With the accelerated development of vehicle electrification and intelligence, vehicle brakes are evolving from conventional purely mechanical components toward integrated electromechanical units, whose performance is directly related to driving safety. As a core component of braking systems, the force-amplifying transmission mechanism is regarded as a decisive factor influencing braking performance and has consequently attracted sustained research interest. A systematic review of commonly used force-amplifying transmission mechanisms in automotive brakes is presented, including lever-type, wedge-slider, and screw-based configurations, with emphasis placed on their operating principles, performance characteristics, and application progress, together with a comparative analysis of their respective advantages and limitations. Subsequently, the current application status of key technologies, such as multi-objective topology optimization, contact stress analysis, and fatigue life prediction, in the design of force-amplifying transmission mechanisms is discussed, highlighting the necessity of coordinated optimization between lightweight design and structural strength. Furthermore, the application of force-amplifying transmission mechanisms in electro-mechanical brake(EMB) systems is reviewed, with particular emphasis placed on recent technological advances and remaining challenges associated with ball-ramp force amplification and ball screw mechanisms. Finally, future development trends of force-amplifying transmission mechanisms are explored, indicating that breakthroughs in precision manufacturing, innovations in dual-layer or non-conventional force-amplifying structures, and the integration of intelligent algorithms are required to resolve the inherent trade-offs among high amplification ratio, dynamic response, and durability, thereby meeting the increasingly stringent demands of intelligent vehicles for braking response speed, control accuracy, and system reliability.

As the trends of electrification, connectivity, sharing, and intelligence in automobiles require X-by-wire chassis to become more intelligent and agile, the active camber and toe suspension system(ACTS) enhances vehicle maneuverability and stability through active wheel alignment adjustments. Current controllable suspension systems often use serial design approaches, where mechanical and control designs proceed step-by-step, making it difficult to obtain the global optimal solution of the system and fully exploit the potential of suspension mechanics and control. In this case, a system-level co-design optimization method is proposed for ACTS(SCOD-ACTS) to obtain a comprehensive optimal solution of mechanics and control for coordinated operation of multiple actuators in ACTS. During the design phase, the SCOD-ACTS parallelly and collaboratively optimizes the mechanical and control subsystems of ACTS, solving electromechanical system compatibility problems and improving ACTS vehicle dynamics performance; In the mechanical subsystem, a multivariate regression model characterizing the variable kinematics of both camber and toe is established, enabling comprehensive optimization of actuator displacements and kinematic characteristics to resolve multi-actuator coordination challenges; In the control subsystem, phase-plane vehicle state monitoring and active camber-toe coordination controllers are developed, with vehicle phase-plane self-stability boundaries and controller parameters being optimized to determine intervention timing and actuation strategies.. Simulation results show that under double-lane-change conditions at 108 km/h with road adhesion coefficient 0.85, the proposed SCOD-ACTS reduces peak yaw rate by 37.3% and peak sideslip angle by 49.3%, improving vehicle handling stability under extreme operating conditions.

Suspension is critical for vehicle ride comfort and operational stability. The current suspension design method cannot realize the topological configuration optimization of multi-joints(spherical, revolute and translational joints), and there are problems of unclear configuration and low convergence speed. Therefore, a topological design method of multi-joints parsing is proposed, the optimal design of topology and size is realized. Firstly, the suspension design space is discretized and the discrete nodes are constrained by the “three springs”. Then, the multi-spring rod model is established through the rod system between the discrete. Secondly, the quasi-static equilibrium equation is established to solve the state variable, with the goal of maximizing work transmittance efficiency and the kinematic characteristics such as wheel track, toe and camber as the constraints, the optimization model is established to realize the optimal design of topology and size. Finally, the identification algorithm is proposed to solve the problem of unclear configuration and low convergence speed. Many examples show that the proposed design method can not only realize the topology design in strict space, but also realize the innovative design of the new suspension configuration of multi-joints.

The automotive air spring rubber bellows is made of a rubber-double-layer cord composite material, featuring mechanical properties such as hysteresis nonlinearity, anisotropy, and rate dependence under large deformation conditions. To accurately describe the material mechanical behaviour of the bellows, the strain energy function per unit volume is ingeniously decoupled into four parts: rubber hyperelastic strain energy, cord stretching strain energy, rubber-cord angular shear strain energy, and rubber viscoelastic strain energy, based on the hierarchical structure of the bellows. A mapping relationship between engineering strain and engineering stress for the rubber bellows is established, and an anisotropic hyper-viscoelastic constitutive model for the air spring rubber bellows made of rubber-dual-layer cord composite is proposed. The result show that this model can effectively predict the hysteresis nonlinearity, anisotropy, and rate dependence of the rubber bellows, with the maximum relative error between the predicted and experimental data not exceeding 5.78%. The established model can effectively characterize the hyperelastic and viscoelastic mechanical behaviors of the air spring rubber bellows made of rubber-dual-layer cord composite, and the model has the characteristics of simplicity, ease of parameter determination, and accurate prediction. The research results provide a theoretical basis for the precise design and matching of the delayed nonlinearity mechanical properties of automotive air springs.

