Torque Vectoring Research Articles

On Nonlinear Model Predictive Control for Energy-Efficient Torque-Vectoring

A recently growing literature discusses the topics of direct yaw moment control based on model predictive control (MPC), and energy-efficient torque-vectoring (TV) for electric vehicles with multiple powertrains. To reduce energy consumption, the available TV studies focus on the control allocation layer, which calculates the individual wheel torque levels to generate the total reference longitudinal force and direct yaw moment, specified by higher level algorithms to provide the desired longitudinal and lateral vehicle dynamics. In fact, with a system of redundant actuators, the vehicle-level objectives can be achieved by distributing the individual control actions to minimize an optimality criterion, e.g., based on the reduction of different power loss contributions. However, preliminary simulation and experimental studies – not using MPC – show that further important energy savings are possible through the appropriate design of the reference yaw rate. This paper presents a nonlinear model predictive control (NMPC) implementation for energy-efficient TV, which is based on the concurrent optimization of the reference yaw rate and wheel torque allocation. The NMPC cost function weights are varied through a fuzzy logic algorithm to adaptively prioritize vehicle dynamics or energy efficiency, depending on the driving conditions. The results show that the adaptive NMPC configuration allows stable cornering performance with lower energy consumption than a benchmarking fuzzy logic TV controller using an energy-efficient control allocation layer.

A Super-twisting Controller for Active Control of Ground Vehicles with Lateral Tire-road Friction Estimation and CarSim Validation

In this paper, a ground vehicle equipped with Active Front Steering (AFS) and Rear Torque Vectoring (RTV) is considered. The AFS, actuated through the front tires, adds an incremental steer angle on top of the driver’s input, whereas the RTV, actuated through the rear tires, imposes a yaw torque to the vehicle. These actuators allow the active control of the vehicle chassis, so that a feasible and safe reference trajectory can be tracked. To obtain such a feasible reference generation and an efficient control action, the lateral tire-road friction coefficient has to be estimated. To this aim, in this paper the lateral tire-road friction coefficient is estimated in finite-time by means of a high-order sliding mode differentiator. Then, based on this estimation, a high-order sliding mode controller is designed to track the desired references. The performance of the dynamic controller is evaluated using a CarSim virtual vehicle, and the simulation results highlight the characteristics of the proposed observer-based control.

Torque vectoring system design for hybrid electric–all wheel drive vehicle

A torque vectoring system is designed for the hybrid electric–all wheel drive vehicle where the front and rear wheels are powered by the combustion engine and electric motors, respectively. The vehicle provides enhanced handling performance by a twin motor drive unit that can distribute the driving and regenerative braking torques to the rear-left and rear-right wheels independently. Based on the driver’s intention, a sliding mode controller is designed to calculate the desired traction force and yaw moment for the vehicle. The force distribution between the front and rear axles is investigated considering the principle of the friction circle, and characteristics of the engine and drive motors. The vertical tire force is estimated using the random walk Kalman filter for the proportional distribution between the front and rear longitudinal forces. For the torque distribution between the rear-left and rear-right wheels, an optimization problem is formulated by considering the constraints of the friction circle and motor characteristics. The proposed algorithm is evaluated in a simulation environment first by reflecting the characteristics of the hybrid electric–all wheel drive modules. Then, the test vehicle is utilized to validate the handling performance experimentally and to compare with the uncontrolled cases.

Torque Vectoring Algorithm of Electronic-Four-Wheel Drive Vehicles for Enhancement of Cornering Performance

This paper introduces a new torque vectoring (TV) algorithm with regards to enhancing the cornering performance of the electronic-four-wheel drive (e-4WD) vehicles. The proposed TV algorithm aims to distribute the in-wheel motor (IWM) torques to the left and right wheels assisting the vehicle in following the driver's intended trajectories. The proposed TV algorithm first included a yaw rate controller reference for neutral-steering which leads to the improvement of vehicle cornering agility. Thereafter, in-vehicle sensors and a standalone global positioning system (GPS) combined with a smooth sliding mode controller (SMC) were used based on a vehicle bicycle model to generate desired yaw moment values to track the reference. Finally, a novel torque distribution algorithm using the Daisy-chaining allocation, which is suitable for redundant actuator configuration, was utilized. Noteworthy, the Daisy-chaining allocation algorithm takes into account a torque operation area with the characteristics of IWM. In this manner, based on the vehicle bicycle model and using the CARSIM interface, the simulations, evaluation and verifications of the proposed control technique were done. Thereafter, considering a real-car based experiments with various driving scenarios, the effectiveness of the proposed TV algorithm was evaluated. It was confirmed that some evaluation factors in terms of the cornering performance were improved. The following main contributions makes the proposed TV algorithm be a meaningful solution to enhance the cornering performance of e-4WD vehicles: 1) improvement of both smoothness and convergence rate of control action, 2) a practical and intuitive way to distribute IWM torques, and 3) high applicability to mass-produced vehicles.

