Rear wheel torque vectoring model predictive control with velocity regulation for electric vehicles

In this paper we propose a constrained optimal control architecture for combined velocity, yaw and sideslip regulation for stabilisation of the vehicle near the limit of lateral acceleration using the rear axle electric torque vectoring configuration of an electric vehicle. A nonlinear vehicle and tyre model are used to find reference steady-state cornering conditions and design two model predictive control (MPC) strategies of different levels of fidelity: one that uses a linearised version of the full vehicle model with the rear wheels' torques as the input, and another one that neglects the wheel dynamics and uses the rear wheels' slips as the input instead. After analysing the relative trade-offs between performance and computational effort, we compare the two MPC strategies against each other and against an unconstrained optimal control strategy in Simulink and Carsim environment

Model Predictive torque vectoring control for electric vehicles near the limits of handling

In this paper we propose a constrained optimal control architecture to stabilize a vehicle near the limit of lateral acceleration using the rear axle electric torque vectoring configuration of an electric vehicle. A nonlinear vehicle and tyre model is employed to find reference steady-state cornering conditions as well as to design a linear Model Predictive Control (MPC) strategy using the rear wheels' slip ratios as input. A Sliding Mode Slip Controller then calculates the necessary motor torques according to the requested wheel slip ratios. After analysing the relative trade-offs between performance and computational effort for the MPC strategy, we validate the controller and compare it against a simpler unconstrained optimal control strategy in a high fidelity simulation environment

A torque vectoring optimal control strategy for combined vehicle dynamics performance enhancement and electric motor ageing minimisation*

In this paper we propose a control architecture that combines velocity, sideslip angle and yaw rate regulation with motor temperature regulation on a electric vehicle with four independent electric motors. The linear controller incorporates both the vehicle dynamics and the electric motor dynamics by combining a four-wheel vehicle model with a motor degradation model. It is found that the resulting controller not only enhances the vehicle stability of the vehicle, but also extends the lifetime of motors by regulating their temperatures

Integration of torque blending and slip control using nonlinear model predictive control

Antilock Braking System (ABS) is an important active safety feature in preventing accidents during emergency braking. Electrified vehicles which include both hydraulic and regenerative braking systems provide the opportunity to implement brake torque blending during slip control operation. This study evaluates the design and implementation of a new torque allocation algorithm using a Nonlinear Model Predictive Control (NMPC) strategy that can run in real-time, with results showing that wheel-locking can be prevented while also permitting for energy recuperation

Analysis and Control of High-Speed Wheeled Vehicles

In this work we reproduce driving techniques to mimic expert race drivers and obtain the open-loop control signals that may be used by auto-pilot agents driving autonomous ground wheeled vehicles. Race drivers operate their vehicles at the limits of the acceleration envelope. An accurate characterization of the acceleration capacity of the vehicle is required. Understanding and reproduction of such complex maneuvers also require a physics-based mathematical description of the vehicle dynamics. While most of the modeling issues of ground-vehicles/automobiles are already well established in the literature, lack of understanding of the physics associated with friction generation results in ad-hoc approaches to tire friction modeling. In this work we revisit this aspect of the overall vehicle modeling and develop a tire friction model that provides physical interpretation of the tire forces. The new model is free of those singularities at low vehicle speed and wheel angular rate that are inherent in the widely used empirical static models. In addition, the dynamic nature of the tire model proposed herein allows the study of dynamic effects such as transients and hysteresis. The trajectory-planning problem for an autonomous ground wheeled vehicle is formulated in an optimal control framework aiming to minimize the time of travel and maximize the use of the available acceleration capacity. The first approach to solve the optimal control problem is using numerical techniques. Numerical optimization allows incorporation of a vehicle model of high fidelity and generates realistic solutions. Such an optimization scheme provides an ideal platform to study the limit operation of the vehicle, which would not be possible via straightforward simulation. In this work we emphasize the importance of online applicability of the proposed methodologies. This underlines the need for optimal solutions that require little computational cost and are able to incorporate real, unpredictable environments. A semi-analytic methodology is developed to generate the optimal velocity profile for minimum time travel along a prescribed path. The semi-analytic nature ensures minimal computational cost while a receding horizon implementation allows application of the methodology in uncertain environments. Extensions to increase fidelity of the vehicle model are finally provided.Ph.D.Committee Chair: Dr. Panagiotis Tsiotras; Committee Member: Dr. Amy Pritchett; Committee Member: Dr. David Taylor; Committee Member: Dr. Olivier Bauchau; Committee Member: Dr. Wassim Hadda

