutyautomatic transmissions. A new kind of lock valve was designed in this paper, and the main purpose of this paper is to improve the working performance of the lock valve. Firstly, the AMESim simulation model of the lock valve was built based on the analysis of the working circuit of the lock valve, and the influence of several design parameters on the lock valve working performance were analysed, which provided a reliable theoretical basis for the subsequent optimisation research. Additionally, thecomplete second-orderresponse surface model between design parameters and output oil pressure response time and control oil pressure fluctuation is established based on ISE and ITAE criteria. Finally, the particle swarm optimisation(PSO) algorithm and Box-Behnkenresponse surface methodology were applied to the optimisation of the mathematical model. The optimisation results have shown that the two optimisation methods can significantly improve the output oil pressure response speed of the lock valve, but the optimisation results of Box-Behnkenresponse surface methodology are more stable than the PSO algorithm in the control oil pressure, and the growth fluctuation is smaller.

KEYWORDS:

• Heavy-dutyautomatic transmissions
• lock valve
• AMESim
• particle swarm optimisation(PSO) algorithm
• box-behnkenresponse surface methodology

Acknowledgments

This work was supported by the National Natural Science Foundation of China (Grant No. 51965011), Science and Technology Innovation Team Project in Guizhou Province (Grant No. Q.K.H.P.T.R.C[2020]5020), Research Project on Innovation Group of Guizhou Provincial Education Department(Grant No. Q.J.H. KY Z. [2018]011) and Training Plan for High-levelInnovative Talent in Guizhou Province (Grant No. Q.K.H.P.T.R.C[2016]5659).

Disclosure statement

No potential conflict of interest was reported by the author(s).

Statement

Some or all data, models, or code generated or used during the study are available in a repository or online in accordance with funder data retention policies (DOI: 10.5281/zenodo.4588326).

Additional information

Funding

This work was supported by the National Natural Science Foundation of China [Grant No. 51965011].