For the purpose of regenerative braking control in hybrid and electrical vehicles, recent studies have suggested controlling the slip ratio of the electric-powered wheel. A slip tracking controller requires an accurate slip estimation in the overall range of the slip ratio (from 0 to 1), contrary to the conventional slip limiter (ABS) which calls for an accurate slip estimation in the critical slip area, estimated at around 0.15 in several applications. Considering that it is not possible to directly measure the slip ratio of a wheel, the problem is to estimate the latter from available online data. To estimate the slip of a wheel, both wheel speed and vehicle speed must be known. Several studies provide algorithms that allow obtaining a good estimation of vehicle speed. On the other hand, there is no proposed algorithm for the conditioning of the wheel speed measurement. Indeed, the noise included in the wheel speed measurement reduces the accuracy of the slip estimation, a disturbance increasingly significant at low speed and low torque. Herein, two different extended Kalman observers of slip ratio were developed. The first calculates the slip ratio with data provided by an observer of vehicle speed and of propeller wheel speed. The second observer uses an original nonlinear model of the slip ratio as a function of the electric motor. A sinus tracking algorithm is included in the two observers, in order to reject harmonic disturbances of wheel speed measurement. Moreover, mass and road uncertainties can be compensated with a coefficient adapted online by an RLS. The algorithms were implemented and tested with a three-wheel recreational hybrid vehicle. Experimental results show the efficiency of both methods. (C) 2016 Elsevier Ltd. All rights reserved.