Torque Vectoring Research Articles

Torque Vectoring Control of RWID Electric Vehicle for Reducing Driving-Wheel Slippage Energy Dissipation in Cornering

The anxiety of driving range and inconvenience of battery recharging has placed high requirements on the energy efficiency of electric vehicles. To reduce driving-wheel slip energy consumption while cornering, a torque vectoring control strategy for a rear-wheel independent-drive (RWID) electric vehicle is proposed. First, the longitudinal linear stiffness of each driving wheel is estimated by using the approach of recursive least squares. Then, an initial differential torque is calculated for reducing their overall tire slippage energy dissipation. However, before the differential torque is applied to the two side of driving wheels, an acceleration slip regulation (ASR) is introduced into the overall control strategy to avoid entering into the tire adhesion saturation region resulting in excessive slip. Finally, the simulations of typical manoeuvring conditions are performed to verify the veracity of the estimated tire longitudinal linear stiffness and effectiveness of the torque vectoring control strategy. As a result, the proposed torque vectoring control leads to the largest reduction of around 17% slip power consumption for the situations carried out above.

Artificial Intelligence for Stability Control of Actuated In–Wheel Electric Vehicles with CarSim® Validation

This paper presents an active controller for electric vehicles in which active front steering and torque vectoring are control actions combined to improve the vehicle driving safety. The electric powertrain consists of four independent in–wheel electric motors situated on each corner. The control approach relies on an inverse optimal controller based on a neural network identifier of the vehicle plant. Moreover, to minimize the number of sensors needed for control purposes, the authors present a discrete–time reduced–order state observer for the estimation of vehicle lateral and roll dynamics. The use of a neural network identifier presents some interesting advantages. Notably, unlike standard strategies, the proposed approach avoids the use of tire lateral forces or Pacejka’s tire parameters. In fact, the neural identification provides an input–affine model in which these quantities are absorbed by neural synaptic weights adapted online by an extended Kalman filter. From a practical standpoint, this eliminates the need of additional sensors, model tuning, or estimation stages. In addition, the yaw angle command given by the controller is converted into electric motor torques in order to ensure safe driving conditions. The mathematical models used to describe the electric machines are able to reproduce the dynamic behavior of Elaphe M700 in–wheel electric motors. Finally, quality and performances of the proposed control strategy are discussed in simulation, using a CarSim® full vehicle model running through a double–lane change maneuver.

Integrated Chassis Control and Control Allocation for All Wheel Drive Electric Cars with Rear Wheel Steering

This study investigates a control strategy for torque vectoring (TV) and active rear wheel steering (RWS) using feedforward and feedback control schemes for different circumstances. A comprehensive vehicle and combined slip tire model are used to determine the secondary effect and to generate desired yaw acceleration and side slip angle rate. A model-based feedforward controller is designed to improve handling but not to track an ideal response. A feedback controller based on close loop observation is used to ensure its cornering stability. The fusion of two controllers is used to stabilize a vehicle’s lateral motion. To increase lateral performance, an optimization-based control allocation distributes the wheel torques according to the remaining tire force potential. The simulation results show that a vehicle with the proposed controller exhibits more responsive lateral dynamic behavior and greater maximum lateral acceleration. The cornering safety is also demonstrated using a standard stability test. The driving performance and stability are improved simultaneously by the proposed control strategy and the optimal control allocation scheme.

A Novel Control Method for In-wheel SR Motor to Implement Torque Vectoring Control for Compact EV

A Novel Control Method for In-wheel SR Motor to Implement Torque Vectoring Control for Compact EV

Surrey Team to Improve Torque Vectoring in EVs

EV manoeuvrability – and wider acceptability – likely outcomes of EU project

Contributions of vehicle dynamics to the energy efficient operation of road and rail vehicles

This review addresses efforts made within the field of vehicle dynamics to contribute to the energy efficient operation of road and rail vehicles. Selected investigations of the research community to develop technology solutions to reduce energy consumption are presented. The study explores the impact and potential of relatively mature technologies such as regenerative braking, but also recent research directions that are seeking to develop solutions such as regenerative suspensions, a concept common to both transport modes. Specifically for road vehicles, the study includes rear wheel steering, camber control and torque vectoring, and for rail vehicles active steering and light-weighting. Operationally, there would appear to be great potential for rail vehicles in the wider adoption of connected driver advisory systems, and automatic train control systems, including the application of machine learning techniques to optimise train speed trajectories across a route. Similarly, for road vehicles, predictive and cooperative eco-driving strategies show potential for significant energy savings for connected autonomous vehicles.

