Vehicle Handling Research Articles

Study of Yaw Moment Control Strategy of Four Wheel Independent Drive Electric Vehicle

Abstract A yaw moment control strategy for four wheel independent drive electrics vehicle is proposed in this paper. The control strategy is a hierarchical architecture which containing a yaw motion generation layer and a longitudinal force distribution layer. The yaw motion generation layer consists of feedforward control and feedback control. Unlike previous strategy, in this paper, yaw rate is considered as the only control variable which is feasible to be detected in practice. The feedforward control is used to enhance overshoot in transient condition while the feedback control is to change vehicle steady state steering characteristic and extend its lateral limit. The longitudinal force distribution layer is designed based on vehicle wheel load transfer model. It makes each tire fully utilized its lateral force limits. Based on these two layers, a control model is built accordingly. Both steady state and transient state experiments are conducted in Carsim simulation associated with the built Simulink control model. The simulation results show that proposed control strategy can enhance vehicle handling performance in steady state and transient state. Experiments were conducted on an electric vehicle which evaluated the accuracy of the established model and the effect of the proposed yaw moment control strategy.

Dynamic Envelope Optimization of Articulated Vehicles Based on Multi-Axle Steering Control Strategies

Steer-by-wire technology, critical for autonomous driving, enables full-wheel steering in articulated vehicles, significantly enhancing maneuverability in complex driving environments. This study investigates dynamic envelope optimization for articulated multi-body vehicles by integrating coordinated multi-axle steering control strategies with higher-order Bezier curve designs. Unlike traditional approaches that primarily focus on single-axle steering, this research emphasizes the advantages of multi-axle steering control, which significantly reduces the dynamic envelope and enhances maneuverability. To address the challenges posed by constrained road environments, a comparative analysis of Septimic Bezier curves under various control point configurations was conducted, demonstrating their effectiveness in achieving smoother curvature transitions and steering comfort. The results highlight the pivotal role of reducing curvature peaks and increasing curvature continuity in optimizing vehicle performance. Furthermore, advanced steering control strategies, such as Articulation Angle Reference (AAR) and Dual Ackermann Steering (DAS), were shown to outperform conventional methods by ensuring precise trajectory control and improved stability. This study provides actionable insights for enhancing vehicle handling and safety in complex driving scenarios, offering a framework for future road design and multi-axle steering system development.

Research on Lateral Stability Control of Four-Wheel Independent Drive Electric Vehicle Based on State Estimation.

This paper proposes a hierarchical framework-based solution to address the challenges of vehicle state estimation and lateral stability control in four-wheel independent drive electric vehicles. First, based on a three-degrees-of-freedom four-wheel vehicle model combined with the Magic Formula Tire model (MF-T), a hierarchical estimation method is designed. The upper layer employs the Kalman Filter (KF) and Extended Kalman Filter (EKF) to estimate the vertical load of the wheels, while the lower layer utilizes EKF in conjunction with the upper-layer results to further estimate the lateral forces, longitudinal velocity, and lateral velocity, achieving accurate vehicle state estimation. On this basis, a hierarchical lateral stability control system is developed. The upper controller determines stability requirements based on driver inputs and vehicle states, switches between handling assistance mode and stability control mode, and generates yaw moment and speed control torques transmitted to the lower controller. The lower controller optimally distributes these torques to the four wheels. Through closed-loop Double Lane Change (DLC) tests under low-, medium-, and high-road-adhesion conditions, the results demonstrate that the proposed hierarchical estimation method offers high computational efficiency and superior estimation accuracy. The hierarchical control system significantly enhances vehicle handling and stability under low and medium road adhesion conditions.

Vehicle Subframe Effects on Frontal Collision

Ensuring passenger safety is a crucial consideration in designing vehicles. Forces that are transmitted to the passenger compartment, particularly during collisions, can jeopardize the safety of the passengers. Hence, minimizing forces in the vehicle body and chassis plays a crucial role in enhancing passenger safety. Many vehicles with a unibody chassis feature a subframe as a supplementary element. The primary contribution of the subframes to the performance of the vehicle is the reduction of noise and vibration. However, subframes can also improve factors such as suspension stiffness, vehicle handling, and vehicle stability. This paper examines the effect of the presence and design modifications to the subframe on vehicle body forces. To achieve this, five subframe designs with different dimensions and materials are modeled. They are then installed on a full-scale vehicle model, and frontal collisions with a barrier and a pole are simulated. The vehicle body forces during various cases of frontal collisions are studied by measuring the acceleration and acceleration magnitude of three points on the B-pillar in the time domain and frequency domains. The results indicate that a subframe can reduce vehicle body forces, with the subframe's design influencing the extent of this effect.