The transient dynamic characteristics of tires on μ-step roads are critical to vehicle handling performance. Based on the brush-model to analyze the contact relationship between tire and road, combined with the constitutive relationship and initial boundary conditions, the partial differential equations for the deformation of the tread element in the brush model under combined working conditions are derived. Mathematical expressions for tire forces and aligning moments based on tread element deformation are provided. The tread element deformation correction function under a single road surface is extended to complex dynamic road. Considering the influence of static and dynamic friction coefficients, numerical examples investigate the deformation behavior of the tread and the variation patterns of tire forces under different road surfaces, wheel torque, and front wheel angle excitations. Results indicate that under complex dynamic road surfaces, the deformation of tread elements is constrained by road surface adhesion conditions. And there are mutual coupling relationships between tread element deformations within the tire-road contact area. Under μ-step road boundary conditions, the deformation of tread elements on the original road surface exhibits transient response characteristics of a second-order underdamped system under a unit step input, ultimately converging to a tangent line determined by slip ratio or side slip angle. Additionally, the tire force response exhibits a certain degree of lag when the wheel crosses the boundary of the μ-step road. The lag time increases as the slip ratio or side slip angle decreases and as the wheel rolling speed decreases.

The analysis of actuator damping characteristics and accurate preview of the road ahead are crucial for the design of intelligent suspension control. Focusing on generalized functional safety, an intelligent suspension model predictive control considering damping force loss of the damper and preview perception deviation is proposed. A half-vehicle suspension dynamics model is built. Based on the characteristics of the electromagnetic valve damper, the impact of temperature changes in the electromagnetic valve coil on the damper performance is analyzed, and a damping force attenuation model for the electromagnetic valve damper is obtained. Based on augmented Kalman filtering, the elevation of the road ahead is estimated based on vehicle dynamics response to generate redundant information for road excitation. A preview information correction strategy is designed to correct the deviation of road elevation detection, and it is combined with the damping force attenuation model of the damper to construct an intelligent suspension fault-tolerant control strategy considering generalized functional safety. Through simulation and real vehicle test, the results show that the proposed intelligent suspension fault-tolerant strategy can compensate for the damping force loss of the damper, significantly reduce the adverse effects of road elevation detection deviation, and improve the fault-tolerant control performance of intelligent suspension for generalized functional safety, so as to guarantee the driving safety of intelligent vehicle.

A hierarchical fault-tolerant control strategy for active suspension is proposed to mitigate the increased risk of vehicle instability caused by actuator faults under complex operating conditions. The strategy is concerned on maintaining vehicle body attitude stability and is composed of a vehicle-body layer, a force-distribution layer, and an execution layer. The vehicle-body layer computes the required body control forces based on vehicle states; the force-distribution layer allocates target control forces to individual suspensions according to the body control force requirement; the execution layer issues suspension control commands to realize the prescribed forces. A fault-diagnosis observer is first designed to monitor the health status of the suspension actuators. Fault-tolerant control is then implemented based on the observer outputs. Under multi-actuator partial-failure conditions, a dynamic master-force-distribution method is developed, whereby force allocation coefficients are adjusted in the force-distribution layer according to the diagnostic results. Under single-actuator total-failure conditions, a three-actuator optimal fault-tolerant control method is formulated: vehicle body control forces are solved by an optimal control algorithm subject to the remaining actuators' force output ranges, and the allocation matrix is reconstructed in the force-distribution layer. At the execution layer, sliding-mode controllers are designed to robustly track each suspension's control force target. Simulation results show that, compared with conventional fault-tolerant control, the proposed methods improve vehicle stability: when multiple suspension actuators partially fail, the dynamic master-force-distribution method reduces actuation by the faulty suspensions and decreases the maximum body vertical displacement, pitch angle, and roll angle by 28.5%, 22.8%, and 34.2%, respectively. When a single suspension actuator completely fails, the three-actuator optimal fault-tolerant control method, which accounts for actuator output limits, suppresses body vertical vibration and reduces the maximum body vertical displacement and roll angle by 35.9% and 38.2%, respectively.

To address the issue of poor steering feel in commercial vehicle electro-hydraulic compound steering systems(EHCS) caused by nonlinearities, time-varying parameters, and load disturbances under traditional power-assist control, a power-assist control strategy based on driver steering torque tracking is proposed. The strategy decouples steering feel design from the EHCS control structure, ensuring that the target torque design does not compromise the robustness of the tracking controller. A target steering torque map is constructed using experimental data, with vehicle speed, steering wheel angle, and angular velocity as inputs. Furthermore, a reduced-order extended state observer is employed to estimate the rack load force in real time, thereby improving the accuracy of torque tracking and the ability to reject road disturbances. Based on a nonlinear dynamic model incorporating parameter uncertainties and external disturbances, an adaptive integral terminal sliding mode controller is designed to generate appropriate motor assist torque, effectively compensating for hydraulic uncertainties and road-induced load disturbances, and enabling accurate tracking of the desired driver steering torque. The effectiveness of the proposed strategy is validated through co-simulation using MATLAB/Simulink and AMEsim, as well as hardware-in-the-loop testing under various working conditions and disturbance scenarios. Results demonstrate that the integration of the reduced-order extended observer and the adaptive integral terminal sliding mode controller significantly enhances steering torque tracking accuracy and system stability.

