Vehicle Agility Research Articles

Chassis concept of the individually steerable five-link suspension: a novel approach to maximize the road wheel angle to improve vehicle agility

The Institute of Vehicle Systems Engineering at Ulm University of Applied Sciences is currently developing the autonomous concept vehicle Nimbulus-e in the strategic field “Intelligent Commercial Vehicles” with the aim of maneuvering as agilely as possible in confined spaces. To achieve high agility under the conditions mentioned, large road wheel steering angles are necessary. As part of the basic vehicle concept, the first step is to select a suitable chassis for this purpose. Conventional suspensions cannot be applied due to the mechanical connection of the tie rod to the steering knuckle limiting the road wheel angles. Therefore, the approaches published so far for individual chassis concepts with large steering angles are analyzed and evaluated for use. In this paper, the concept of a novel individually steerable five-link suspension is described. The concept includes a vehicle body mounted steering actuator connected to the chassis via a self-locking worm gear. Due to the body mounted connection of the steering actuator, it does not contribute to the unsprung mass. An analysis of the kinematic and elastokinematic properties and the achievable road wheel steering angle is presented. In the Nimbulus e concept vehicle, the individually steerable corner module is used on both the front and the rear axle. The system is driven by four wheel hub motors. This means that eight control variables are available for the vehicle.

Series-Hybrid Powertrains: Advancing Mobility Control in Electric Tracked Vehicle Technology

This work focuses on developing a mobility control system for high-speed series-hybrid electric tracked vehicles, which operate with independent traction motors for each track. The scope of this research includes modeling a series-hybrid powertrain specific to military tracked vehicles and conducting an in-depth analysis of its dynamic behavior. Subsequently, this study conducts a critical review of mobility control approaches sourced from the literature, identifying key techniques relevant to high-inertia vehicular applications. Building on foundational models, this study proposes a robust closed-loop mobility control system aimed at ensuring precise and stable off-road vehicle operations. The system’s resilience and adaptability to a variety of driving conditions are emphasized, with a particular focus on handling maneuvers such as steering and pivoting, which are challenging operations for tracked vehicle agility. The performance of the proposed mobility control system is tested through a series of simulations, covering a spectrum of operational scenarios. These tests are conducted in both offline simulation settings, which permit meticulous fine-tuning of system parameters, and real-time environments that replicate actual field conditions. The simulation results demonstrate the system’s capacity to improve the vehicular response and highlight its potential impact on future designs of mobility control systems for the heavy-duty vehicle sector, particularly in defense applications.

Predictive handling limits monitoring and agility improvement with torque vectoring on a rear in-wheel drive electric vehicle

This work presents a predictive torque vectoring controller that optimally monitors the vehicle limits of handling using active torque distribution in the rear axle of a fully electric vehicle. It works in combination with a feedforward controller designed to improve the vehicle's agility. The overall torque vectoring strategy is described together with the vehicle lateral dynamics, sideslip angle estimator, and torque allocation method. Numerical simulations for various scenarios and road profiles show the benefits of predicting the vehicle's handling limits and the enhancement of vehicle stability in terms of reduced vehicle sideslip angle and driver effort. The proposed optimal control method for predicting vehicle handling limit violations does not require a dedicated solver, making it a promising candidate for real-time applications. The case study is a vehicle equipped with two rear in-wheel motors in the framework of HiPERFORM, an ECSEL Joint Undertaking (JU) European research project. Hardware-in-the-loop (HiL) tests were performed on a dedicated e-axle test bench to integrate the torque vectoring controller with the real e-motors and a dual inverter. The results of the HiL testing demonstrate that the torque-vectoring requirements are satisfied by the hardware configuration in use.

