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This study developed a control algorithm of torque vectoring system to improve the handling performance of a green-car and evaluate the performance of vehicle dynamics to improve the controllability and stability. Firstly, this study configured the control algorithm with the supervisory controller that decides the control mode by checking the current driving status of a vehicle, target yawrate calculator that reflects the steady-state and transient-state response characteristics according to the control mode, as well as the reference yawrate calculation, desired yaw moment calculation and transferred torque calculation. The control mode consists of the agile mode that controls the torque vectoring to improve the controllability and the safe mode that controls the torque vectoring to improve stability. Secondly, this study performed the modeling of the dual motor type torque vectoring system and an EV AWD vehicle. Lastly, this study defined the driving test scenario and evaluation method as well as the quantitative performance index for each vehicle test and established the co-simulation environment for the handling test evaluation. This study found that when a vehicle is controlled by applying the torque vectoring system, handling performance was improved according to controllability and safe mode by verifying the simulation.

This paper describes a yaw stability control algorithm of 4WD vehicles based on model predictive torque vectoring with physical constraints. A vehicle planar model based predictive rear and all-wheel torque vectoring algorithms were developed for 4WD vehicles by considering predictive states and driver’s steering wheel angle. The physical constraints applied to the model predictive control consist of three types: limitation on magnitude of tire force, change rate of tire force, and output torque of transfer case. Two types of torque vectoring algorithms, rear-wheel and all-wheel, were constructed for comparative analysis. The steady state yaw rate was derived and applied as a desired value for yaw stability of the vehicle. The algorithm was constructed in a MATLAB/Simulink environment and the performance evaluation was conducted under various test scenarios, such as step steering and double lane change, using the CarSim software. The evaluation results of the predictive torque vectoring showed sound performance based on the prediction of states and driver’s steering angle.

The increasing demands for driving comfort and driving dynamics lead to the introduction of a variety of control systems in modern vehicles. So far, these systems are working in peaceful coexistence and do not use potential synergies. This paper presents a modular control allocation, which combines lateral torque distribution at the rear axle, longitudinal torque distribution, and rear wheel steering. The previous investigations of torque vectoring mostly neglected the secondary yaw torque, which is a result of the dependency between lateral and longitudinal forces at the tyres. An increase in longitudinal forces leads to a decrease in lateral forces and, therefore, results in a yaw torque. A comprehensive vehicle and tyre model is used to analyze this secondary effect for different vehicle states and requested yaw torque. The investigation shows that the influence of the secondary yaw torque varies heavily depending on the vehicle state and the requested yaw torque. Especially, for stabilizing torque requests at high lateral acceleration, the secondary effect is significant and should not be neglected. The investigation shows that an optimal distribution for each yaw torque request exists and that results in maximum lateral forces and thereby maximum lateral acceleration. These results are used within the paper’s modular control allocation. A model-based reference generator delivers desirable yaw rates and side slip angles, which are transferred into necessary lateral forces at the wheels by the control allocation unit. This force-based approach enables modular expandability and usability across multiple vehicles. The proposed controller shows that using the available systems in conjunction helps to increase driving performance and the vehicles stability at the same time.

A key feature achievable by electric vehicles with multiple motors is torque-vectoring. Many control techniques have been developed to harness torque-vectoring in order to improve vehicle safety and energy efficiency. The majority of the existing contributions only deal with specific aspects of torque-vectoring. This paper presents an integrated approach allowing a smooth coordination among the main blocks that constitute a torque-vectoring control framework: (1) a reference generator, that defines target yaw rate and sideslip angle; (2) a high level controller, that works out the required total torque and yaw moment at the vehicle level; (3) a low level controller, that maps the required force and yaw moment into individual wheel torque demands. In this framework, the driver can select one among a number of driving modes that allow to change the vehicle cornering response and, as a second priority, maximise energy efficiency. For the first time, the selectable driving modes include an “Energy efficiency” mode that uses torque-vectoring to prioritise the maximisation of the vehicle energy efficiency, thus further increasing the vehicle driving range. Simulation results show the effectiveness of the proposed framework on an experimentally validated 14 degrees of freedom vehicle model.

Although electric vehicle fully exhibits its comparative merits of energy conservation and environmental friendliness, further improvement of its traction energy efficiency lacks comprehensive investigations in the past. In this paper, the effect of the torque vectoring on traction energy conservation during cornering for a rear-wheel-independent-drive electric vehicle is investigated. Firstly, turning resistance coefficient and energy conservation mechanism of torque vectoring are derived from the single track dynamic model. Next, an optimal torque vectoring control strategy based on genetic algorithm is proposed, with the consideration of the influence of the operation-point change of the in-wheel motors, to find out the best torque vectoring ratio offline. Finally, various simulation tests are conducted to validate the energy conservation effect after Simulink modelling. The results verify that though the optimization of the operating region of the motors is the main part for tractive energy conservation, the contribution of torque vectoring itself can reach up to 1.7% in some typical cases.

This paper presents a new yaw stability controller for a rear double-driven electric vehicle. A linear parameter varying (LPV) model of the vehicle is formulated using longitudinal speed measurement and tire cornering stiffness estimation. The LPV model is then utilized to design a gain-scheduled (H_∞) controller with guaranteed stability. Results from simulations, performed with CarSim, show that the new controller improves the vehicle performance and handling even in extreme maneuvers and that it is robust to model parameter uncertainties.