Nonlinear Model Predictive Control for Integrated Energy-Efficient Torque-Vectoring and Anti-Roll Moment Distribution

This study applies nonlinear model predictive control (NMPC) to the torque-vectoring (TV) and front-to-total anti-roll moment distribution control of a four-wheel-drive electric vehicle with in-wheel-motors, a brake-by-wire system, and active suspension actuators. The NMPC cost function formulation is based on energy efficiency criteria, and strives to minimize the power losses caused by the longitudinal and lateral tire slips, friction brakes, and electric powertrains, while enhancing the vehicle cornering response in steady-state and transient conditions. The controller is assessed through simulations using an experimentally validated high-fidelity vehicle model, along ramp steer and multiple step steer maneuvers, including and excluding the direct yaw moment and active anti-roll moment distribution actuations. The results show: 1) the substantial enhancement of energy saving and vehicle stabilization performance brought by the integration of the active suspension contribution and TV; 2) the significance of the power loss terms of the NMPC formulation on the results; and 3) the effectiveness of the NMPC with respect to the benchmarking feedback and rule based controllers.

Coordination of Lateral Vehicle Control Systems Using Learning-Based Strategies

The paper proposes a novel learning-based coordination strategy for lateral control systems of automated vehicles. The motivation of the research is to improve the performance level of the coordinated system compared to the conventional model-based reconfigurable solutions. During vehicle maneuvers, the coordinated control system provides torque vectoring and front-wheel steering angle in order to guarantee the various lateral dynamical performances. The performance specifications are guaranteed on two levels, i.e., primary performances are guaranteed by Linear Parameter Varying (LPV) controllers, while secondary performances (e.g., economy and comfort) are maintained by a reinforcement-learning-based (RL) controller. The coordination of the control systems is carried out by a supervisor. The effectiveness of the proposed coordinated control system is illustrated through high velocity vehicle maneuvers.

An energy efficient intelligent torque vectoring approach based on fuzzy logic controller and neural network tire forces estimator

In electric vehicles (EVs) with multiple motors, torque vectoring (TV) control can effectively enhance the cornering response and safety. Moreover, TV systems can also improve the overall efficiency through an optimal torque distribution that also considers the power consumption. For such a complex control system with multiple objectives, intelligent control techniques have demonstrated to be one of the best alternatives. However, the works proposed in the literature do not handle both vehicle dynamics behaviour and energy efficiency, and generally do not consider the real-time implementability of the developed controllers. To overcome the aforementioned issues, in this work, a novel torque vectoring approach is proposed, which uses a neural network-based vertical tire forces estimator and considers the regenerative braking capabilities of EVs. Moreover, the implementability of the controller in a hetereogenous (FPGA and microcontroller) automotive suitable system on chip is addressed, ensuring its real-time capabilities. For the sake of validating the proposed approach, a set of experiments have been carried out in a hardware in the loop setup. The performance of the proposed TV approach has been compared with other two TV approaches from the literature, evaluating them in several challenging manoeuvres in high and low tire-road friction coefficient scenarios. Results show that the proposed approach not only is able to enhance the vehicle dynamics behaviour but also to decrease the energy consumption about 13%.

Design of Integrated Autonomous Driving Control System That Incorporates Chassis Controllers for Improving Path Tracking Performance and Vehicle Stability

This paper describes an integrated autonomous driving (AD) control system for an autonomous vehicle with four independent in-wheel motors (IWMs). The system consists of two parts: the AD controller and the chassis controller. These elements are functionally integrated to improve vehicle stability and path tracking performance. The vehicle is assumed to employ an IWM independently at each wheel. The AD controller implements longitudinal/lateral path tracking using proportional-integral(PI) control and adaptive model predictive control. The chassis controller is composed of two lateral control units: the active front steering (AFS) control and the torque vectoring (TV) control. Jointly, they find the yaw moment to maintain vehicle stability using sliding mode control; AFS is prioritized over TV to enhance safety margin and energy saving. Then, the command yaw moment is optimally distributed to each wheel by solving a constrained least-squares problem. Validation was performed using simulation in a double lane change scenario. The simulation results show that the integrated AD control system of this paper significantly improves the path tracking capability and vehicle stability in comparison with other control systems.

A cooperative control strategy for yaw rate and sideslip angle control combining torque vectoring with rear wheel steering

ABSTRACT Automobiles are becoming more and more complex as multiple control systems are integrated into the vehicle platform. This paper investigates the coordination of active rear steering (RWS) and torque vectoring (TV) – which is enabled by independent electric motors at the rear axle – in controlling vehicle lateral dynamics. The proposed controller aims at enhancing vehicle handling performance and stability while cornering. The coordination of the two actuators is achieved by weighting their contribution based on their impact on vehicle dynamics according to the working condition. The impact of each control system is assessed by means of phase portraits. These plots are a very powerful tool for analysing vehicle nonlinear dynamics as they readily display vehicle stability properties and map equilibrium point locations and movement to changing parameters and control inputs. Based on phase portrait analysis, a performance index is thus proposed, which weights more the control action (TV or RWS) capable of leading the vehicle at the nearest equilibrium point with the fastest rate. The controller performance is assessed through numerical simulations carried out using a nonlinear 14 dofs vehicle model. Results are compared with ones of the two controllers alone (RWS and TV) in different manoeuvers and adherence conditions.

