TY - JOUR
AU - 
AB -  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. Keywords: personalised transmission gear model; PTGM; vehicle test data; principal component analysis; PCA; cerebellar model articulation controller; driving style; vehicle speed; throttle position; neural network; automatic transmission; gear prediction. Reference to this paper should be made as follows: Shi, B., Hu, J. and Xu, L. (2015) ` on personalised transmission gear modelling', Int. J. Vehicle Systems Modelling and Testing, Vol. 10, No. 4, pp.356­365. 
TI - Automatic transmission gear prediction based on personalised transmission gear modelling
JO - International Journal of Vehicle Systems Modelling and Testing
DO - 10.1504/IJVSMT.2015.073041
DA - 2015-01-01
UR - https://www.deepdyve.com/lp/inderscience-publishers/automatic-transmission-gear-prediction-based-on-personalised-u5E1nnkun0
SP - 356
EP - 365
VL - 10
IS - 4
DP - DeepDyve
ER -