关键词: 动力换挡, 换挡品质, 湿式离合器, 油压分段控制, 遗传算法

Abstract: Powershift technology has been widely used in heavy tractors in recent years due to its advantages of uninterrupted power during shifting. As one of the core parts of the power shift transmission, the electrohydraulic control system is responsible for cooling and lubrication as well as shifting operations, and its control process has a direct impact on vehicle performance and shift quality. It is of great significance to put forward a reasonable control strategy of wet clutch oil pressure for improving and optimizing the shifting quality of power shift gearbox. In this paper, the sliding friction, shift time and jerk are selected as the evaluation indicators of shift quality. The relationship between the three indicators and the electrohydraulic control system of the PST was established through formula deduction. According to the output characteristics of the wet clutch during the shifting process, the power shifting process was divided into four stages: the original gear phase, the torque phase, the inertia phase, and the new gear phase. Combined with the shift evaluation indicators, a wet clutch hydraulic control strategy was proposed. The process of shifting 1st gear to 2nd gear was taken as an example. The corresponding Statechart control model was established in the AMESim simulation platform. The genetic algorithm was used to optimize the key parameters in the control model, and then run the simulation which the parameter optimization results were applied. Compared with the simple control strategy, the application of this control strategy could reduce the maximum jerk during the shift from 22.14 m/s3 to 9.78 m/s3. At the same time, the sliding friction per unit area of the wet clutch friction plate was 0.163 J/mm2 which was less than the allowable value. The effectiveness and rationality of the optimized control strategy for improving the shift quality were verified.

Key words: powershift, shift quality, wet clutch, hydraulic pressure segment control, genetic algorithm