The electric motor steer-by-wire system without the road wheel angle sensor faces two major challenges: one is the real-time accurate acquisition of the steering resistance torque; the other is the real-time accurate angle control. This paper designs a low-calculation and high-precision proportional integral observer to achieve the accurate estimation of the system torque. For the steering dynamics presenting the typical second-order series system characteristics, this paper designs a backstepping angle tracking control algorithm. The Lyapunov function is further established to demonstrate that the sensorless steer-by-wire system using the designed control algorithm is asymptotically stable. A real vehicle chassis test platform using the sensorless steer-by-wire system is constructed, and the sinusoidal and slope tests are selected for the validation of the control algorithm. The experimental results show that the proportional integral observer can effectively estimate the system resisting moment, and the backstepping control algorithm based on the the proportional integral observer is able to track the target angle accurately in real-time.

The easy turn(EZT) function of electric vehicles is designed for scenarios with low speed and large steering wheel angles. Based on the normal driving of the vehicle, it reduces the steering radius of the vehicle and improves its maneuverability by applying braking torque to the inner steering wheel and controlling the torque increase of the drive motor simultaneously without affecting the original acceleration and deceleration characteristics of the vehicle. This paper conducts principle analysis, control strategy development, simulation and real vehicle verification of the EZT function of the single-motor front-wheel drive electric vehicle. Firstly, based on the single-track vehicle model, analyze the principle by which the EZT function reduces the steering radius of the vehicle. Secondly, with the goal of not affecting the acceleration and deceleration characteristics of the original vehicle, a matching strategy for the braking torque of the inner wheels of the EZT and the increment of the motor torque is designed, and the differences in the effect of reducing the steering radius of the vehicle between the braking of the inner rear wheel and the braking of the inner front wheel are analyzed. Secondly, a Carsim-Simulink joint simulation platform was established to simulate and verify the EZT control strategy. The results show that when the EZT function is activated, the steering radius of the vehicle can be reduced without affecting the original vehicle's acceleration and deceleration characteristics. In addition, when the same braking torque is applied, the reduction in the steering radius is greater when braking the inner rear wheel than when braking the inner front wheel. Finally, a real vehicle test platform was built, the steering radius estimation algorithm was designed, and the real vehicle verification of the EZT control strategy was carried out. The results show that the error of the steering radius estimation method is 0.4%, and the reduction of the steering radius increases approximately linearly with the increase of the braking torque of the inner rear wheel. The minimum steering radius can be reduced by 0.18 m without the wheels locking up.

Vehicle motion control requires real-time information on road surface unevenness to adjust control strategies, ensuring the safety, comfort, and efficiency of vehicle operation. Road surface unevenness is characterized by randomness and high uncertainty. Traditional methods based on time-domain and frequency-domain analysis of vibration signals are limited in their ability to provide accurate and predictable recognition results. With the development of vehicle intelligence, non-contact sensors such as cameras and Li DAR have enriched the sources of information for unevenness recognition. The application of artificial intelligence(AI) algorithms has further improved the precision of identification. This study focuses on road surface unevenness recognition for vehicle motion control, systematically analyzing the development, principles, technical challenges, and applications of three major technical approaches:contact-based methods, non-contact-based methods, and multi-source information fusion. Furthermore, by integrating the development trends of AI, the study provides insights into the future of pan-scenario, refined, and highly reliable road surface unevenness perception technologies.

To address the challenge of inaccurate longitudinal state estimation in four-wheel-drive(4 WD) vehicles under complex driving conditions, which limits the performance of traction control systems(TCS), this paper proposes a multi-source Information adaptive fusion algorithm(MIAFA). The algorithm adaptively fuses vehicle dynamics-based acceleration and wheel-speed-based velocity estimates according to signal reliability. Considering tire nonlinearity under slip conditions, a unified tire-road adhesion model is developed using longitudinal slip and lateral slip angle, combined with multi-sensor data such as IMU and steering angle. Kalman filtering is applied to estimate tire forces and adhesion coefficients, while the vehicle dynamics model provides longitudinal acceleration. To further enhance robustness, a wheel speed stability map based on slip ratio and acceleration is used to refine longitudinal velocity estimation. Simulation and experimental results on low-adhesion roads demonstrate that the proposed MIAFA improves state estimation accuracy under complex combined driving scenarios, effectively enhancing TCS performance in 4 WD vehicles.

The caliper braking force is the fundamental signal of the electronic mechanical braking system(EMB), and its accurate estimation is one of the core technologies of EMB. However, under anti lock braking(ABS) conditions, the caliper braking force exhibits high-frequency and large fluctuation characteristics, and conventional estimation methods are difficult to simultaneously meet the requirements of response speed and accuracy. In response to this issue, this paper proposes a high-speed motion clamp force estimation method based on corrected extended Kalman filter(C-EKF), and its effectiveness is verified through real vehicle testing. Firstly, establish a physical model of the EMB motor and caliper, and combine the braking force stroke data from actual vehicle testing to construct a force displacement fitting curve for the clamping and releasing process of the caliper. Secondly, analyze the characteristics of braking force changes at the moment of clamping and releasing under ABS working conditions, provide a calculation formula for instantaneous braking force changes, and estimate the braking force using the C-EKF method. Finally, the effectiveness of the method was verified through simulation and real vehicle testing. The experimental results show that the braking force estimation accuracy based on C-EKF has improved by 12.93% and 5.99% compared to Kalman filter(KF) and extended Kalman filter(EKF), respectively, and the response time has been shortened by 36.3 ms and 10.3 ms, respectively.