A novel nonlinear drift control for sharp turn of autonomous vehicles

Under the sharp turn condition, especially on low adhesion coefficient roads, drift motion can improve vehicle agility and ensure safety. During drift motion, the large sideslip angle and rear wheel slip as well as the saturation of lateral tyre force result in the complex nonlinear dynamic behaviour, such as non-unique equilibrium point and unstable vehicle dynamic. Therefore, this paper focuses on the analysis of dynamic characteristics and the design of both the equilibrium point and feedback controller for drift motion. First, a dynamics model, in which the rear wheel slip ratio is treated as a parametric variable and the influence of rear wheel lateral force on the drift motion is explicitly expressed, is established. Then the expression of steady-state yaw rate and the virtual control input is derived, and the error dynamic model is presented furthermore. To design the equilibrium point, in this paper the effect of rear tyre slip angle and slip ratio on the equilibrium point is discussed, and the condition of stable equilibrium point is proposed. In addition, the design of desired rear tyre slip angle by synthesising the cornering performance during drift motion is presented. For the drift controller design, the main idea is to design the stabilisation controller for the nonlinear dynamic system with the stable equilibrium point and to calculate static feedback gain by means of Lyapunov stability theory. To illustrate the characteristics of drift motion, the influence of the vehicle speed and rear wheel slip ratio on the vehicle path and the low damping characteristics of the dynamic system on the control performance are analysed by different simulation conditions. Moreover, the performance and effectiveness of the proposed drift control are verified by simulation.

Torque vectoring control of a hybrid drive vehicle to enhance vehicle agility and stability

This paper presents a torque vectoring algorithm that enhances the agility and stability of a hybrid drive vehicle that is a rear-wheel-drive vehicle equipped with two front in-wheel motors and an open differential at the rear axle. Vehicle agility and stability are closely related to the understeer gradient and the yaw rate damping coefficient. Based on this relation, the proposed torque vectoring algorithm is designed to modify the two cornering characteristics using steady-state and transient control inputs, each input with its respective purpose. Firstly, the steady-state control input improves vehicle agility by reducing the vehicle understeer gradient. Secondly, the transient control input improves the lateral stability by increasing the yaw rate damping coefficient. The effects of the proposed algorithm on vehicle dynamic responses are analyzed to select proper design parameter values for the algorithm. The proposed algorithm has been validated via both computer simulations and vehicle tests. From the simulation results, the proposed control algorithm showed better control performance compared to that of the sliding mode based yaw rate controllers, fulfilling the objectives of the steady-state and transient control strategies. The vehicle tests have been conducted with the algorithm implemented in a rear-wheel-drive vehicle equipped with two front in-wheel motors. The vehicle test results show that the proposed algorithm can provide superior performance in enhancing vehicle agility and stability compared to that of the uncontrolled case.

Integrated Yaw Stability Control of Electric Vehicle Equipped with Front/Rear Steer-by-Wire Systems and Four In-Wheel Motors

This paper presents the integrated motion control method for an electric vehicle (EV) equipped with a front/rear steer-by-wire (SbW) system and four in-wheel motor (IWM). The proposed integrated motion control method aims to maintain stable cornering. To maintain vehicle agility and stability, the lateral force and yaw rate commands of the vehicle are generated by referring to the neutral steering characteristics. The driver’s driving force command, the lateral force command based on the bicycle model, and the yaw moment generated by the high-level controller are distributed into the driving force of each wheel and the lateral force of the front and rear wheels by the yaw moment distribution. Finally, the distributed forces are directly controlled by a low-level controller. To directly control the forces, a driving force observer and a lateral force observer were introduced via driving force estimation in the IWMs and rack force estimation in the SbW system. The control performance is verified through computer simulations.

Car-following characteristics of various vehicle types in respective driving phases

The dynamics of mixed traffic were analysed by examining the driving features of various vehicles based on their car-following behaviours in a test-driving circuit. By applying decision tree analysis to the observed data, we successfully compared the acceleration, velocity difference and distance gap of different vehicles during driving trials and quantitatively extracted the features of these physical values from the respective driving phases. Our investigation resulted in a short-distance gap for motorcycles in the steady phase, while a difference in the distance gap between cars and trucks appeared in the early deceleration phase. We also obtained the characteristic behaviours of these vehicle types in different driving phases. Our findings indicate that the car-following characteristics of various types of vehicles are affected by the recognition of vehicle agility. Furthermore, we propose the necessary model parameters for replicating these features in different driving phases.