Cooperative Control of Distributed Drive Electric Vehicles for Handling, Stability, and Energy Efficiency, via ARS and DYC

Distributed drive electric vehicles (DDEV), characterized by their independently drivable wheels, offer significant advantages in terms of vehicle handling, stability, and energy efficiency. These attributes collectively contribute to enhancing driving safety and extending the all-electric range for sustainable transportation. Nonetheless, the challenge persists in designing a control strategy that effectively coordinates the objectives of handling, stability, and energy efficiency under both lateral and longitudinal driving conditions. To this end, this paper proposes a cooperative control strategy for DDEVs, incorporating active rear steering (ARS) and direct yaw moment control (DYC) to enhance handling capabilities, stability, and energy efficiency. A stability boundary is delineated using an analytical expression that correlates with the front wheel steering angle, and an adjustment factor is introduced to quantify vehicle stability based on this input parameter. This factor aids in establishing a coordinated control reference for handling and stability. At the upper-level motion control layer, a model predictive control method is developed to track this reference and implement ARS and DYC for superior performance. Specifically, the rear lateral force serves as the control command for ARS, which is converted into a rear wheel steering angle using a tire inverse model. Meanwhile, the front lateral force is modeled as linear-time-varying to simplify calculations. At the lower-level torque allocation layer, the adjustment factor is utilized to balance tire workload rate and in-wheel motors’ (IWM) energy consumption, enabling efficient switching between energy consumption and driving stability targets, and the torque allocation is conducted to acquire the expected IWMs’ command. Both the upper and lower-level optimization problems are formulated as convex problems, ensuring efficient and effective solutions. Simulations verify the effectiveness of this strategy in improving handling, stability, and energy economy under DLC cases, while maintaining high computational efficiency.

Research on active steering control strategy of line-controlled steering system based on MPC

In order to improve the stability of the steer by wire (SBW) system during steering, this paper adopts a control strategy based on model predictive control (MPC) to achieve active steering control of the SBW system. Based on the fixed steering gain of the vehicle, design an ideal angular transmission ratio curve, and determine the stability of the vehicle's driving by controlling the yaw rate and center of mass lateral angle, achieving active steering function of the vehicle. Based on Simulink and Carsim platform, establish a vehicle model and analyze the frequency response characteristics of the steering actuator assembly to verify its working stability. The simulation results show that the designed control strategy can significantly improve vehicle handling stability.

Model predictive control parameter optimization method for autonomous vehicles

Aiming at the problem of insufficient tracking accuracy and long computational time of model predictive control algorithm in trajectory tracking of autonomous vehicles, a model predictive control parameter optimization method based on a nondominated sorting whale optimization algorithm was proposed. In this method, the parameter optimization problem of the model predictive control algorithm was transformed into a multiobjective optimization problem, with the predictive horizon, control horizon, and sample time as optimized objectives and the sum of squared lateral trajectory errors and computational time as optimized variables. The multiobjective optimization problem was solved using the nondominated sort whale optimization algorithm, and then the Pareto optimal solution set was obtained. The best solution was determined using the expert scoring method, continuous order weighted average operator method, game theory combination weighting method, and technique for order preference by similarity to the ideal solution method. Finally, the phase plane method is used to analyze the vehicle handling stability. The experimental results show that compared with the initial method, the lateral trajectory errors of the proposed method are reduced by 66.09% on average, and the computational time is reduced by 54.68% on average. Therefore, the proposed method is considered to be an effective method for parameter optimization of model predictive control.

The direct yaw-moment control based on adaptive fuzzy LQR for distributed drive electric vehicles

This paper introduced a hierarchical control strategy for direct yaw moment (DYC) to enhance the handling and stability of distributed drive electric vehicles (DDEVs) at medium to high speeds. The upper controller entailed a speed-following PI controller and an adaptive fuzzy linear quadratic regulator (AFLQR) controller, with the control objectives centered on reducing the absolute value of the sideslip angle and tracking the desired yaw rate. The proposed approach utilizes a fuzzy logic-based AFLQR controller, which could dynamically adjust the weighting parameters for sideslip angle and yaw rate in response to the vehicle speed and sideslip angle, offering better adaptability to varying driving conditions. At the lower control level, a tire-dynamic-load-based torque distribution method was applied. The control strategy’s efficacy was demonstrated through co-simulation involving CarSim and Simulink. This evaluation compared AFLQR control against non-yaw control, conventional LQR control and sliding mode control (SMC), focusing on handling and stability during sinusoidal steering wheel input test and double lane change maneuver. Results highlight that AFLQR reduces the sideslip angle by 7.88% and the yaw rate error by 84.29% compared to LQR, enhancing vehicle handling and stability. Lastly, a hardware-in-the-loop (HIL) experiment verified the control strategy’s validity.

