Search Results (43)

During cornering braking, longitudinal braking and lateral tire forces share the limited tire–road adhesion capacity and induce dynamic load transfer among the four wheels, thereby intensifying the coupling between braking performance and lateral stability. Improper braking-force distribution may consequently cause premature tire-force saturation, excessive yaw response, and vehicle sideslip. To address this problem, this study proposes a coordinated cornering-braking stability control strategy for brake-by-wire vehicles. The total braking force is first determined according to the desired deceleration and initially distributed among the four wheels based on their estimated dynamic vertical loads. An improved sliding mode controller with adaptive yaw–sideslip weighting then generates an additional yaw moment, while a sequential quadratic programming algorithm redistributes the wheel braking forces, subject to the total braking-force, additional yaw-moment, and tire-friction-circle constraints. The proposed strategy was evaluated using CarSim/Simulink co-simulations under eight operating conditions involving different initial speeds, braking demands, and road-adhesion levels. Compared with the proportional distribution strategy, the optimized strategy reduced the yaw rate root-mean-square error by 76.2–98.6%, the maximum sideslip angle tracking error by 8.1–97.4%, and the maximum magnitude of the actual sideslip angle by 20.9–98.6%. The improvements were particularly pronounced under high-speed and high-braking-demand conditions. Although the stability-priority allocation moderately extended the braking duration in some cases, it effectively suppressed excessive yaw and sideslip responses, demonstrating its simulation-level effectiveness in improving the cornering-braking stability of brake-by-wire vehicles. Full article

This study investigates a simulation-based estimation–control chain that integrates an interactive multiple model adaptive unscented Kalman filter (IMM-AUKF) with a multiple-input multiple-output model predictive controller (MIMO-MPC). A seven-degree-of-freedom vehicle model and a Pacejka tire model are used to represent nonlinear vehicle dynamics. The controller updates model and regression region steering constraints from the estimated adhesion state and jointly allocates front/rear steering and longitudinal force commands. The revised experiments use a 0.5 ms plant integration step and a consistent 20 ms estimator/controller update. Under high-adhesion double lane change (DLC), the proposed chain lowers speed RMSE from 0.7950 to 0.1833 m/s and mean adhesion estimation RMSE from 0.1567 to 0.0812. Under variable-adhesion single lane change (SLC), lateral RMSE decreases from 0.1816 to 0.1649 m, speed RMSE from 0.7983 to 0.2505 m/s, and mean adhesion estimation RMSE from 0.1622 to 0.0949. Heading error is not uniformly improved and is reported as a design trade-off. These results provide reproducible simulation evidence, while hardware-in-the-loop and real-vehicle validation remain future work. Full article

Distributed-drive electric vehicles (DDEVs) enable independent wheel-torque control, providing flexibility to improve energy efficiency and vehicle stability. However, tire–road adhesion, motor and battery capabilities, and actuator availability constrain these objectives, which may conflict under low-adhesion conditions, high-power acceleration, emergency braking, and combined longitudinal–lateral maneuvers. This paper provides a structured review of coordinated torque-allocation strategies for balancing energy efficiency and stability in DDEVs. Existing research is examined in terms of regenerative braking, tire-slip energy-loss reduction, and stability control under longitudinal, yaw, and combined conditions. Control approaches are classified as rule-based, stability-region-based, mode-switching, multi-objective optimization and predictive control, state-adaptive dynamic-priority coordination, and learning-based safety-hybrid methods. These approaches differ in real-time performance, constraint handling, adaptability, interpretability, and engineering maturity. A hierarchical hybrid architecture integrating rule-based supervision, state assessment, constraint-aware optimization, and learning-based enhancement appears more suitable for practical deployment than a single algorithm or fixed-weighting scheme. Key challenges include dynamic stability-boundary estimation, safety-assured coordination, multi-actuator fault tolerance, real-time implementation, and standardized vehicle-level validation. This review provides guidance for coordinated control-system development and future research on DDEVs. Full article

