Reliability of Extended Kalman Filtering Technic on Vehicle Mass Estimation - Mert BÜYÜKKÖPRÜ; Erdem UZUNSOY (Journal of Innovative Science and Engineering, 2021)
In this paper, vehicle mass estimation problem was researched by the Kalman Filtering process to investigate the effectiveness of Extended Kalman Filter technique on mass estimation, which has widely been studied on the related literature with all the successful results. For the purpose, a longitudinal vehicle dynamics model was developed, and the equations were transformed into the well-known state space form. Nonlinear problem in its nature was discretised and linearized by using Euler method. In addition, road slope was calculated by a road slope inclinometer logic. IPG CarMaker® was utilized to overcome the nonlinearities, design the test environment and the conditions, as it was observed that the simulation results were not met the expectations with the logged vehicle physical test data. Several test scenarios were run to simulate level and wavy road with different vehicle speeds. The results showed that the Extended Kalman Filter could provide a reasonable convergence when the initial values were correctly selected, no nonlinear issue occurred and certain tuning parameters were defined with stable boundary conditions for mass estimation.