An Advanced Automatic Transmission with Interlocking Dog Clutches: High-Fidelity Modeling, Simulation and Validation

2017-01-1141

03/28/2017

Event
WCX™ 17: SAE World Congress Experience
Authors Abstract
Content
Fuel economy regulations have forced the automotive industry to implement transmissions with an increased number of gears and reduced parasitic losses. The objective of this research is to develop a high fidelity and a computationally efficient model of an automatic transmission, this model should be suitable for controller development purposes. The transmission under investigation features a combination of positive clutches (interlocking dog clutches) and conventional wet clutches. Simulation models for the torque converter, lock-up clutch, transmission gear train, interlocking dog clutches, wet clutches, hydraulic control valves and circuits were developed and integrated with a 1-D vehicle road load model. The integrated powertrain system model was calibrated using measurements from real-world driving conditions. Unknown model parameters, such as clutch pack clearances, compliances, hydraulic orifice diameters and clutch preloads were estimated and calibrated. Simulation results, such as vehicle acceleration, turbine speed, and output shaft speed, are reported and compared with the measured data to validate the transmission model. Subsequently, the transmission model was coupled with internal combustion engine and road load models. This arrangement permitted investigating the dog clutch engagement dynamics under transient conditions. The relative speed of the dog clutch halves was found to be highly sensitive to the transmission input torque, which indicates that a precise engine torque control schemes are necessary for successful engagement.
Meta TagsDetails
DOI
https://doi.org/10.4271/2017-01-1141
Pages
8
Citation
Alzuwayer, B., Prucka, R., Haque, I., and Venhovens, P., "An Advanced Automatic Transmission with Interlocking Dog Clutches: High-Fidelity Modeling, Simulation and Validation," SAE Technical Paper 2017-01-1141, 2017, https://doi.org/10.4271/2017-01-1141.
Additional Details
Publisher
Published
Mar 28, 2017
Product Code
2017-01-1141
Content Type
Technical Paper
Language
English