Mathematical modelling of torque vectoring differentials

Active safety systems which distribute forces between the wheels are an integral part of a modern vehicle. The vast majority of systems are based on the brake systems, and reducing engine power, which certainly reduces the vehicle speed. However, the needs of car owners are constantly changing, there is a need for devices capable of ensuring the distribution of traction between the wheels without having to reduce speed. With this task perfectly cope with the main transmission containing the mechanisms of power distribution (MPD). Power distribution mechanisms have a greater impact on vehicle handling. More often meet term, Torque Vectoring Differential (TVD). They began to be used for the first time since the mid-1990s, and found their application in sports cars, and then in executive cars. The described main gears are an integral part of dynamic stabilization systems. During the vehicle development, these systems are developed and investigated using mathematical modeling methods. Therefore, the paper describes the design, operation principle and kinematic design of the most common. The equations systems describing the dynamics of the links and the mechanism as a whole are also composed. The mathematical modeling of vehicles equipped with TVD is modelled.

Torque Vectoring for High-Performance Electric Vehicles: An Efficient MPC Calibration

In electric vehicles with in-wheel motors, torque vectoring is an effective technology to reach high-level performance in sport driving conditions. To vary the torque at each wheel while guaranteeing proper safety constraints, Model Predictive Control (MPC) has been shown to be the most suited strategy. However, MPC requires simple models and significant tuning effort to be really effective. In this letter, we propose a simple predictive grey-box model of the vehicle dynamics and an efficient calibration strategy to tune the MPC cost with little additional experimental effort. Results on a full-fledged multi-body simulator complete this letter.

An Adaptive Backstepping Sliding Mode Controller to Improve Vehicle Maneuverability and Stability via Torque Vectoring Control

To improve the maneuverability and stability of a vehicle and fully leverage the advantages of torque vectoring technology in vehicle dynamics control, a finite-time yaw rate and sideslip angle tracking controller is proposed by combining a second-order sliding mode (SOSM) controller with the backstepping method in this paper. However, existing research indicates that first-order sliding mode (FOSM) control suffers from the chattering problem, while the traditional SOSM controller requires knowing the bound of the uncertain term in advance to obtain the switching gain, which is difficult in practice. To address these problems, this paper proposes an adaptive second-order sliding mode (ASOSM) controller based on the backstepping method by adding the high-frequency switching term to the first derivative of the sliding mode variable, which implies that the actual control can be acquired after an integration process. The switching gain in the ASOSM controller is obtained by an adaptive algorithm without knowing any information of the uncertainty. The proposed algorithm is compared with FOSM and SOSM in different scenarios to demonstrate its applicability and robustness. Simulation results show that the bandwidth of the vehicle transient response can be improved by 21%. In addition, ASOSM and SOSM controllers are insensitive to vehicle mass and tire type, implying their robustness to such disturbances. Furthermore, ASOSM requires less control action because of the adaptive law when it performs similarly with SOSM and FOSM.

Torque Vectoring and Rear-Wheel-Steering Control for Vehicle's Uncertain Slips on Soft and Slope Terrain Using Sliding Mode Algorithm

Off-road vehicles always experience serious uncertain longitudinal and lateral slips when running on soft and slope terrains, and some parameters of the vehicles, such as the cornering stiffness and the slip of wheels, are always not constants. In this paper, control strategies for the torque of each wheel and the rear-wheel-steering angle are proposed to maintain a stable velocity and approach an ideal reference model for the off-road vehicle by using second-order sliding mode (SOSM) techniques. An observer is constructed to estimate the actual sideslip angle of the vehicle with the consideration of uncertainties and disturbances of the system. Then, with conditions of bounded uncertainties and disturbances, composite super-twisting (ST) controllers combined with a velocity controller are designed to generate the total torque, the differential torque, and the rear-wheel-steering angle. On this basis, the proposed controllers have been verified to lead good robustness for maintaining the stable velocity and approaching the ideal reference model by using an optimal torque allocation controller at a lower layer. In comparison with conventional yaw moment controllers without the rear-wheel-steering control, the proposed controllers are shown to be more effective.

Active stabilisation of a car-trailer system by means of torque vectoring

The addition of a trailer to a vehicle introduces significant changes to its dynamic behaviour. The lateral response of the system is of significant interest as unfavourable conditions may lead to instability in the system. The instability introduced may cause the vehicle to stray from the intended path, and in extreme situations may even cause the vehicle to topple over. This instability may be introduced by the driver or may even be triggered by external factors such as lateral forces arising because of wind. Active stabilisation systems are normally based on brakes. Electric vehicles with independent motors provide instead the opportunity to stabilise the system through torque vectoring. This paper proposes a feedback-loop control system as an active driver assistance system that enables stabilisation of the system by means of torque vectoring for an independent motor two-wheel drive electric vehicle.