Electromechanical brake(EMB) exhibits advantages such as rapid response speed and high control accuracy, making it a crucial development trend in automotive brake-by-wire systems. To address industry challenges of inaccurate clamping force estimation and difficult-to-guarantee tracking control accuracy in EMB systems, this study proposes a clamping force estimation and tracking control strategy based on piecewise affine(PWA) identification. First, the structure and working principle of EMB systems were analyzed, establishing a comprehensive EMB system dynamics model that incorporates driving motor models, motor friction models, and transmission mechanism models, laying the foundation for subsequent clamping force tracking control strategy design. Second, experimental data of EMB clamping force variations with brake pad temperature and motor rotation angle were obtained. Based on this, a PWA identification-based EMB clamping force estimation model was developed, with its accuracy verified through comparative analysis of simulation and experimental data. Third, an EMB clamping force tracking control scheme integrating PID, nonsingular terminal sliding mode, and model predictive control algorithms was formulated, with functional allocation and design procedures determined by leveraging the performance characteristics of different control algorithms. Finally, simulation and experimental validation of the proposed EMB clamping force tracking control strategy were conducted. Results demonstrate that the proposed strategy effectively accelerates system response, reduces steady-state errors, and enhances tracking accuracy.

Aiming at the insufficient estimation accuracy of road adhesion coefficient and tire cornering characteristics parameters for distributed drive electric vehicle(DDEV), the road adhesion coefficient and tire cornering characteristics parameters estimation are constructed by combining multi-sensor fusion and physical constraint neural network modeling theories. First, the maximum correntropy(MC) criterion is integrated with square-root cubature quadrature Kalman filter(SCQKF) to construct the MC-SCQKF algorithm, where the measured covariance matrix is optimized and quadrature cubature sampling is employed to achieve accurate estimation of PMSM speed and rotor position under non-Gaussian noise, simultaneously enhancing the robustness of the system and the convergence of state estimation. Second, enabling real-time estimation of yaw rate, sideslip angle, longitudinal speed, and road friction coefficient through MC-SCQKF, and they are utilized as features to develop the physics-informed neural network for tire lateral force prediction(TirePINN) model. Finally, the cornering stiffness of front and rear tires is fitted using predicted lateral forces and calculated slip angles by the estimated vehicle state parameters, forming a self-validating closed-loop observation system for tire cornering characteristics. Hardware-in-the-loop tests show fitted errors of 1.49%(front) and 1.37%(rear) for tire cornering stiffness.

The presence of water in the driveway is one of the key factors contributing to vehicle destabilization, and thicker water can even lead to “hydroplaning”. Therefore, real-time understanding of the thickness of accumulated water in the lane is crucial for drivers and autonomous vehicles. Based on the concept of smart tires, this paper proposes a triaxial accelerometer-based method to quantify the water accumulation in the lane. The fluid-solid coupling model between tire-water film-lane is established through ABAQUS, and the three-dimensional acceleration signal at the center of the tire liner is analyzed, and the radial direction acceleration signal is selected as the research object. The signals were acquired and processed using Butterworth low-pass filtering with a cutoff frequency of 450 Hz, aiming at removing high-frequency noise. The signal conversion from the global coordinate system to the local coordinate system is carried out, and the prediction model of lane water thickness based on BP neural network is constructed after obtaining the eigenvalue data matrix. The results of real-vehicle tests show that the triaxial accelerometer-based lane water accumulation quantification method proposed in this paper has high accuracy and good prospects for development and application.

Distributed drive electric vehicles(DDEV), benefiting from their independently actuated wheel-end architecture, demonstrate significant potential in terms of control degrees of freedom and dynamic response flexibility. However, the highly coupled mechanical-electrical-road system inherent in DDEVs poses greater challenges for modeling accuracy, state estimation, and control strategy design. This paper presents a comprehensive review of recent advances in the modeling, state estimation, and control of DDEVs. Regarding modeling, the review focuses on key aspects of mechanical-electrical-road coupled systems, including road surface modeling, unbalanced magnetic force modeling of in-wheel motors, and longitudinal-lateral-vertical coupled dynamics modeling and mechanisms. For the problem of dynamic response estimation in complex systems, the paper summarizes approaches based on model-driven, data-driven, and hybrid model-data-driven methods, with particular attention to the applications of Kalman filtering, Transformer architectures, and physics-informed neural networks. In terms of control, various adaptive control strategies, disturbance rejection methods, and multi-objective optimization techniques under different control frameworks are reviewed. Finally, this paper summarizes and discusses the main challenges and future development trends in current research. It points out that future studies on DDEV should further enhance system behavior prediction under multi-physics coupling effects, multi-source heterogeneous information fusion, and end-to-end control methods based on artificial intelligence and physical prior knowledge.

At present, the dynamic analysis and control of distributed-drive electric vehicles are based on deterministic dynamic models. However, the random perturbation of system parameters, along with uncertainties in state variables and sensor information, causes control systems based on deterministic models to struggle in adapting to complex road conditions. To address this, the influence of random disturbances such as wheel load fluctuations and non-uniform distribution of road surface adhesion coefficients in the wheel-ground interaction process is considered, and a wheel-ground uncertainty model is established. Dynamics simulation experiments are conducted under both hard and soft road conditions using the wheel-ground uncertainty model. In-depth analysis of the experimental results leads to the proposal of a "Tension-Relaxation Theory " between the wheel-ground interaction constraints and wheel-end control constraints. Using parasitic power, traction efficiency, and additional yaw moment as evaluation indices, statistical disturbance analysis is performed in conjunction with the wheel-ground uncertainty model. This further clarifies the inherent relationship between the wheel-ground contact state and control modes, ultimately leading to the proposal of a mode-switching drive control mechanism. Multi-condition simulation results indicate that, compared to a single control mode, the mode-switching drive control mechanism effectively reduces parasitic power and additional yaw moment while maintaining stable and high traction efficiency, thereby verifying the correctness and effectiveness of the proposed theoretical method.