Yaw Rate and Sideslip Angle Control Through Single Input Single Output Direct Yaw Moment Control

Electric vehicles with independently controlled drivetrains allow torque vectoring, which enhances active safety and handling qualities. This article proposes an approach for the concurrent control of yaw rate and sideslip angle based on a single-input single-output (SISO) yaw rate controller. With the SISO formulation, the reference yaw rate is first defined according to the vehicle handling requirements and is then corrected based on the actual sideslip angle. The sideslip angle contribution guarantees a prompt corrective action in critical situations such as incipient vehicle oversteer during limit cornering in low tire-road friction conditions. A design methodology in the frequency domain is discussed, including stability analysis based on the theory of switched linear systems. The performance of the control structure is assessed via: 1) phase-plane plots obtained with a nonlinear vehicle model; 2) simulations with an experimentally validated model, including multiple feedback control structures; and 3) experimental tests on an electric vehicle demonstrator along step steer maneuvers with purposely induced and controlled vehicle drift. Results show that the SISO controller allows constraining the sideslip angle within the predetermined thresholds and yields tire-road friction adaptation with all the considered feedback controllers.

Low-cost Electric Bus Stability Enhancement Scheme Based on Fuzzy Torque Vectoring Differentials: Design and Hardware-in-the-loop Test

Abstract In this paper, a fuzzy torque vectoring differential controller is proposed to improve the lateral stability for the two-motor-wheel drive electric buses with low sensor cost and computational cost. In order to know the unmeasured sideslip angle, unscented Kalman filter algorithm is used to accurately estimate the sideslip angle based on a nonlinear electric bus model and the measured yaw rate and lateral acceleration signals. Given the advantages of high computational efficiency and human’s heuristic knowledge, fuzzy rules are newly designed to control the torque vectoring differentials, and thereby to provide an extra yaw moment on the electric bus body to compensate the driver steering aiming at stability enhancement. To validate the effectiveness and efficiency of the proposed fuzzy torque vectoring differential controller, hardware-in-the-loop tests were carried out to evaluate the controller performance in real-time under different driving maneuvers and road conditions. The test results indicate that the fuzzy torque vectoring differential controller can significantly improve the handling and path-following accuracy for the two-motor-wheel drive electric buses.

Integrated lateral dynamics control concept for over-actuated vehicles with state and parameter estimation and experimental validation

In order to improve the driving dynamics and the driving safety, the car manufacturers have introduced several systems like active front and rear steering, torque vectoring or braking systems. This work presents an integrated lateral dynamics control concept for a over-actuated vehicle. The system incorporates only sensors and actuators available in current series production cars. The driver’s commands are translated by a trajectory generation module, which calculates the desired lateral vehicle dynamics. A kinematic feedforward and a stabilizing feedback controller provide the desired vehicle motion, which is realized by an optimization-based model inversion algorithm using a nonlinear two-track model including all relevant driving dynamics effects. The convex optimization problem, taking also input constraints into account, can be solved in real-time. The non-measured states and additional parameters as the friction coefficient or the road inclination and bank angles are accurately estimated using a second order divided difference Kalman filter. Experimental validation results of a fully equipped test vehicle are provided, which show the excellent performance of the proposed control concept up to the adhesion limit of the tires on several road conditions.

DESIGN AND ASSESSMENT OF A SPEED-BASED INTEGRATED ACTIVE VEHICLE CONTROLLER FOR LATERAL STABILITY

An integrated active vehicle control system implementing fuzzy-logic control (FLC) is introduced. The system integrates three commercially- available active vehicle control systems, namely, Active Front Steering (AFS), Electronic Stability Control (ESC) and Torque Vectoring System (TVS) aiming at enhancing vehicle handling and cornering stability and rollover prevention. Two different vehicle models were constructed to simulate the dynamic behavior of the system with and without the proposed integration controller, namely, a 14-DOF vehicle dynamic model with nonlinear tire characteristics and a 2DOF bicycle reference model. Last model was utilized to generate controller’s reference values of vehicle’s yaw rate and body side slip angle at a given forward speed and driver’s steering input. Simulation was carried out in the MATLAB/SIMULINK software environment. The effectiveness of the system was investigated applying five different standard cornering test maneuvers at different vehicle forward speeds of 10, 20 and 30m/s. Simulated test maneuvers are: step, J-turn, single lane change (SLC), sine with dwell, (SWD) and fishhook.Results reveal that, for stability enhancement, AFS is most effective at low vehicle speeds with declining efficacy as speed goes up. Both ESC and TVS have been found to be equally effective at moderate to high speeds. In conclusion, an integrated chassis control (ICC) strategy has been proposed that improves vehicle handling and cornering performance across the entire operating range of speed using a forward-speed-based stability criterion to allocate stability control authority and ensure smooth transition of control between the three AFS, ESC, and TVS systems.

