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This paper presents sliding-mode-observer (SMO)-based adaptive sliding mode control (SMC) and neural network (NN) control for effective tracking of the slip ratio applicable to electric vehicles (EVs) and hybrid EVs (HEVs), where electric motors are used to achieve braking in addition to propulsion. The proposed SMO alleviates the difficulty in choosing its gains. To adapt the road condition parameter for better performance, a Lyapunov-based adaptation is integrated with the sliding-mode controller. The resulting adaptive controller performs very well in achieving slip tracking in the face of parameter uncertainties. Furthermore, to cope up with the uncertainties and unknown nonlinearity involved with the vehicle slip dynamics, a nonmodel-based NN controller is developed using the function approximation properties of the multilayer perceptrons.