Influence of unmanned combat aerial vehicle agility on short-range aerial combat effectiveness

The flight agility of an unmanned combat aerial vehicle (UCAV) determines its ability to rapidly transition from one state to another. In this paper, the influence of flight agility on short-range aerial combat effectiveness is quantitatively investigated. This research is based on one-on-one three-dimensional aerial combat engagements. First, a 6-DOF mathematical model of the UCAV is established, and a nonlinear dynamic inverse controller is designed. The flight agility is calculated based on the 180∘ flight heading reverse maneuver, and the influence of the control law parameters is studied. To implement autonomous intelligent aerial combat engagements, a three-dimensional approximate dynamic programming method is proposed. The performance of the designed algorithm is validated through both Monte Carlo simulations with various initial conditions and engagements with another algorithm for comparison. After training, the aerial combat simulation framework is constructed by combining the point-mass-based guidance law and a nonlinear UCAV model. Combat engagements are conducted between UCAVs with different configurations. The quantitative results are evaluated through Monte Carlo simulations and correlation analysis.

Front/Rear Axle Torque Vectoring Control for Electric Vehicles

Vehicles equipped with multiple electric machines allow variable distribution of propulsive and regenerative braking torques between axles or even individual wheels of the car. Left/right torque vectoring (i.e., a torque shift between wheels of the same axle) has been treated extensively in the literature; however, fewer studies focus on the torque shift between the front and rear axles, namely, front/rear torque vectoring, a drivetrain topology more suitable for mass production since it reduces complexity and cost. In this paper, we propose an online control strategy that can enhance vehicle agility and “fun-to-drive” for such a topology or, if necessary, mitigate oversteer during sublimit handling conditions. It includes a front/rear torque control allocation (CA) strategy that is formulated in terms of physical quantities that are directly connected to the vehicle dynamic behavior such as torques and forces, instead of nonphysical control signals. Hence, it is possible to easily incorporate the limitations of the electric machines and tires into the computation of the control action. Aside from the online implementation, this publication includes an offline study to assess the effectiveness of the proposed CA strategy, which illustrates the theoretical capability of affecting yaw moment that the front/rear torque vectoring strategy has for a given set of vehicle and road conditions and considering physical limitations of the tires and actuators. The development of the complete strategy is presented together with the results from hardware-in-the-loop (HiL) simulations, using a high fidelity vehicle model and covering various use cases.

Cooperative chassis control system of electric vehicles for agility and stability improvements

This study describes a cooperative chassis control system that controls longitudinal motion in accordance with the yaw movement for electric vehicles. This system can be used to improve vehicle agility and stability using the integration of torque distribution unit and electronic stability control (ESC). Moreover, this system can assist drivers smoothly navigate through a curve before ESC intervention. The structure of the proposed control system is fundamentally a model following controller, thereby making the vehicle follow the desired instantaneous handling characteristics by regulating the feedforward of the cornering stiffness, state feedback of longitudinal acceleration, and front and rear drive ratios. Experiments are performed to demonstrate the effectiveness of the proposed control system. The maximum steering angle during cornering is confirmed to be significantly reduced with proper deceleration/acceleration control and adjustment of the drive torques of the front and rear axles. Moreover, trajectory tracking can be significantly improved. The proposed control strategy can be used to assist intelligent vehicles to plan a reasonable trajectory, thereby enabling these vehicles to safely and rapidly pass corners or avoid obstacles while ensuring safety.

Vehicle optimal road departure prevention via model predictive control

This article addresses the problem of road departure prevention using integrated brake control. The scenario considered is when a high-speed vehicle leaves the highway on a curve and enters the shoulder or another lane, owing to excessive speed or a reduction in the friction of the road due to adverse weather conditions. In such a scenario, the vehicle speed is too high for the available tyre–road friction and road departure is inevitable; however, its effect can be minimized with an optimal braking strategy. To achieve online implementation, the task is formulated as a receding horizon optimization problem and solved in a linear model predictive control (MPC) framework. In this formulation, a nonlinear tyre model is adopted in order to work properly at the friction limits. The optimization results are close to those obtained previously using a particle model optimization, parabolic path reference (PPR), coupled to a control algorithm, the modified Hamiltonian algorithm (MHA), specifically designed to operate at the vehicle friction limits. This shows that the MPC formulation may be equally effective for vehicle control at the friction limits. The major difference here, compared with the earlier PPR/MHA control formulation, is that the proposed MPC strategy directly generates an optimal brake sequence, while PPR provides an optimal reference first, then MHA responds to the reference to give closed-loop actuator control. The presented MPC approach has the potential for use in future vehicle systems as part of the overall active safety control to improve overall vehicle agility and safety.