Development of a novel general reconfigurable vehicle dynamics model

This study presents a novel approach in vehicle modeling to provide a unified reconfigurable vehicle dynamics model. The introduced vehicle model is applicable for analysis, design, and control of a wide variety of vehicles and can be reconfigured for four- and three-wheeled vehicles. Active steering, differential braking, torque vectoring, and active camber are included in the model as the actuators. The proposed set of model equations is reconfigurable and can be adjusted for various vehicles without need for new derivation. Two matrices are defined called the corner reconfiguration matrix and actuator reconfiguration matrix that are responsible for wheel and actuator configurations of the vehicle, respectively. The model can be reconfigured by setting the diagonal elements of these two matrices. The proposed reconfigurable model is compared with different high-fidelity vehicle models in CarSim for validation. Simulation results show that four-wheeled and three-wheeled vehicle models can all be drawn from the single unified reconfigurable model. In addition, the introduced model properly matches the high-fidelity CarSim models for four-wheeled and three-wheeled vehicles.

Evaluating Model Predictive Path Following and Yaw Stability Controllers for Over-Actuated Autonomous Electric Vehicles

Active safety systems are of significant importance for autonomous vehicles operating in safety-critical situations like an obstacle-avoidance manoeuvre with high vehicle speed or poor road condition. However, a conventional electronic stability control system, may not always yield desired path following and yaw stability performance in such circumstances merely through brake intervention. This paper pursues a detailed investigation on utilising model predictive control (MPC) and torque vectoring for path following and yaw stability control of over-actuated autonomous electric vehicles (AEVs) in dangerous double lane change manoeuvre. The control problem of the AEV is formulated based on MPC by utilising active front steering and torque vectoring, and constraints are imposed explicitly on yaw rate and sideslip angle to ensure yaw stability. Four MPC-based controllers are designed based on double-track vehicle models. Specifically, they include two one-level controllers, i.e. one with torque vectoring and one with equal torque allocation, and two two-level controllers, i.e. one with optimisation-based torque allocation and one with rule-based allocation. These controllers are assessed extensively, with respect to passing velocity, tracking accuracy, tyre utilisation and robustness. The effect of horizon length on the control performance and computational efficiency is also investigated.

Handling performance of an 8x8 combat vehicle

In this research paper, the handling stability of an 8x8 combat vehicle will be assessed using two different control systems. The first technique utilizes a Torque Vectoring Controller (TVC) to control the vehicle yaw rate to meet the desired value. The second technique utilizes an Active Rear-axles Steering (ARS) to minimize the vehicle sideslip. TVC will be designed as a Single Input Single Output (SISO) control problem using a Sliding Mode Control (SMC) technique, while an Optimal Linear Quadratic Regulator (LQR) will be utilized to develop the ARS controller. The two controllers will be evaluated against a conventional vehicle with fixed rear axles. TruckSim software is used in corporation with Matlab/Simulink to implement and assess the controllers using Double Lane Change (DLC) over high and low coefficient of friction road surfaces at various speeds. The results give an insight into the driving conditions at which each controller is utilized and introduce a novel method to coordinate the integration between both controllers for integrated chassis applications.

An Energy-Saving Torque Vectoring Control Strategy for Electric Vehicles Considering Handling Stability Under Extreme Conditions

Four-wheel independently actuated electric vehicles (FWIA EVs) allow variable distributions of driving torques among individual wheels to improve vehicle performance. To reduce energy consumption while ensuring handling stability, we propose an optimal torque vectoring control strategy based on a two-level distribution formula. This strategy can naturally decouple front/rear axle torque vectoring from left/right torque vectoring and avoid the contradiction between stability and energy saving. First, considering the motor efficiency, the vehicle's total torque is optimally distributed to the front and rear axles based on model predictive control. Then, based on the front/rear axle distribution ratio, the left/right torque vectoring is revised to produce a suitable additional yaw moment to improve the handling stability. A sliding mode controller is designed to track the reference yaw rate calculated from a nonlinear reference model. The nonlinear reference model is more suitable for extreme conditions due to the accurate reflection of the nonlinear characteristics. A suitable additional yaw moment can ensure vehicle stability and avoid excessive energy consumption due to vehicle instability. The simulation and hardware-in-the-loop experimental results demonstrate that the proposed control strategy can reduce energy consumption while ensuring vehicle stability.

Development of Torque Vectoring Control Algorithm for Front Wheel Driven Dual Motor System and Evaluation of Vehicle Dynamics Performance

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.