Distributed drive electric vehicles feature independently controllable wheel-end torque, enabling highly maneuverable steering modes such as compass steering and significantly enhancing the vehicle's ability to navigate narrow spaces. To address the inherent trade-off between motion accuracy and tire wear in compass steering scenarios, this paper proposes a control strategy based on non-cooperative game theory that simultaneously considers displacement error and tire wear. The compass steering motion is classified into two representative types, and a three-degree-of-freedom vehicle dynamics model is established to analyze the underlying mechanisms. A hierarchical control architecture is developed, consisting of a strategy optimization layer and an execution control layer, with the sum of squared tire forces on the pivot wheel and the total squared slip velocities of non-pivot wheels defined as the control objectives. The dynamic states of individual wheels under different compass steering types are further examined. A decoupling method for lateral and longitudinal tire forces is realized based on the mapping between wheel speed and tire force, and the corresponding optimization objectives and constraints are formulated. Leveraging a non-cooperative game framework, a Nash equilibrium-based optimization algorithm is designed to compute the optimal front wheel steering angles and wheel speeds under various test scenarios. A PI controller is then applied to ensure accurate wheel speed tracking. Hardware-in-the-loop(HiL) test results show that, compared with the minimum-displacement-error strategy, the proposed method maintains similarly low displacement errors across six test scenarios, while significantly reducing tire wear. The results validate the proposed strategy's high motion accuracy, strong practical applicability, and robust performance.

To investigate the impact of unbalanced electromagnetic forces(UEMFs) resulting from air gap eccentricity in in-wheel motors(IWMs) on vehicle vertical vibration and motor performance, this study proposes a novel calculation method for UEMFs based on a GA-BP neural network, developed in accordance with data-driven modeling theory. Taking into account the coupling between vehicle mechanical vibration and motor vibration, a comprehensive electromechanical coupling model for IWM-driven electric vehicles(EVs) that incorporates both road roughness and UEMFs is established. On this basis, the excitation characteristics and influencing factors of UEMFs is studied, a comparative analysis between the vertical vibration responses of vehicle suspension systems with and without UEMF excitation is conducted, the influence patterns of motor UEMFs on both vehicle dynamic performance and motor performance across various driving conditions is revealed, and correctness of the proposed model and the validity of analytical results are subsequently verified through bench tests. The results show that the UEMFs significantly deteriorates the vehicle ride comfort under acceleration/deceleration conditions, and the larger the acceleration/deceleration, the more obvious the deterioration. However, it has almost no effect on the vehicle dynamics under uniform speed conditions. The research findings establish a solid theoretical foundation for both the analysis and suppression of negative vibration effects of IWM-driven EVs.

Modular vehicles eliminate mechanical constraints between wheels and achieve independent steering of all wheels, greatly enhancing the flexibility of chassis control. However, under curved conditions, each steering wheel is dynamically independent, and the vertical loads of each wheel exhibit time-varying differences, posing challenges for the design of the controller. This paper proposes a linear parameter varying(LPV) modeling method for full wheels independent steering vehicles based on tensor product model(TP model) to address the problems of constraints on scheduling parameter selection, conservatism of system convex hull, and lack of unified tight convex hull construction. The TP model can use higher-order singular value decomposition(HOSVD) to select vertices of the convex hull for the original time-varying system and construct a tight convex hull of the system, resulting in a polytopic LPV(PLPV) model. A robust gain scheduling controller is designed by the parallel distributed compensation(PDC) framework to solve the problem of trajectory tracking caused by different speeds and time-varying vertical loads on each wheel. At the same time, in the derivation of the trajectory tracking model, the reference heading angle of the controller is corrected using the vehicle's side-slip angle, and a robust controller with side slip compensation(RLPV-SC) is derived, which further improves the accuracy of the trajectory tracking. Finally, the effectiveness of the proposed control algorithm is verified through Carsim-Simulink simulation.

The longitudinal-lateral motion control systems for intelligent vehicles equipped with various in-vehicle communication networks are essentially a class of networked control systems(NCSs). With the rapid development of network technology and the increasingly complex vehicle driving environment, the longitudinal-lateral motion control system of intelligent vehicles faces some problems such as strong coupling nonlinearity of the system, network-induced delay, data packet loss, network communication congestion, and malicious attacks, which inevitably lead to a decline in the stability of the system and a deterioration of the control performance. Therefore, how to construct a nonlinear dynamical system model for intelligent vehicles, design network security communication protocols, and develop research on network security control strategies are the keys to the safe and stable control of intelligent vehicles under unreliable networks at present. In view of this, the shortcomings of the existing methods are summarized and the future research directions are prospected by focusing on the four aspects of “longitudinal-lateral dynamics modeling of intelligent vehicles”, “longitudinal-lateral control methods of intelligent vehicles”, “delay and event-triggered control of intelligent vehicles”, and “cyber security control of intelligent vehicles”. The analysis shows that constructing a longitudinal-lateral nonlinear dynamics model of intelligent vehicles with complete nonlinear characteristics by comprehensively considering the inherent nonlinear characteristics of vehicle dynamics and the random network risk is the key foundation for longitudinal-lateral motion system performance analysis and controller design; replacing the traditional longitudinal-lateral decoupling control strategy and developing a longitudinal-lateral coupling controller that comprehensively considers the dynamics coupling impacts, network delay, and communication congestion; and developing a longitudinal-lateral security control strategy under network attacks are the main research directions for future motion control of intelligent vehicles.