On the Lateral Stability System of Four-Wheel Driven Electric Vehicles Based on Phase Plane Method

To improve the handling and stability of four-wheel independent drive electric vehicles (FWID EVs), this paper introduces a hierarchical architecture lateral stability control system. The upper-level controller is responsible for generating the additional yaw moment required by the vehicle. This includes a control strategy based on feedforward control and a Linear Quadratic Regulator (LQR) for handling assistance control, an LQR-based stability control, a PID controller-based speed-following control, and a stability assessment method. The lower-level controller uses Quadratic Programming (QP) to optimally distribute the additional yaw moment to the four wheels. A “normalized” method was proposed to determine vehicle stability. After comparing it with the existing double-line method, diamond method, and curved boundary method through the open-loop Sine with Dwell test and the closed-loop Double Lane Change (DLC)test simulation, the results demonstrate that this method is more sensitive and accurate in determining vehicle stability, significantly enhancing vehicle handling and stability.

Active suspension using a PID controller with an inerter device

This article is dedicated to a thorough investigation of the behavior of an active suspension system that incorporates the PID controller in a vehicle quarter, adding an inerter component to the system. Through comprehensive analysis of a variety of track profiles, the active suspension system demonstrates its ability to precisely control wheel movements, with the primary aim of improving passenger comfort and vehicle handling. By using the PID controller in conjunction with track sensors, the system is able to continuously evaluate road conditions, identifying the most effective movements to optimize the driving experience. The PID controller, incorporating proportional, integral, and derivative components, plays a fundamental role in minimizing error, integrating corrections, and adjusting response time as needed. The inerter, an innovative mechanical device with two terminals, is introduced to provide inertia without adding mass to the system, utilizing the difference in angular acceleration between the terminals. To validate and compare the performance of the active suspension system with and without the inerter, detailed simulations are conducted using the powerful tools of MATLAB and Simulink. The inclusion of the inerter in the active suspension system, along with the PID controller, aims to enhance the overall effectiveness of the system, even if it results in a slight increase in manufacturing costs. However, the substantial benefits in terms of passenger comfort and vehicle safety fully justify this additional investment. This approach represents a significant advancement in the field of automotive suspension systems, offering an ideal balance between performance, cost, and user satisfaction.

Tire wear monitoring using feature fusion and CatBoost classifier

Addressing the critical issue of tire wear is essential for enhancing vehicle safety, performance, and maintenance. Worn-out tires often lead to accidents, underscoring the need for effective monitoring systems. This study is vital for several reasons: safety, as worn tires increase the risk of accidents due to reduced traction and longer braking distances; performance, as uneven tire wear affects vehicle handling and fuel efficiency; maintenance costs, as early detection can prevent more severe damage to suspension and alignment systems; and regulatory compliance, as ensuring tire integrity helps meet safety regulations imposed by transportation authorities. In response, this study systematically evaluates tire conditions at 25%, 50%, 75%, and 100% wear, with an intact tire as a reference, using vibration signals as the primary data source. The analysis employs statistical, histogram, and autoregressive–moving-average (ARMA) feature extraction techniques, followed by feature selection to identify key parameters influencing tire wear. CatBoost is used for feature classification, leveraging its adaptability and efficiency in distinguishing varying wear patterns. Additionally, the study incorporates feature fusion to combine different types of features for a more comprehensive analysis. The proposed methodology not only offers a robust framework for accurately classifying tire wear levels but also holds significant potential for real-time implementation, contributing to proactive maintenance practices, prolonged tire lifespan, and overall vehicular safety.

Bionic Optimization Design and Fatigue Life Prediction of a Honeycomb-Structured Wheel Hub.

