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Model Based Control of Synchronizers for Reducing Impacts during Sleeve to Gear Engagement
Technical Paper
2019-01-1303
ISSN: 0148-7191, e-ISSN: 2688-3627
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English
Abstract
This paper presents a model based control strategy aimed to reduce noise and wear during gearshifts in conventional and hybrid Dual Clutch Transmissions (DCT and DCTH) and Automated Manual Transmissions (AMT). The control strategy is based on a newly developed dog teeth position sensor layout at China Euro Vehicle Technology AB (CEVT), a detailed simulation model for gear engagement and already existing speed sensors in the transmission. The details of dog teeth position sensor and simulation model are also presented in this paper. During gear shifting, noise is generated because of impacts between the sleeve teeth and the idler gear dog teeth after speed synchronization. Besides noise, these impacts are also responsible for delaying the completion of shift and contribute to wear in the dog teeth, hence reducing the lifespan of the transmission. The simulation model for gear engagement can simulate these impacts. Based on the simulation model and optimal control theory, an ideal dog teeth position trajectory is formulated that avoids the impact between sleeve and idler gear dog teeth, before the start of torque ramp up. The open loop strategy then controls the synchronization torque in the beginning of speed synchronization in such a way that the dog teeth position during shift follows the ideal dog teeth position trajectory. Since the control strategy is based on optimal control theory, its effect on speed synchronization time is minimal. The control strategy is designed in such a way that it can easily be applied in the existing transmission control software. By applying the control strategy on the simulation model, it is shown that the impacts during gear engagement are reduced.
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Authors
Citation
Piracha, M., Grauers, A., Barrientos, E., Budacs, H. et al., "Model Based Control of Synchronizers for Reducing Impacts during Sleeve to Gear Engagement," SAE Technical Paper 2019-01-1303, 2019, https://doi.org/10.4271/2019-01-1303.Data Sets - Support Documents
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References
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