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

As an important vehicle state parameter, automotive mass has important reference value to the safety performance and comfort of automobiles. Current researches mostly focus on optimizing the established longitudinal dynamics model or improving the algorithm to reduce the recognition error. However, it often neglects that the longitudinal vibration caused by different driving speeds is very different, so the recognition rate is low under various complicated working conditions. In this paper, the speed decoupling model is firstly established to study the interference caused by the longitudinal vibration of the vehicle during the dynamic quality recognition. At the same time, the horizontal speed is decomposed from the combined speed. Then, several real vehicle tests are carried out at different gear speeds. The obtained gear speeds are decoupled and the results are brought into the longitudinal dynamics model. And the quality parameters are estimated by means of recursive least squares algorithm. The estimated value obtained is compared with the estimation result not be decoupled. The results show that the quality parameter estimates obtained by eliminating the longitudinal vehicle speed through speed decoupling are closer to the true value and the average relative error is reduced by 8.57% compared with the original. The speed decoupling model established in this paper reduces the requirements of the roadway conditions based on the traditional dynamics quality identification method and reduces the volatility of the acquisition parameters, which makes the identification method more common.