High precision state estimation is crucial when executing drift control and high speed control close to the stability limit, on electric RC scale cars. In this thesis the estimation is made possible through recursive Bayesian filtering; more precisely the Extended Kalman Filter. By modelling the dynamics of the car and using it together with position measurements and control input signals, it is possible to do state estimation and prediction with high accuracy even on non-measured states. Focus is on real-time, on-line, estimation of the so called slip angles of the front and rear tyres, because of their impact of the car’s behaviour. With the extended information given to the system controller, higher levels of controllability could be reached. This can be used not only for higher speeds and drift control, but also a possibility to study future anti-skid safety measures forground vehicles.