Active stabilisation of a car-trailer system by means of torque vectoring

The addition of a trailer to a vehicle introduces significant changes to its dynamic behaviour. The lateral response of the system is of significant interest as unfavourable conditions may lead to instability in the system. The instability introduced may cause the vehicle to stray from the intended path, and in extreme situations may even cause the vehicle to topple over. This instability may be introduced by the driver or may even be triggered by external factors such as lateral forces arising because of wind. Active stabilisation systems are normally based on brakes. Electric vehicles with independent motors provide instead the opportunity to stabilise the system through torque vectoring. This paper proposes a feedback-loop control system as an active driver assistance system that enables stabilisation of the system by means of torque vectoring for an independent motor two-wheel drive electric vehicle.

Improvement of vehicle stability using a controller based on reinforcement learning

This paper presents a study on the use of reinforcement learning to control the torque vectoring of a small electric racecar aiming to improve vehicle handling and vehicle stability. The reinforcement-learning algorithm used is Neural Fitted Q Iteration, and the sampling of experiences is based on simulations using the software CarMaker. The cost function is based on the position of the states on the phase-plane of sideslip angle and angular velocity. To investigate the maximum ratio of torque distribution that should be set to guarantee stability as well as the effectiveness of the controller inputs in the learning process, two experiments were done (A and B) with different states and possibilities of torque distribution. The controller A is able to improve the vehicle handling and stability with a significant reduction in vehicle sideslip angle. And it also showed that 70% of torque distribution is enough to keep the vehicle stable.

Brake blending and torque vectoring of road electric vehicles: a flexible approach based on smart torque allocation

Application of regenerative braking on electric vehicles has a large impact on several aspects of design, implemented functionalities and overall performances of road vehicles. In particular, multi-quadrant capabilities and improved control performances of modern electric drives can be fully exploited to improve vehicle efficiency, stability and overall environmental impact. Conventional, mechanical friction brakes are currently devoted not only to stop the vehicle but also to the actuation of safety related mechatronics systems such as electronic braking distribution (EBD), AntiBlockierSystem (ABS) and electronic stability control/program (ESC/ESP). The result is an over-actuated system of electrical (electric motors) and mechanical actuators (friction brakes), whose mixed, blended application has to be carefully optimised. In this work a simplified approach is proposed in which concepts transferred from previous studies on high speed trains and autonomous vehicles are re-purposed and adapted in an innovative way to electric road vehicles.

Disturbance Compensation and Torque Coordinated Control of Four In-Wheel Motor Independent-Drive Electric Vehicles

Functional overlaps and conflicts between Electronic Power Steering (EPS) and Torque Vectoring Control (TVC) for Distributed Drive Electric Vehicles (DDEVs) are issues that need to be addressed urgently. This paper deals with the interaction between EPS and TVC from the view of the influence mechanism and control objectives. The disturbance steering torque caused by the differential torque is estimated and a unified steering characteristic is proposed. To validate the analysis results, a torque compensation strategy is modified based on the exiting control system. And a robust vehicle state estimator is built thanks to the aligning torque estimation to provide the necessary information. Typical ground test results show that the torque oscillation on the steering wheel is suppressed. A faster and more linear steering response can be seen, which means that the proposed disturbance compensation strategy can comfort the contradiction between EPS and TVC, also improve the handling performance of the vehicle.

Brake blending and torque vectoring of road electric vehicles: a flexible approach based on smart torque allocation

Application of regenerative braking on electric vehicles has a large impact on several aspects of design, implemented functionalities and overall performances of road vehicles. In particular, multi-quadrant capabilities and improved control performances of modern electric drives can be fully exploited to improve vehicle efficiency, stability and overall environmental impact. Conventional, mechanical friction brakes are currently devoted not only to stop the vehicle but also to the actuation of safety related mechatronics systems such as electronic braking distribution (EBD), AntiBlockierSystem (ABS) and electronic stability control/program (ESC/ESP). The result is an over-actuated system of electrical (electric motors) and mechanical actuators (friction brakes), whose mixed, blended application has to be carefully optimised. In this work a simplified approach is proposed in which concepts transferred from previous studies on high speed trains and autonomous vehicles are re-purposed and adapted in an innovative way to electric road vehicles.