A multi-objective hierarchical all-wheel steering control strategy is proposed to enhance maneuverability while reducing tire wear for multi-axle heavy-duty distributed-drive electric vehicles. This strategy comprises three layers: an upper layer, a lower layer, and a coordination layer controller. The upper layer controller preliminarily generates the steering angle for each wheel based on a linear two-degree-of-freedom multi-axle vehicle model that accounts for roll characteristics. The lower layer controller aims to minimize and equalize tire wear by compensating for the steering angles initially calculated by the upper layer. The coordination layer controller employs a Linear Quadratic Optimal controller to cooperatively track the desired vehicle yaw rate and sideslip angle. When the vehicle yaw rate error falls below a preset threshold, tire wear minimization is prioritized; when it exceeds the threshold, vehicle handling stability and safety take precedence. Hardware-in-the-loop tests demonstrate that the proposed control strategy effectively tracks the desired yaw rate and sideslip angle. The vehicle exhibits excellent maneuverability with reduced tire wear under low-speed driving conditions and strong handling stability at high speeds, effectively regulating the sideslip angle within 0.5°±0.3°.

Traditional four-wheel independent steering(4 WIS) control strategies often fail to maintain kinematic constraints under wheel disturbances, compromising vehicle motion coordination. To address this, a cooperative control strategy based on the instantaneous center of rotation(ICR) is proposed. First, vehicle dynamics, steering mechanism, and overall energy consumption models are established. The Fermat point is introduced as the ICR when wheel axes do not intersect, and a virtual linkage mechanism is designed to correlate wheel steering angles under uncertainty, ensuring real-time kinematic constraints. A compound control strategy is developed, where a target control loop drives the wheel angles to converge to the desired values, and an instantaneous control loop ensures tracking of the ICR to maintain kinematic coordination. These loops work collaboratively to improve vehicle motion coordination and control robustness. Finally, using steering angles, tire sideslip angles, vehicle kinetic energy, total steering energy consumption, and tire slip loss as evaluation metrics, the proposed strategy is validated through Matlab/Simulink simulations and experiments on a self-developed 4 WIS electric forklift under different disturbance conditions. Results show that under single-wheel disturbances, compared to position control, the proposed strategy reduces the average maximum tire sideslip angle by 68.38% and slip loss by 22.26%, effectively enhancing vehicle motion coordination under uncertainty.

The four-wheel independent multi-mode steering system integrates four-wheel steering and in-wheel motor differential steering functions, enabling multiple steering modes such as front-wheel, four-wheel, and compound steering. Under varying speeds and road adhesion conditions, the critical stability boundaries associated with each steering mode differ significantly and evolve dynamically. To address this, a hierarchical multi-mode switching stability control method is proposed. The upper layer formulates a quantitative description of stability boundaries under different steering modes, defining the stable domains and switching criteria across various driving conditions. The lower layer models the system as a switched system composed of multiple subsystems and employs a common Lyapunov function to design the switching control strategy, ensuring globally asymptotic stability during mode transitions. The proposed method is validated through co-simulation using Carsim and MATLAB/Simulink. Results demonstrate that the approach effectively suppresses vehicle sideslip angle and yaw rate fluctuations under high-speed and low-adhesion conditions, thereby enhancing vehicle stability in extreme driving scenarios.

With the rapid advancement of new-energy vehicle technologies, distributed-driven architectures have emerged as a paramount enabler of performance breakthroughs, catalyzing intensive research into torque-vectoring control（TVC） and electronic stability control（ESC）. Different from the traditional vehicles, distributed-driven vehicles are demonstrated as an advantageous transportation system with high-precision motion control ability and high reliability stabilization ability. Accordingly, the effectiveness of such advantages requires more efforts to cooperate the TVC and ESC. Motivated by this, this paper respectively formulates the control systems of TVC and ESC based on the multi-agent system theory. Subsequently, with the H_∞ control theory, a design method of a robust cooperative controller for TVC and ESC is proposed, where the driver input, TVC-induced moment and ESC-induced moment are taken into account. Furthermore, based on the stable region analysis, dynamic attenuation factors and weighting functions are introduced to reduce the conservatism of the existing methods, where the TVC and ESC are switched within enable and disable via the judgement of inside or outside of the stable region. Finally, co-simulations are carried out with AVL VSM and Simulink to validate the proposed cooperative strategy under representative maneuvers such as double-lane change, step-steer, and sinusoidal-with-delay. By comparison, under normal conditions, the proposed strategy suppresses superfluous ESC interventions, preserving yaw-rate gain and longitudinal velocity at TVC-dominant levels, whereas in extreme scenarios it recruits ESC to stabilize the lateral motion of vehicles, thereby safeguarding safety.

