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Modeling and Parameter Estimation of Automatic Transmission for Heavy-Duty Vehicle Using Dual Clutch Scheme
Technical Paper
2020-01-2242
ISSN: 0148-7191, e-ISSN: 2688-3627
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English
Abstract
This paper focuses on modeling of the heavy-duty vehicle drivetrain with automatic transmission by using dual clutch scheme. The planetary gear set in the automatic transmission is complicated structure and difficult to understand. The advantage of the dual clutch scheme is that it can be used to represent the complex planetary gear set intuitively, which is a great help to understand the gear shifting process. It is also suitable for being used in the controller due to its low order. Some conditions are required to convert the planetary gear set to the dual clutch model. The heavy-duty vehicle driveline can be converted to the dual clutch model due to its heavy engine and vehicle inertia.
This paper also proposes system parameter estimation methods to represent the driveline model. The main parameters are lumped inertia, lumped gear efficiency, output shaft compliance and friction coefficient of clutches. First, a method for estimating lumped inertia and lumped gear efficiency is proposed using WLSE (Weighted Least Square Estimation) when gear is engaged. Second, resonance frequency of the system is obtained from the lock-up oscillation data occurring at the end of the gear shifting. The output shaft compliance is calculated by analyzing resonance frequency of the system. Third, the slip and friction coefficients of the clutch over time are calculated in the inertia phase. Using those data, the relationship between the dynamic friction coefficients and slip of the wet clutch can be obtained.
Finally, a simulation is constructed to verify the accuracy of the proposed dual clutch model and the estimated system parameters. Simulation result is compared with experimental data.
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Lee, T. and Choi, S., "Modeling and Parameter Estimation of Automatic Transmission for Heavy-Duty Vehicle Using Dual Clutch Scheme," SAE Technical Paper 2020-01-2242, 2020, https://doi.org/10.4271/2020-01-2242.Data Sets - Support Documents
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References
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