Integrated Active Control of Electric Vehicles

This work describes how it is possible to integrate active chassis control actions in an electric vehicle. A vehicle with Active Front Steering (AFS) is considered, which imposes an incremental steer angle to the front wheels. Using Permanent Magnet Synchronous Motors (PMSMs) as powertrain, fitting on the left/right wheel axle shafts, it is possible to impose not only a desired longitudinal traction, but also an appropriate active yaw torque, so mimicking a classic Rear Torque Vectoring (RTV). The AFS, along with the RTV, allow imposing a desired behavior for the active chassis control of the vehicle, so improving its safety.

Review on Vehicle Drive Technology of Torque Vectoring

Review on Vehicle Drive Technology of Torque Vectoring

Improvement of vehicle stability using a controller based on reinforcement learning

This paper presents a study on the use of reinforcement learning to control the torque vectoring of a small electric racecar aiming to improve vehicle handling and vehicle stability. The reinforcement-learning algorithm used is Neural Fitted Q Iteration, and the sampling of experiences is based on simulations using the software CarMaker. The cost function is based on the position of the states on the phase-plane of sideslip angle and angular velocity. To investigate the maximum ratio of torque distribution that should be set to guarantee stability as well as the effectiveness of the controller inputs in the learning process, two experiments were done (A and B) with different states and possibilities of torque distribution. The controller A is able to improve the vehicle handling and stability with a significant reduction in vehicle sideslip angle. And it also showed that 70% of torque distribution is enough to keep the vehicle stable.

Design and Analysis of DYC and Torque Vectoring using Multiple-Frequency Control Electronic Differential in an Independent Rear Wheel Driven Electric Vehicle

Electric vehicle (EV) are being embraced in recent times as they run on clean fuel, zero tail emission and are environment-friendly. Recent advancements in the field of power electronics and control strategies have made it possible to the advent in the vehicle dynamics, efficiency and range. This paper presents a design for traction control system (TCS) for longitudinal stability and Direct Yaw Control (DYC) for lateral stability simultaneous. The TCS and DYC is based on multiple frequency controlled electronic differential with a simple and effective approach. Along with it, some overviews have been presented on some state of the art in traction control system (TCS) and torque vectoring. The developed technique reduces nonlinearity, multisensory interfacing complexity and response time of the system. This torque and yaw correction strategy can be implemented alongside fuzzy control, sliding mode or neural network based controller. The effectiveness of the control method has been validated using a lightweight neighbourhood electric vehicle as a test platform. The acquired results confirm the versatility of proposed design and can be implemented in any DC motor based TCS/DYC.

Influence of vehicle parameters on handling characteristics and its control using torque vectoring

Handling characteristics of any vehicle refer to its directional control and stability for given steering input. The vehicle parameters such as mass, mass moment of inertia and location of C.G. play a vital role and decide handling response of vehicle under different scenarios. Safe drive is ensured by vehicle’s responses such as yaw rate, roll rate and vehicle lateral acceleration. This paper presents steady state and transient state simulation of XUV 500 model in accordance with ISO 4138 and ISO 3888 test standards using CarSim-Simulink Co-simulation. Influence of changes in above mentioned parameters on Vehicle response during both tests is investigated. Further, control of power train output using torque vectoring is implemented in both tests and its effect on vehicle response is evaluated. The results show that understeer tendency of the vehicle is reduced in steady state test (Constant radius test: ISO 4138). Critical vehicle responses of yaw rate, roll rate, lateral acceleration and path tracking showed improvement during the DLC manoeuvre (ISO 3888) hence, improving stability at higher lateral accelerations.

Novel Constrained Nonlinear Control of Vehicle Dynamics Using Integrated Active Torque Vectoring and Electronic Stability Control

This study deals with a new integrated control sys-tem using active torque vectoring (ATV) and electronic stability control (ESC) for enhancement of vehicle directional stability and steerability. In this system, the stabilizing yaw moment is calculated using a novel constrained nonlinear controller with both input and state constraints. The proposed controller is designed using the prediction of continuous nonlinear vehicle model. It guarantees the vehicle directional stability by restricting the side slip angle in the admissible range when the yaw rate tracks its desired response for improved steerability. The calculated yaw moment is generated as driving and braking forces by integrated ATV and ESC systems. According to the integration policy, the required yaw moment is initially generated by ATV in the rear wheels to keep the vehicle driving performance. Because of limitation of ATV, if more yaw moment is required, the ESC system applies asymmetric braking pressure to front wheels to compensate the scarcity of ATV. By the proposed strategy, the small reduction of vehicle velocity is achieved and the vehicle drivability performance is maintained. In order to determine the full capacity of ATV, the active differential dynamics is considered in this paper. Simulation studies are conducted to evaluate the efficiency of proposed constrained controller in comparing with the unconstrained controller and a conventional nonlinear model predictive controller (NMPC) developed in the recent papers using 14-DOF vehicle model. By analyzing the results, it is clear that the proposed controller is much faster and easy to solution and implementation.