This paper presents the vehicle handling performance improvement by means of transfer case and torque vectoring system. The focus is on the agility and stability during handling situation. Transfer case can control the torque distribution ratio between the front and rear axles. The left and right torque vectoring system of the rear axle is a device that can control both the torque flow amount and its direction between the right and left wheels. The controller for lateral dynamics was applied by using a sliding mode control method and a commercial dynamic analysis tool was used for the vehicle performance simulation.

Available space optimised packaging of the drivetrain components can lead to unconventional wheel axle loads, which have to be taken into account for the design of the vehicles’ dynamics. The BMW Group Research and Technology developed in cooperation with the German Aerospace Institute (DLR) and the Institute of Control Systems (IRS) within the Karlsruhe Institute of Technology (KIT) a control concept for torque vectoring. In spite of the challenge of high rear axle loads, attractive and at the same time safe ride characteristics are achieved by utilisation of independent distribution of drive and brake wheel torques.

The foundation of a dynamic as well as safe driving behaviour is, even in combination with modern vehicle control systems, based on the passive set-up of the suspension. Considering the constraints of a powerful rear wheel drive with torque vectoring functionality and centre of gravity position towards the rear of the car, special attention has to be paid to the rear axle layout. Regarding the kinematics the toe and camber curves are defined after choosing the position of roll and pitch centres. A linear increase of toe-in during bump motion reduces the general tendency towards oversteering of the vehicle. At the same time, toe angle changes during wheel travel must be limited to ensure sufficient directional stability on uneven tracks. To maximise the forces transmitted by the tyre in longitudinal and lateral directions, the tyre contact patch area must be as large as possible at all times. Thus a progressive increase of negative camber angle in bump motion is desirable to partly compensate the reduction of the contact patch area induced by the roll motion of the vehicle body during cornering. Here, as well, a compromise needs to be found regarding directional stability requirements. Besides the wheel trajectories the elasto-kinematics, that means the change of wheel alignment due to longitudinal and lateral forces, play an important role while designing the vehicle dynamic characteristics. A tendency towards toe-in at the outer rear wheel in a turn will reduce the oversteering tendency of the vehicle. Under longitudinal braking and propulsion forces only small changes of toe angle are desirable.

Much discussion on the launch of electric vehicles focuses on the reduction in emissions. Regarding vehicle dynamics there are also interesting new approaches to define vehicle characteristics, especially with multi-motor concepts. Bosch Engineering GmbH has demonstrated such concepts in a test vehicle.

The importance of electronically controlled systems in the chassis and drivetrain is increasing. The associated products have the aims of improving not only safety aspects but also driving pleasure, with end customers laying not inconsiderable weight on the potential to define the driving experience through the components’ influence. Magna Powertrain has developed a torque vectoring System with clearly demonstrable improvement of agility and high availability, and brought it into series production.

The new BMW X6 is the most sporty member of the BMW X model family. As well as having xDrive, the intelligent four-wheel drive system, it is the first car to be equipped with torque vectoring technology (Dynamic Performance Control) as standard, for targeted optimisation of driving dynamics.

Goal-directed utilization of active torque distribution to the four drive wheels has become increasingly important in the last few years, especially with a view to improving both driving dynamics and traction. With the Vector Drive rear axle transmission, ZF generates such a torque vectoring system at the rear axle. This paper describes how such a rear axle torque vectoring system can be implemented while fulfilling the requirements of precise torque control and robust functional availability with adherence to safety requirements as well as suitability for volume production. Based on the technical description of the system and its setup, the interaction of the individual components within the overall functionality is demonstrated.

Developers are increasingly focusing on torque management to improve longitudinal and lateral dynamics and to enhance the vehicle’s characteristics. In four-wheel drive vehicles in particular, it is no longer simply a matter of distributing torque to the individual axles but also to the individual wheels. The basis of all Ricardo concepts is the use of a actuator with a low torque. The torque is increased with the aid of a transmission, thus influencing the known friction ellipsis of the wheels.

The increasing number of vehicle dynamics control systems in modern vehicles requires a fully integrated concept, which considers the different controller functions and adjusts them with respect to each other. The model-based development and the verification of the global chassis controller of Magna Steyr is presented with reference to the combination of a torque vectoring differential and an active rear wheel steering system. The vehicle dynamics simulation program veDyna of Tesis Dynaware is used as virtual vehicle environment.

DYC Torque Vectoring reduces the understeering characteristics during cornering without affecting vehicle stability. As a result, a higher cornering limit is achieved

Electronically controlled driveline and chassis systems provide a means of optimizing driving characteristics depending on the specific driving situation. Furthermore, an effective combination of driveline, steering and chassis systems allow more than ever before a flexible distribution of tire forces. This results in new possibilities for vehicle dynamics control beyond the brake intervention. ZF Friedrichshafen AG explores this extension to “torque vectoring” in several development projects in order to enhance steering response, general performance, and road safety by an optimal usage of the tire force potential.

Ricardo has developed a torque vectoring approach aimed at extending the stability range of vehicles in order to achieve greater dynamic capability.