To address inaccurate longitudinal speed acquisition, difficult road adhesion identification, and insufficient reliability of state inputs for traction control in dual-motor electric vehicles under low-adhesion, adhesion-transition, and drive-slip conditions, this paper proposes a state estimation method oriented to traction control. Four-wheel speeds, inertial measurement unit (IMU) signals, and vehicle dynamics are fused to establish a layered longitudinal speed estimation structure, including slip-confidence evaluation, inertial correction, kinematic and dynamic fusion, and multi-mode weight decision. Standard road adhesion curves, fuzzy inference, and recursive correction are further combined to estimate the peak adhesion coefficient and the optimal slip ratio online. CarSim/Simulink co-simulation results show that the root mean square errors of the proposed speed estimation method are 0.1226, 0.1728, 0.1070, and 0.0322 m/s under comprehensive driving, acceleration slip, emergency braking, and high-speed steering conditions, respectively. Under an adhesion-transition condition, the peak adhesion coefficient and optimal slip ratio can be updated rapidly with road changes. Application results suggest that the estimated states can provide useful inputs for front–rear axle traction coordination under the investigated low-adhesion conditions. Full article

The road surface adhesion coefficient is a key parameter affecting the dynamic control and driving safety of vehicles. Addressing the limitations of vehicle dynamics-based estimation methods under low-excitation conditions, this paper proposes a vision-perception-based CNN–Mamba hybrid regression network (CMHR-Net) for visual road friction potential estimation. By extracting road surface texture features from camera images, the proposed method predicts representative friction levels associated with different road conditions, providing prior information for vehicle active safety and control systems. First, ResNet18 is used to extract local texture features from road surface images. Second, a Mamba State Space Model is introduced to model long-distance dependencies between features, thereby enhancing global representation capabilities. Finally, the predicted adhesion coefficient value is output through a regression layer. Simultaneously, an adhesion coefficient mapping dataset based on road surface semantic attributes is constructed for model training and validation. Experimental results show that under various road surface conditions, including wet asphalt, waterlogged asphalt, waterlogged concrete, ice and snow, and joints, the proposed method significantly reduces the MAE (Mean Absolute Error) compared to the traditional CNN model. Specifically, under low adhesion conditions (μ ≈ 0.20), the error is reduced by approximately 56.9%. Furthermore, in complex variable conditions, the model significantly outperforms the traditional CNN model in both maximum error (Max Error) and mean absolute percentage error (MAPE). For example, under low adhesion conditions (μ ≈ 0.20), the MAPE decreases from 21.59% to 9.28%, and the maximum error decreases from 0.2025 to 0.0628, demonstrating superior stability and robustness. This method can achieve high-precision feedforward estimation of the adhesion coefficient without relying on vehicle dynamics excitation, providing effective support for feedforward control and active safety systems in vehicles. Full article

Reliable estimation of vehicle states, tire cornering stiffness, and the tire-road adhesion coefficient are essential for vehicle lateral stability and intelligent chassis control. Under low adhesion, nonlinear tire operation, and weak excitation, lateral-force residuals are jointly affected by cornering-stiffness variation, adhesion-coefficient variation, and tire-force saturation, which may cause erroneous parameter adaptation. This paper proposes a reliability- and sensitivity-guided co-estimation method for vehicle states, tire cornering stiffness, and the tire-road adhesion coefficient. A hierarchical framework is developed based on a planar 3-DOF vehicle model and a Fiala-type nonlinear tire model. Front- and rear-axle lateral-force pseudo-measurements are reconstructed from lateral acceleration and yaw angular acceleration, without requiring additional tire-force sensors. Parameter-update reliability is evaluated by considering lateral excitation, longitudinal slip, adhesion utilization, and normalized lateral-force residual consistency. Normalized lateral-force sensitivities are then used to allocate the residual between the cornering-stiffness and adhesion-coefficient update channels. CarSim/Simulink co-simulations under high-, intermediate-, and low-adhesion double-lane-change maneuvers demonstrate that the proposed method improves sideslip-angle and lateral-velocity estimation accuracy, suppresses erroneous cornering-stiffness adaptation, and provides more stable estimates of the tire-road adhesion coefficient. Full article

