The experimental verification of the optimal input design method for fault identification is performed using a scaled-down overactuated electric vehicle. In the previous study, online fault identification was achieved by utilizing all the characteristics of an overactuated system (Park and Park, 2016). The perturbation input signal for the actuator fault identification can be applied to the faulty actuators to suppress most of the control performance loss. The scaled-down vehicle contains four independent driving motors and four independent wheel steering motors to model an extremely overactuated system. The lateral velocity and yaw rate are estimated using the state observer to realize feedback control, and the cornering stiffness is determined based on the estimated values. Experimental verification is performed using steady state cornering maneuvers with sudden actuator faults. The experiments with the scaled-down vehicle support the performance of the optimal input design method. When sensor noise and modeling uncertainties exist, the results from our method were much more precise than the results obtained using the conventional white noise perturbation input signal.