The wheel hub is an important component of the wheel, and a good hub design can significantly improve vehicle handling, stability, and braking performance, ensuring safe driving. This article optimized the hub structure through morphological aspects, where reducing the hub weight contributed to enhanced fuel efficiency and overall vehicle performance. By referencing honeycombed structures, a bionic hub design is numerically simulated using finite element analysis and response surface optimization. The results showed that under the optimization of the response surface analytical model, the maximum stress of the optimized bionic hub was 109.34 MPa, compared to 119.77 MPa for the standard hub, representing an 8.7% reduction in maximum stress. The standard hub weighs 34.02 kg, while the optimized hub weight was reduced to 29.89 kg, a decrease of 12.13%. A fatigue analysis on the optimized hub indicated that at a stress of 109.34 MPa, the minimum load cycles were 4.217 × 105 at the connection point with the half-shaft, meeting the fatigue life requirements for commercial vehicle hubs outlined in the national standard GB/T 5334-2021.

Explicit Uncertainty Quantification for Probabilistic Assessment of Rotorcraft Handling Qualities

Early-stage vehicle design processes are typically subject to significant uncertainty in aerodynamic loads and control responses. In many cases, it may be necessary to consider this uncertainty in preliminary evaluations of stability, handling qualities, and performance metrics. This article introduces a novel methodology for computing these quantities in a probabilistic framework using the Koopman operator. The algorithm discretizes the uncertainty space and uses relevant transformations or simulations to map each point to the handling qualities evaluation domain, resulting in a stability, handling quality, and/or performance assessment for each discretized point. The expected value of the stability, handling quality, and/or performance requirement is then computed via quadrature integration, yielding a probabilistic assessment with respect to relevant specifications. The methodology is demonstrated through two examples involving a quadrotor unmanned aerial system and a generic utility helicopter. In the first example, the methodology is applied to evaluate the handling qualities of a quadrotor, for which a multiloop dynamic inversion controller is developed. In the second example, the method is used to quantify the impact of parametric uncertainty in a pilot model on specific pilot???vehicle system performance specifications and to predict vehicle handling qualities. The proposed explicit uncertainty quantification approach is shown to provide a convenient framework for the propagation of parametric uncertainty to stability, handling quality, and performance specifications with unique advantages over Monte Carlo techniques.

Design and research of face gear central limited slip differential and analysis of its influence on vehicle performance

In this paper, a new design scheme of face gear central limited-slip differential (F-LSD) is proposed, which aims to unveil the technical veil of central limited-slip differential and fill the research blank of face gear application in the field of central limited-slip differential. Through the analysis of structural principle and motion characteristics, the dynamic equilibrium equation and locking coefficient model of F-LSD are established. The Simulink simulation model based on F-LSD dynamics and the torque distribution model of the whole transmission system are established. A 7-DOF vehicle dynamics model was established, and the performance of F-LSD vehicle in four conditions of acceleration, climbing, chassis twist sine wave and double line change was studied by using simulation software. At the same time, four groups of four-wheel drive vehicles with different central differential speeds were set as the control test group. The simulation results show that F-LSD system can effectively improve the vehicle's dynamic performance and extrication performance, and will not reduce the vehicle's handling stability, which provides a reference for the practical application of the face gear central limited slip differential.

Vehicle State Estimation by Integrating the Recursive Least Squares Method with a Variable Forgetting Factor with an Adaptive Iterative Extended Kalman Filter

The sideslip angle and the yaw rate are the key state parameters for vehicle handling and stability control. To improve the accuracy of the input parameters and the time-varying characteristics of noise covariance in state estimation, a combined method of recursive least squares with a variable forgetting factor and adaptive iterative extended Kalman filtering is proposed for estimation. Based on the established three-degrees-of-freedom nonlinear model of the vehicle, the variable forgetting factor recursive least squares method is used to identify the tire cornering stiffness and serves as an input for vehicle state estimation. An innovative algorithm is used to optimise the uncertain noise covariance in the iterative extended Kalman filter (IEKF) process. Finally, with the help of the joint simulation of CarSim2019 and Matlab/Simulink R2022a, a distributed drive electric vehicle state parameter estimation model is established, and a simulation analysis of typical working conditions is carried out. Furthermore, an experiment is conducted with the pix moving vehicle and the integrated navigation system. The simulation and experimental results show that, compared to the traditional extended Kalman filter algorithm, the proposed algorithm improves the estimation accuracy of the yaw rate, sideslip angle, and longitudinal speed by 58.17%, 57.2%, and 76.47%, respectively, which shows that the algorithm has a higher estimation accuracy and a stronger applicability to provide accurate state information for vehicle handling and stability control.

