Model-based current limiting for traction control of an electric four-wheel drive race car

This paper describes a novel traction control method and its application to an electric four-wheel driven race car. The proposed control method is based on a detailed model of tire dynamics and is designed for hardware with limited memory and computational power. We derive a linear parameter-varying model from first principles and validate it against a full nonlinear vehicle model. We then use the model to design a gain-scheduled LQRI controller, parametric on the measured vehicle velocity and lateral acceleration. We show that when incorporating additional information about the tire state, a gain scheduled LQRI controller is capable of minimizing excessive wheel spin by limiting the maximum torque available to the driver. This leads to a performance gain in acceleration while improving the handling characteristics of the race car. The proposed controller is thoroughly tested for its sensitivity to sensor noise and changes in system parameters in simulation and then implemented on a prototype race car competing in Formula Student. Experiments indicate satisfactory experimental performance from the initial control design without additional tuning of the controller parameters. This illustrates the simplicity of the design and ease of implementation.