Traction and Stability Control using Fuzzy-based Controller Integration for Electric Vehicles

TL;DR

This paper presents a fuzzy-based control system for electric vehicle traction and stability, integrating slip ratio and yaw control to improve vehicle handling under adverse road conditions.

Contribution

It introduces a novel fuzzy inference system that combines traction and yaw stability controls for electric vehicles with program-controlled rear motors.

Findings

Root-mean-square slip ratio error reduced by 96.14%.

Yaw rate error reduced by over 86% in simulations.

Effective control under various driving conditions.

Abstract

Adverse road conditions can cause vehicle yaw instability and loss of traction. To compensate for the instability under such conditions, corrective actions must be taken. In comparison to a mechanical differential, an electronic differential can independently control the two drive wheels and provide means of generating more effective corrective actions. As a solution for traction and stability issues in automobiles, this study has developed a controller for a vehicle electronic differential consisting of two program-controlled rear motors. The control algorithm adjusts to changing road conditions. Traction was controlled using a motor reaction torque observer-based slip ratio estimation, and yaw stability was achieved by tracking a reference yaw rate calculated using estimated tyre cornering stiffnesses. A recursive least squares algorithm was used to estimate cornering stiffness. The yaw rate of the vehicle, as well as its longitudinal and lateral accelerations, were measured, and the body slip angle was estimated using an observer. A fuzzy inference system was used to integrate the independently developed traction control and yaw control schemes. The fuzzy inference system modifies the commanded voltage generated by the driver's input to account for the traction and yaw stability controller outputs. A vehicle simulator was used to numerically simulate the integrated controller. For the racetrack simulation, the root-mean-square slip ratio error reduced by 96.14% and the RMS yaw rate error reduced by 88.17%. For the double lane change test at 40 km/h and 100 km/h, the RMS yaw rate error reduced by 86.96% and 92.34%, respectively.