Corner module architecture electric vehicles demonstrate superior trafficability and stability over conventional vehicles in challenging road conditions. To address the problem of passing over low obstacles that cannot be circumvented and may be encountered by any wheel during driving, this paper designs a coordinated chassis control strategy for stabilizing the vehicle during single wheel obstacle crossing. Firstly, a variable degree-of-freedom dynamics model is established to simulate the three-wheel driving scenario with one wheel lifted. Secondly, stability during three-wheel driving is ensured through centroid transfer. The vertical load on the wheel diagonally opposite to the lifted wheel is significantly reduced, allowing the majority of the vehicle's weight to be supported by the remaining two wheels. Furthermore, during three-wheel driving, changes occur in the vertical loads on each wheel. These variations not only lead to alterations in slip rates, resulting in changes to longitudinal forces, but also cause shifts in cornering stiffness, which in turn affect steering characteristics. Both effects contribute to path deviation. Subsequently, a variable degree-of-freedom controller based on active suspension was designed, along with a traction control system to prevent excessive slip of the driving wheels and a path-following controller to ensure directional stability. These controllers were integrated to form a coordinated chassis control system. Finally, real vehicle tests of the variable degree-of-freedom control and simulation verification under corresponding road conditions demonstrate that the designed active suspension controller reliably achieves variable degree-of-freedom operation on the real vehicle and maintains stability thereafter. The coordinated chassis controller effectively ensures the vehicle safely traverses low obstacles with a height of 200 mm and a width of 300 mm, while maintaining excellent path tracking performance with a maximum deviation of only 64 mm, significantly improving the vehicle's mobility and driving stability.

To improve the switching quality of multi-mode hybrid electric vehicle(HEV), an optimization control strategy for multi-mode switching quality based on torque distribution is proposed. A dynamic model of the HEV multi-mode powertrain system is established, taking into account the influence of clutch friction work, along with evaluation indices for switching quality. The switching conditions of multi-mode driving are analyzed, and target torque profiles for each driving mode and transition process are theoretically calculated. A hierarchical fuzzy control strategy for clutch hydraulic pressure is designed based on the target torque during switching. The upper-level fuzzy algorithm determines the desired clutch pressure, while the lower-level fuzzy PID controller tracks the actual pressure. A dynamic motor torque compensation strategy is developed based on the target torque of each driving mode to compensate for clutch friction work and engine torque lag. A multi-objective fuzzy optimization method is employed to determine the optimal clutch hydraulic pressure to enhance switching quality. Four standard driving cycles are selected for simulation. Results demonstrate that the proposed control strategy ensures both power performance and driving smoothness, effectively suppresses jerk, reduces clutch friction, and improves the overall quality of driving mode switching.

Four-wheel independent steering(4WIS) vehicles possess outstanding maneuverability, enabling more flexible motion control in complex environments. However, existing trajectory planning methods have yet to fully exploit the motion characteristics of 4WIS vehicles. Most approaches approximate the vehicle contour using a fixed number of discs to impose obstacle avoidance constraints, which limits the balance between avoidance accuracy and computational efficiency and reduces adaptability to dynamic environments. To address these issues, this paper proposes a dynamic trajectory optimization method for 4WIS vehicles operating in unstructured and narrow environments. First, the hybrid A^* algorithm is enhanced by incorporating the multi-modal motion characteristics of 4WIS vehicles to improve node expansion and cost evaluation. A dynamic heading adjustment strategy is introduced, along with an adaptive multi-disc collision detection method that adjusts the collision model according to environmental complexity, thereby improving search efficiency. Next, a trajectory optimization model for 4WIS vehicles is constructed. An adaptive multi-disc obstacle avoidance constraint is proposed, and driving corridors are generated based on the collision detection model corresponding to each path point, enabling linearization of obstacle avoidance constraints and improving the solution success rate and computational efficiency in complex environments. Furthermore, to address trajectory planning in dynamic environments, a local trajectory replanning method based on a fuzzy dynamic window approach is proposed, enabling effective avoidance of dynamic obstacles and newly introduced static obstacles. Simulation results demonstrate that the proposed method can generate smooth, collision-free trajectories. Compared with the conventional hybrid A^* algorithm, the proposed method increases the planning success rate by 11.25% and reduces computation time by 27.64 s, significantly enhancing the trajectory planning efficiency and safety of 4WIS vehicles in complex scenarios.

Four-wheel independent steering(4WIS) vehicles have attracted widespread attention due to their superior maneuverability. However, existing trajectory planning methods insufficiently consider the multiple motion modes of 4WIS vehicles, scene complexity, and obstacle attributes, limiting their flexibility in narrow and cluttered spaces and resulting in low planning efficiency or even failure. To address this, this study proposes a 4WIS vehicle trajectory planning framework based on an optimal control problem(OCP). First, a scene complexity binary classification network is developed based on environmental images and vehicle state information to accurately identify complex and simple scenes. Second, a trajectory guidance strategy for complex scenarios is designed, constructing a set of guided points based on a prior A^* path and decomposing the task into local subtasks between guided points to improve planning efficiency. Third, a Hybrid A^* algorithm tailored for 4WIS vehicles is constructed, incorporating a node expansion mechanism that integrates Ackermann steering, diagonal movement, and zero-turn rotation, along with corresponding node cost functions and mode-switching cost functions. Finally, a trajectory optimization OCP framework considering obstacle attributes is established, introducing logical constraints for “compressible obstacles” to limit the speed when passing over them, thereby enhancing vehicle passability while ensuring safety. Simulation results show that, in typical complex environments with dense obstacles, narrow passages, and significant differences in start and goal positions and orientations, the proposed method improves the planning success rate by 50%, increases traversal efficiency by 40.26%, and enhances computational efficiency by 44.89% compared with the Hybrid A^* algorithm, significantly boosting the trajectory planning performance of 4WIS vehicles.