Optimal control of motorsport differentials

Modern motorsport limited slip differentials (LSD) have evolved to become highly adjustable, allowing the torque bias that they generate to be tuned in the corner entry, apex and corner exit phases of typical on-track manoeuvres. The task of finding the optimal torque bias profile under such varied vehicle conditions is complex. This paper presents a nonlinear optimal control method which is used to find the minimum time optimal torque bias profile through a lane change manoeuvre. The results are compared to traditional open and fully locked differential strategies, in addition to considering related vehicle stability and agility metrics. An investigation into how the optimal torque bias profile changes with reduced track-tyre friction is also included in the analysis. The optimal LSD profile was shown to give a performance gain over its locked differential counterpart in key areas of the manoeuvre where a quick direction change is required. The methodology proposed can be used to find both optimal passive LSD characteristics and as the basis of a semi-active LSD control algorithm.

MODIFIED HAMILTONIAN ALGORITHM FOR OPTIMAL LANE CHANGE WITH APPLICATION TO COLLISION AVOIDANCE

This paper deals with collision avoidance for road vehicles when operating at the limits of available friction. For collision avoidance, a typical control approach is to: (a) define a reference geometric path that avoids collision; (b) apply low-level control to perform path following. However, there are a number of limitations in this approach, which are addressed in the current paper. First, it is typically unknown whether a predefined reference path is feasible or over-conservative. Secondly, the control scheme is not well suited to avoiding a moving object, e.g. another vehicle. Further: incorrect choice of reference path may degrade performance, fast adaptation to friction change is not easy to implement and the associated low-level control allocation may be computationally intensive. In this paper we use the general nonlinear optimal control formulation, include some simplifying assumptions and base optimal control on the minimization of an underlying Hamiltonian function. A particle model is used to define an initial reference in the form of a desired global mass-center acceleration vector. Beyond that, yaw moment is taken into account for the purpose of enhancing the stability of the vehicle. The Hamiltonian function is adapted as a linear function of tyre forces and can be minimized locally for individual wheels; this significantly reduces computational workload compared to the conventional approach of forcemoment allocation. Several combinations of actuators are studied to show the general applicability of the control algorithm based on a linear Hamiltonian function. The method has the potential to be used in future vehicle control systems across a wide range of safety applications and hence improve overall vehicle agility and improve safety.

A yaw-moment control method based on a vehicle's lateral jerk information

Previously, a new control concept called ‘G-vectoring control (GVC)’ to improve vehicle agility and stability was developed. GVC is an automatic longitudinal acceleration control method that responds to vehicle lateral jerk caused by the driver's steering manoeuvres. In this paper, a new yaw-moment control method, which generates a stabilising moment during the GVC command and has positive acceleration value and the driver's accelerator pedal input is zero, was proposed. A new hybrid control, which comprises GVC, electric stability control and this new control, was constructed, and it was installed in a test vehicle and tested on a snowy surface. The very high potential for improvement in both agility and stability was confirmed.

Comparison and combination of direct yaw-moment control and G-Vectoring control

Previously, we developed a new control concept called ‘G-Vectoring control (GVC)’ to improve vehicle agility and stability. GVC is an automatic longitudinal acceleration control method that responds to vehicle lateral jerk caused by a driver's steering manoeuvres. In this paper, we compare GVC with the well-known direct yaw-moment control (DYC) method for daily driving ranges in particular. GVC shows good manoeuvrability (i.e. enhances both yawing and lateral acceleration) performance while maintaining a natural yaw and roll feeling in the early stage of cornering, while DYC is effective in improving vehicle stability during large lateral motions. We also re-review our previously proposed ‘hybrid control’ method that combines the strengths of GVC and DYC. A side-by-side test carried out for the hybrid control method and DYC only confirms the effectiveness of the hybrid control method.

