Optimization of Shifting Schedule of Vehicle Coasting Mode Based on Dynamic Mass Identification

Correct shifting schedule of vehicle coasting mode play a vital role in improving vehicle comfort and economy. At present, the calibration of the transmission shifting schedule ignores the impact of vehicle’s dynamic mass. This paper proposes a method for optimizing the shifting schedule of the coasting modes with gear based on the dynamic mass identification of the vehicle. This method identifies the dynamic mass of the vehicle during driving and substitute them into the process of solving the shifting schedule parameters. Then we get the optimal shifting schedule. At first, establish the Extended Kalman Filter to Pre-process the experimental data, reducing errors caused by excessive data fluctuations. Then, establishing a weighted squares estimation model based on particle swarm optimization to identify the dynamic mass of the vehicle. Finally, combined with the results of mass identification, the optimal shifting speed interval between each vehicle gears is solved by using the dynamic three-parameter shift law. In this paper, Dong Feng S30 is used for experiments. The results show that the average relative error of vehicle dynamic mass identification is controlled within 1.28% and the relative fluctuation degree of identification results is controlled within 4% under normal driving conditions in each gear position. On this basis, the obtained optimal shifting speed interval of the vehicle coasting mode with gear has higher feasibility and accuracy.