A real-time nonlinear model predictive control strategy for stabilisation of an electric vehicle at the limits of handling

In this paper, we propose a real-time nonlinear model predictive control (NMPC) strategy for stabilization of a vehicle near the limit of lateral acceleration using the rear axle electric torque vectoring configuration of an electric vehicle. A nonlinear four-wheel vehicle model that neglects the wheel dynamics is coupled with a nonlinear tire model to design three MPC strategies of different levels of complexity that are implementable online: one that uses a linearized version of the vehicle model and then solves the resulting quadratic program problem to compute the necessary longitudinal slips on the rear wheels, a second one that employs the real-time iteration scheme on the NMPC problem, and a third one that applies the primal dual interior point method on the NMPC problem instead until convergence. Then, a sliding mode slip controller is used to compute the necessary torques on the rear wheels based on the requested longitudinal slips. After analyzing the relative tradeoffs in performance and computational cost between the three MPC strategies by comparing them against the optimal solution in a series of simulation studies, we test the most promising solution in a high-fidelity environment

On Steady-State Cornering Equilibria for Wheeled Vehicles with Drift

In this work we derive steady-state cornering conditions for a single-track vehicle model without restricting the operation of the tires to their linear region (i.e. allowing the vehicle to drift). For each steady-state equilibrium we calculate the corresponding tire friction forces at the front and rear tires, as well as the required front steering angle and front and rear wheel longitudinal slip, to maintain constant velocity, turning rate and vehicle sideslip angle. We design a linear controller that stabilizes the vehicle dynamics with respect to the steady-state cornering equilibria using longitudinal slip at the front and the rear wheels as the control inputs. The wheel torques necessary to maintain the given equilibria are calculated and a sliding-mode controller is proposed to stabilize the vehicle using only front and rear wheel torques as control inputs

Feedback brake distribution control for minimum pitch

The distribution of brake forces between front and rear axles of a vehicle is typically specified such that the same level of brake force coefficient is imposed at both front and rear wheels. This condition is known as ‘ideal’ distribution and it is required to deliver the maximum vehicle deceleration and minimum braking distance. For subcritical braking conditions, the deceleration demand may be delivered by different distributions between front and rear braking forces. In this research we show how to obtain the optimal distribution which minimises the pitch angle of a vehicle and hence enhances driver subjective feel during braking. A vehicle model including suspension geometry features is adopted. The problem of the minimum pitch brake distribution for a varying deceleration level demand is solved by means of a model predictive control (MPC) technique. To address the problem of the undesirable pitch rebound caused by a full-stop of the vehicle, a second controller is designed and implemented independently from the braking distribution in use. An extended Kalman filter is designed for state estimation and implemented in a high fidelity environment together with the MPC strategy. The proposed solution is compared with the reference ‘ideal’ distribution as well as another previous feed-forward solution

Driver Control Actions in High-Speed Circular Driving

In this pilot study we investigate driver control actions during high speed cornering with a rear wheel drive vehicle. Six drivers were instructed to perform the fastest maneuvers possible around a marked circle, while trying to retain control of the vehicle and constant turning radius. The data reveal that stabilization of the vehicle is achieved with a combination of steering and throttle regulation. The results show that the drivers used steering control to compensate for disturbances in yaw rate and sideslip angle. Vehicle accustomed drivers had the most consistent performance resulting in reduced variance of task metrics and control inputs

Real-time path-tracking MPC for an over-actuated autonomous electric vehicle

This paper illustrates the development of a nonlinear constrained predictive path-tracking controller, including realistic vehicle dynamics and multiple actuator inputs and its implementation in real time on an experimental vehicle platform. The controller is formulated for a particular over-actuated vehicle equipped with Torque Vectoring (TV) as well as All-Wheel-Steering (AWS) functionalities, which allow for the enhanced control of vehicle dynamics. The proposed Nonlinear Model Predictive Controller (NMPC) takes into account the nonlinearities in vehicle dynamics across the range of operation up to the limits of handling as dictated by the adhesion limits of the tyres. In addition, crucial constraints regarding the actuators’ physical limits are included in the formulation. The performance of the controller is demonstrated in a high fidelity simulation environment, as well as in real-time on a test vehicle, during the execution of demanding driving scenarios