Research on optimization of the vehicle handling stability considering flexibility of the front subframe

The elastic deformation of the front subframe during vehicle traveling affects the vehicle handling stability. The static equilibrium equations of the suspension and subframe system and the kinematics equations of the three-degree-of-freedom vehicle are established to illustrate the principle of the influence of the flexible front subframe on the handling stability. The front subframe is flexibly processed to establish a rigid-flexible coupled vehicle model. Simulations of the suspension kinematic and compliance alongside sine swept steering simulation of the vehicle are performed. The front subframe bushing stiffness, which has a greater impact on the handling stability, is selected as a design variable through parametric test design, and the NSGA-II algorithm is used for multi-objective optimization with the vehicle handling stability evaluation index as the optimization target. The simulation results show that the flexible front subframe will make the bushing force change to affect the suspension kinematic and compliance characteristics and then affect the vehicle handling stability, the gain of vehicle yaw rate and the delay time of lateral acceleration decrease, while the gain of roll angle increases. The optimization results show that the optimized vehicle exhibits improvements in the yaw rate gain, delay time of lateral acceleration, and roll angle gain.

The evolution of damper technology for enhanced ride comfort and vehicle handling in vehicle suspension system

The evolution of damper technology for enhanced ride comfort and vehicle handling in vehicle suspension system

Multi-parameter and multi-objective optimization of dual-fuel cell system heavy-duty vehicles: Sizing for serial development

Dual-fuel cell hybrid system provides an attractive propulsion option in transportation, especially for heavy-duty vehicles. However, the larger vehicle weight improves the sensitivity of power demand to road conditions and vehicle handling, making it a challenge to realize reasonable sizing. The scope of this work is to demonstrate a multi-objective and multi-parameter optimization for the serial development of the heavy-duty vehicle, powered by a dual-fuel cell hybrid system. Toward this end, a comprehensive modeling is presented combining the degradation model of the FC system and the battery system. The Pareto theory is introduced to evaluate the three-dimensional objectives involving the equivalent hydrogen consumption, the mass goal, and the vehicle dynamic, which is derived from different six-dimensional parameters under a dual-layer optimization approach. The brute force approach is not applicable in the presence of the curse of dimensionality arising from multi-parameter optimization. The proposed methodology offers a viable approach to acquiring rational sets of sizing solutions in the optimization space with high-dimensional parameters. Considering the serialization of products, the improved solution and the corresponding performance upper limit have be determined according to the proposed methodology under different weight levels as well.

Modelling and experimental validation of an adaptive interconnected suspension with adjustable roll stiffness (AIS-ARS)

Vehicle suspensions play a critical role in improving vehicle stability and ride quality, especially in heavy vehicles that usually have high centre-of-gravity and carry tons of cargo. Interconnected suspension systems, in which the suspension struts are connected through hydraulic hoses and flow control devices, have great potential to enhance vehicle handling stability and attenuate vibrations. This study introduces an adaptive interconnected suspension with adjustable roll stiffness (AIS-ARS) to address the shortcomings of existing designs. It not only eliminates the conventional anti-roll bars but also enables adjustment of roll stiffness depending on the driving and vehicle load conditions. The AIS-ARS system uses a unique design that avoids using expensive electro-proportional flow control valves, which has been a significant barrier to widespread implementation. Instead, the system utilises a cost-effective control strategy that uses only two solenoid valves to achieve adaptive damping. The mathematical modelling of the AIS-ARS is also straightforward, requiring less computational power and making it more practical for real-time implementations. Overall, the AIS-ARS system represents a significant advancement in the design of interconnected suspension systems, offering a more cost-effective, practical, and versatile solution. The above features are validated through laboratory experiments and co-simulation between MATLAB/Simulink and ADAMS/Car.

Optimal and robust control methodologies for 4DOF active suspension systems: A comparative study with uncertainty considerations

AbstractDrawing from recent developments in the field, this article explores advanced control methodologies for active suspension systems with the aim of enhancing ride comfort and vehicle handling. The study systematically and comprehensively implements, simulates, and compares five control methods: Proportional‐integral‐derivative (PID), linear quadratic regulator (LQR), , , and synthesis in the context of half‐vehicle active suspension systems. By using a detailed system model that includes parameter uncertainties and performance weights, analysis, and simulations are conducted to evaluate the performance of each control approach. The results provide valuable insights into the strengths and limitations of these methods, offering a comprehensive comparative analysis. Notably, the study reveals that control may not ensure stability for all possible combinations within a broad range of uncertainties, indicating the need for careful consideration in its application. The results and simulations thoroughly evaluate and compare the performance of each control strategy across various output responses, contributing to the advancement of more effective and reliable active suspension systems.