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Parameter Identification of a Power loss Model for Vehicle Transmissions Based on Sensitivity Analysis
ISSN: 0148-7191, e-ISSN: 2688-3627
To be published on September 15, 2020 by SAE International in United States
As the transmission design directly impacts drive unite operation and power flow to the driveline, the transmission power loss is a critical target in the drivetrain development. The demand of more precise and more efficient power loss prediction has therefore increased significantly, which highlights the need of new methodologies in order to optimize the power loss model for vehicle transmissions. The possible power losses that exist in the power flow path, are gear mesh losses, gear churning losses, gear windage losses, bearing losses, synchronizer losses and sealing losses. Thanks to the decades of research, analytical models are available for the prediction of these component losses, which could deliver power loss distributions and overall efficiency maps of complex transmissions. The aim of this paper is to introduce a methodology to improve the accuracy of a chosen power loss model on a system level. A detailed power loss prediction for a two-speed transmission in an electric vehicle has been performed. The simulated overall power losses and the available experimental results match well. However, since many assumptions are made in the analytical modelling process, there are still deviations between the predicted and the measured results. In order to reduce the deviations, all uncertain parameters are firstly analyzed based on the parameter sensitivity analysis method FAST that allows determining the influential uncertain parameters. The sensitivities of those influential parameters are locally defined at all operating regions, by which the sensitive operating areas of all influential uncertain parameters to the overall power losses could be defined. The identification of these parameters at their sensitive regions prevents the unnecessary interference with other uncertain parameters at the identification process. With the help of identified parameters, a better proximity between the simulated and measured results is achieved.