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Performance Analysis of an Automated Manual Transmission Controller for Two-Wheeler
ISSN: 0148-7191, e-ISSN: 2688-3627
Published October 11, 2019 by SAE International in United States
This content contains downloadable datasetsAnnotation ability available
Event: International Conference on Advances in Design, Materials, Manufacturing and Surface Engineering for Mobility
This research paper presents controller development and its performance analysis for two wheeler. The comparison for different performance parameters is carried out with and without automated manual transmission (AMT) controller. The AMT considered in this case, includes a conventional manual transmission gear-box with conventional clutch pedal. It is equipped with clutch actuation with the help of linear actuator and the gear shifting using servo motor. The upshift and downshifting of the gears and the clutch actuation is done through the decisions of controller. The results generated during the engine dynamometer test are used as input creating two wheeler vehicle model using AVL cruise software. This vehicle model is used to predict vehicle performance. The vehicle performance results are validated with chassis dynamometer test data. The vehicle model is modified for generation of gear shifting plot. Also Gear shift program (GSP) is generated using the GSP generation module of AVL cruise software. The two criteria used for GSP generation are fuel economy and vehicle acceleration performance. The simulation results include generation of gear shifting graph which covers range from 0-120 kmph. The hysteresis zones are established during up shift and downshift, based on actuator and sensor limitations. The automated manual transmission controller algorithm has used the input of two parameters which are vehicle speed and throttle position. The simulation shows 4% reduction with generated gear shift plot.
CitationPatil, S. and Wani, K., "Performance Analysis of an Automated Manual Transmission Controller for Two-Wheeler," SAE Technical Paper 2019-28-0125, 2019, https://doi.org/10.4271/2019-28-0125.
Data Sets - Support Documents
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