Improvement in vehicle agility and stability by G-Vectoring control

We extracted a trade-off strategy between longitudinal traction/braking force and cornering force by using jerk information through observing an expert driver's voluntary braking and turning action. Using the expert driver's strategy, we developed a new control concept, called ‘G-Vectoring control’, which is an automatic longitudinal acceleration control (No DYC) in accordance with the vehicle's lateral jerk caused by the driver's steering manoeuvres. With the control, the direction of synthetic acceleration (G) changes seamlessly (i.e. vectoring). The improvements in vehicle agility and stability were evaluated by theoretical analysis and through computer simulation. We then introduced a ‘G-Vectoring’ equipped test vehicle realised by brake-by-wire technology and executed a detailed examination on a test track. We have confirmed that the vehicle motion in view of both handling and ride quality has improved dramatically.

The Design and Performance Analysis of Multi-Axle Dynamic Steering System

In order to improve vehicle steering performance, Multi-axle dynamic steering technology is put forward. Because of simple and low cost, the mechanical dynamic steering mechanism is suitable for heavy vehicle. In order to design ideal steering mechanism, the principle of dynamic steering mechanism was analyzed. Based on theories of multi-axle dynamic steering and vehicle dynamic, the steering angle relationships of different axles were analyzed. Taken a vehicle as an example, the corresponding steering mechanism was designed. Full vehicle model was established and handling stability performance was simulated. The results show that the mechanical dynamic steering vehicle can effectively improve vehicle agility performance at low speed and stability at high speed.

操縦安定性改善のためのアクティブスタビライザと電動パワーステアリングの分散制御系設計(<特集>D&D2009)

In this paper, a distributed control system for an active stabilizer and an electric power steering (EPS) is designed in order to improve the performance on vehicle agility and stability. A full vehicle simulation model with the active stabilizer and the EPS is derived and used to evaluate the performance for the distributed controllers design. A controller for the active stabilizer is designed using the linear quadratic (LQ) control theory to balance competing goals for the roll reduction of a vehicle body during cornering and the improvement of the ride comfort on a rough road and to especially take the road surface disturbance to the steering system into account. Also a controller for the EPS considering the self-aligning torque generated from the vehicle system with the stabilizer is designed to assist the steering of the driver by applying the H_∞ control theory. It is verified that the distributed control system applied to the full vehicle model is effective to not only reduce the roll motion and the steering torque during cornering but also improve the driving stability and the ride comfort on the rough road.

Real-Time Computation of Spatial and Flat Obstacle Avoidance Trajectories for UUVs

This paper elaborates on the previous paper (Horner and Yakimenko, 2007) and develops a framework for planning obstacle avoidance trajectories in real-time. The proposed obstacle avoidance algorithm relies on the relevant situational awareness data provided by forward-looking sonar. Based on this information, current objectives, UUV attitude, vehicle agility and autopilot performance, an onboard computer chooses between avoiding newly discovered obstacles in the vertical plane, horizontal plane, or performing a spatial three-dimensional manoeuvre. Once information about an obstacle, which can be moving, is updated the obstacle-avoiding trajectory should be updated as well. Therefore, an UUV follows the originally determined trajectory only between two consecutive updates. One of the key features of the proposed approach is that a new trajectory should start from the current conditions defined by position, velocity and acceleration. Having the rate of situational awareness data update as fast as 1Hz, puts another severe constraint on the computational effectiveness of the path generation algorithm. These features rule out the usage of all but one known approaches. The advocated approach is the direct method of calculus of variations based on inverse dynamics in the virtual domain. The paper presents a detailed derivation of computational routine and demonstrates the results of some preliminary simulations.

Load change reactions of passenger cars: Method of investigation and improvement

Customer demand for good vehicle agility consists of a quick reaction of the vehicle with actuation of the throttle pedal on the one hand and a high comfort level with respect to vibration and noise within the vehicle on the other hand, which means a reduction in disturbing side effects. In order to achieve a satisfactory compromise it is necessary to gain a deeper understanding of the complex, high dynamic vibration ‘vehicle/drivetrain’ system. For 2 years ika has been carrying out such detailed vehicle investigations and test bench measurements in addition to comprehensive computer aided engineering (CAE) analysis for various research projects.