Electric Vehicle Tire Slip Identification Method Based on Self-Adaptive Kalman Filter

Abstract

Based on the traits of the four-wheel drive EV, a self-adaptive Kalman filter was applied to gain the information of vehicle velocity and traction forces on four wheels. With the estimated vehicle velocity and the driving forces on four wheels, the slope k of the curves of adhesion coefficient versus slip rate could be calculated and consequently the road adhesion condition could be accurately identified. Through calculating the difference between the estimated slope k with the optimized slope k as a control reference, the output torques of the motors were adjusted instantaneously to improve the using rate of the adhesion as well as the rationality of the traction control strategy. Simulation results illustrate that whether on high adhesion or low adhesion roads the vehicle is passing, the strategy can estimate the k effectively.

A road surface identification method for a four in-wheel-motor drive electric vehicle

“…The state variable is estimated in the Sage-Husa adaptive filtering algorithm added with forgetting factor (see Zhou et al, 2007). In the filtering calculation, on the one hand, the predicted value is corrected constantly by means of observation, and on the other hand, the unknown or uncertain system model parameters and noise statistic parameters are estimated and corrected.…”