2019/04/09 by Daghan Dogan, Dogan, Daghan, Pınar Boyraz +1
Engineering · #FOS: Electrical engineering #Hydraulic and Pneumatic Systems #Railway Engineering and Dynamics #Signal Processing (eess.SP) #Soil Mechanics and Vehicle Dynamics #Systems and Control (eess.SY) #Vehicle Dynamics and Control Systems #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.1904.04504
openalex publication_date 2019/04/09 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
The application of traction control systems (TCS) for electric vehicles (EV)\nhas great potential due to easy implementation of torque control with\ndirect-drive motors. However, the control system usually requires road-tire\nfriction and slip-ratio values, which must be estimated. While it is not\npossible to obtain the first one directly, the estimation of latter value\nrequires accurate measurements of chassis and wheel velocity. In addition,\nexisting TCS structures are often designed without considering the robustness\nand energy efficiency of torque control. In this work, both problems are\naddressed with a smart TCS design having an integrated acoustic road-type\nestimation (ARTE) unit. This unit enables the road-type recognition and this\ninformation is used to retrieve the correct look-up table between friction\ncoefficient and slip-ratio. The estimation of the friction coefficient helps\nthe system to update the necessary input torque. The ARTE unit utilizes machine\nlearning, mapping the acoustic feature inputs to road-type as output. In this\nstudy, three existing TCS for EVs are examined with and without the integrated\nARTE unit. The results show significant performance improvement with ARTE,\nreducing the slip ratio by 75% while saving energy via reduction of applied\ntorque and increasing the robustness of the TCS.\n