Automatic transmission gear prediction based on personalised transmission gear modelling Online publication date: Fri, 13-Nov-2015
by Bin Shi; Jie Hu; Li Xu
International Journal of Vehicle Systems Modelling and Testing (IJVSMT), Vol. 10, No. 4, 2015
Abstract: Gear shifting strategy may affect fuel economy and driving ability, therefore, gear prediction algorithm becomes an important research topic for automatic transmission. In this work, we propose to predict the gear changes based on historical vehicle test data and driver's driving style. Firstly, a set of vehicle data that affect gear shifting are obtained using a dimension reduction algorithm, e.g., principal component analysis. This will greatly reduce the computational complexity of the system. Secondly, the dimension reduced data are applied to obtain the personalised transmission gear model (PTGM). This is accomplished by using the locally designed neural network, i.e., CMAC in this work. Finally, the driver style evaluation index is applied here as an auxiliary parameter to achieve the accuracy of the predictions. Simulations are conducted to verify the effectiveness of the proposed scheme.
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