Direct yaw moment control (DYC) is an effective way to alter the behaviour of electric cars with independent drives. Controlling the torque applied to each wheel can improve the handling performance of a vehicle making it safer andfaster on a race track. The state-of-the-art literature covers the comparison of various controllers (PID, LPV, LQR, SMC, etc.) using ISO manoeuvres. However, more advanced comparison on important characteristics of the controllers performance is missed, such as the robustness of the controllers under changes in the vehicle model, steering behaviour, use of the friction circle and, ultimately, lap time on a track. In this study, we have compared the controllers according to some of the aforementioned parameters on a modelled race car. Interestingly, best lap times are not provided by perfect neutral or close-to-neutral behaviour of the vehicle, but rather by allowing certain deviations from the target yaw rate. In addition, a modified PID controller showed that its performance is comparable to other more complex control techniques such as MPC.

Direct Yaw Moment Control (DYC) is an effective way to alter the behaviour of electric cars with independent drives. Controlling the torque applied to each wheel can improve the handling performance of a vehicle making it safer and faster on a race track. The state-of-the-art literature covers the comparison of various controllers (PID, LPV, LQR, SMC, etc.) using ISO manoeuvres. However, a more advanced comparison of the important characteristics of the controllers’ performance is lacking, such as the robustness of the controllers under changes in the vehicle model, steering behaviour, use of the friction circle, and, ultimately, lap time on a track. In this study, we have compared the controllers according to some of the aforementioned parameters on a modelled race car. Interestingly, best lap times are not provided by perfect neutral or close-to-neutral behaviour of the vehicle, but rather by allowing certain deviations from the target yaw rate. In addition, a modified Proportional Integral Derivative (PID) controller showed that its performance is comparable to other more complex control techniques such as Model Predictive Control (MPC).

Vehicle simulators are widely used devices in disciplines such as Formula 1, design of commercial vehicles, military or civil flights, etc. They enable the evaluation of several aspects of a vehicle during the design phase. Driving simulators are composed of a hardware, with which a driver interacts, and a software that contains the mathematical model of the car and the track, being also the responsible for solving the physics between both parties. An important part of this set is the interaction with the circuit where the car should run, especially if it is a real track-based model. In this work a method is proposed for modeling a circuit that involves a low economic cost its integration into a professional simulator located in the Automotive Intelligence Centre – AIC, Amorebieta, Basque Country, Spain. The 3D model presented corresponds to the Vilariño Motorsport Circuit (Olaberria), obtained by aerial photogrammetry with a drone achieving a resolution of 3.4 mm / px) and a maximum error of 50 mm being also a fast and low-cost method with respect to capture systems with LIDAR. Keywords: Driving Simulator, Formula Student, Arial Photogrammetry, Virtual Environment

Electrification has drastically changed the way road car are designed. The floor of the vehicle chassis has substantially changed to contain the batteries, and electric motors can be placed practically inline to each axle. Furthermore, the compactness of the electric motors permits a layout where each wheel is powered by a single motor. Independently powering each vehicle’s wheel brings several advantages in terms of manoeuvrability and stability. Torque vectoring refers to the algorithm that utilizes these independent motors to alter the yaw of the vehicle for the aforementioned purpose. In this PhD dissertation, a torque vectoring algorithm is designed for a race car, following the V-shape development framework, widely used in the automotive industry. Race cars differ in many aspects from road cars, they generally have stiffer suspension, lower centre of gravity, grippy tyres, etc. They also feature a more neutral behaviour compared to the generally understeering behaviour or road cars. In this way, the front axle can generate higher lateral force, vehicle may increase its lateral acceleration, and therefore the car can negotiate faster the corners. However, this decrement on the understeering gradient may also suppose a decrease on its stability margin, so the car is trickier to drive. In this work, the torque vectoring is applied on a vehicle which features a close-to-neutral balance (therefore a with a little margin to increase its steady state lateral acceleration), aiming to keep this balance most of the times and preventing the vehicle from becoming unstable for the driver. Besides, since the algorithm needs to work in harmony with the driver -especially given the fact that the vehicle will presumably be driven at the limit handling area- the algorithm is designed and tested in conjunction to a human driver using a Human-In-the-Loop system.