The intelligent chassis corner module system achieves a high degree of integration of by-wire drive/brake, by-wire steering, and active suspension. The corner module eliminates mechanical connections and reduces a large number of mechanical transmission components, supporting independent control of each dynamic unit of the vehicle. This facilitates software-defined and redundant reliability design, making it an ideal carrier for driverless vehicles. However, excessive steering angle control inputs increase the overall control difficulty of the vehicle. Relying solely on traditional steering control methods can easily lead the vehicle into a nonlinear unstable state. To improve the accuracy and stability of vehicle path tracking, a model predictive control(MPC) method based on the instantaneous center of rotation(ICR) is proposed. A corner module vehicle dynamics model and path planning model are established. By converting between Cartesian and polar coordinate systems, a decoupling mapping between vehicle motion and ICR is established. The traditional wheel angle control is transformed into ICR control. Based on model predictive control, coordinated control of lateral and yaw motions is achieved. A wheel angle calculation method with low system conservatism, applicable to the path tracking system of corner module vehicles, is proposed. Combining with closed-loop system feedback, an ICR model predictive control strategy is constructed. The effectiveness and real-time performance of the proposed control strategy are verified through co-simulation and hardware-in-the-loop testing platforms. Simulation results show that the proposed ICR tracking control strategy effectively ensures the accuracy and stability of path tracking, providing significant reference value for the design of steering control systems in corner module vehicles.

Intelligent vehicle path tracking control is crucial for ensuring driving safety and stability. Variable speed and road conditions affect the vehicle's states, and current path tracking methods, which trigger braking based on conservative stability conditions, aim to maintain lateral stability during path tracking. However, these methods tend to degrade path tracking performance under extreme conditions. To address this issue, an elliptical geometric model was applied to represent the dynamic stability region of the vehicle system, and a mapping model between elliptical parameters and driving conditions was established, which allowed for an explicit representation of the dynamic stability region of the vehicle system. Using affine transformations, the constraints for the optimization problem were designed, resulting in a dynamic region of stability integrated path-tracking control method. Verification on a hardware-in-the-loop platform showed that the proposed method reduces vehicle states' fluctuations caused by braking interventions under high-speed, low-friction conditions, while improving path tracking accuracy by more than 25.5%, and meeting real-time requirements.

With the extension of autonomous driving technology to complex scenarios, distributed drive four-wheel steering (4WID-4WIS) vehicles have become a research focus in terms of their control under extreme operating conditions due to their remarkable maneuverability. How to balance path tracking accuracy and driving stability in such scenarios remains a key challenge to be addressed. To enhance the maneuverability of 4WID-4WIS vehicles under extreme operating conditions, a hierarchical controller combining model predictive control(MPC) and proportional-integral(PI) control is proposed. First, a vehicle dynamics model based on the Frenet coordinate system is established, comprehensively considering front axle equivalent steering angle, rear axle equivalent steering angle, and additional yaw moment as control variables. Steering angle constraints based on the phase plane method are incorporated to balance vehicle stability. A hierarchical controller is designed, introducing a deceleration strategy based on centroid sideslip angle prediction to simultaneously ensure path tracking performance and stability. In the lower controller, a torque and steering angle allocation strategy is developed based on the optimal tire utilization principle, considering both tire force limits and motor constraints. This guarantees coordination among wheel steering angles and torque distribution while enhancing vehicle stability margin. Finally, the proposed control method is validated through co-simulations on Matlab/Simulink and CarSim platforms under low-adhesion road conditions. Simulation results demonstrate that the hierarchical control structure effectively improves path tracking accuracy and stability of vehicles with superior dynamic performance. Compared with traditional hierarchical MPC methods, the maximum lateral displacement error is reduced by 29.8%, average error decreases by 46.2%, and yaw rate is effectively controlled, confirming the validity of the proposed approach.

Path tracking is a crucial technology in the field of autonomous driving; however, communication delays between sensors and actuators, as well as steering system response lag, can significantly impair tracking accuracy. To address this issue, an equivalent first-order linear model of the steering delay is constructed based on an in-depth analysis of its impact on path tracking precision. A feedforward-feedback integrated controller is designed, incorporating previewed curvature of the target path and compensation for the steering delay, thereby enabling high-precision path tracking under various speeds and delay conditions. A co-simulation model of the autonomous vehicle integrating steering delay characteristics is developed using Matlab/Simulink and Carsim. Comparative analysis of path tracking performance under different control algorithms demonstrates that the proposed integrated controller achieves significant improvements: at low speed, the performance metric is improved by 97.50%, 82.27%, 60.71%, and 95.56% compared to the traditional LQR controller, preview feedforward controller, delay-compensated feedback controller, and sliding mode controller, respectively; at medium speed, the improvements are 94.38%, 72.31%, 48.12%, and 93.33%; and at high speed, the improvements are 77.51%, 72.13%, 75.06%, and 88.04%, indicating superior path tracking performance.