Speed information is crucial for controlling distributed electric-drive loaders, especially for driving and operation. Due to complex working conditions, the wheels of the loader often experience different conditions, leading to inaccurate speed estimation. To solve this, this paper proposes a multi-sensor fusion speed estimation method based on a Finite State Machine (FSM). The method uses the FSM to identify the wheel states and adaptively switches between the weighted average method and integration method to estimate the vehicle’s speed accurately. When all wheels are slipping, the acceleration integration method is used, starting from the latest trustworthy speed estimate. When the wheels are not slipping, the speed is estimated using the weighted average of the trustworthy wheels. Additionally, the method addresses the relative motion between the front and rear vehicle bodies caused by articulated steering by using an articulated steering projection method to ensure accurate wheel state estimation from IMU signals. Simulation and hardware-in-the-loop experiments show that the proposed method can accurately estimate vehicle speed under various road conditions. Specifically, under low-adhesion road conditions with all four wheels in a slipping state, it improves speed estimation accuracy by over 75% compared to traditional methods such as simple averaging, selective averaging, and pure integration. Full article

To address the uncertainty of the measurement noise covariance matrix in vehicle state estimation, this paper proposes a symmetry-aware extended Kalman filter optimized by a chaotic-gradient strategy. The symmetry-aware concept is introduced from the approximate mirror symmetry of vehicle lateral dynamics under left and right steering excitations. Under identical road adhesion and vehicle operating conditions, the yaw-rate and sideslip-angle responses should exhibit balanced statistical characteristics for positive and negative lateral motions. However, a fixed measurement noise covariance matrix may break this balance and lead to direction-dependent estimation bias or delayed convergence. To improve the statistical consistency of the estimation process, the proposed method adaptively tunes the measurement noise covariance matrix according to the innovation covariance mismatch. A chaotic search mechanism is first used to enhance global exploration, and a variable-step gradient method is then applied to refine the local optimal solution. Through the iterative combination of chaotic traversal and gradient-based refinement, the proposed observer improves the balance between model prediction and measurement correction under stochastic disturbances. The effectiveness of the proposed method is verified through CarSim and MATLAB/Simulink co-simulation. The results show that, compared with EKF, UKF, and AEKF benchmark observers, the proposed CG_EKF provides more accurate estimation of vehicle yaw rate and sideslip angle. Full article

This paper proposes an enhanced adaptive unscented Kalman filter (SH-AUKF) method based on the Sage–Husa algorithm to address the issue of insufficient estimation accuracy for state parameters and road adhesion coefficients in distributed drive mining dump trucks under complex mining conditions. By integrating a seven-degree-of-freedom vehicle dynamics model with the Dugoff tire model, a collaborative observer is constructed for estimating state parameters and the four-wheel road adhesion coefficient. Through joint simulation verification using Trucksim–Matlab 2025b, it was demonstrated that under sinusoidal steering, step steering, and varying road adhesion coefficients (0.3~0.7), the root mean square error (RMSE) of longitudinal vehicle speed, slip angle, and yaw rate estimation using SH-AUKF was significantly reduced compared to the traditional UKF. Additionally, the estimation error of the four-wheel road adhesion coefficient was decreased by 8~26%. This has significant application value for improving the automation level of mining transportation. Full article

Distributed drive vehicles provide enhanced actuation flexibility, making longitudinal–lateral coordinated stability control essential for improving vehicle handling and safety under complex driving conditions. Nevertheless, the existing coordinated control strategies commonly employ stability reference models with fixed tire–road friction coefficients, which restrict their adaptability to time-varying adhesion environments. In addition, conventional sliding mode-based lateral stability controllers may exhibit limited performance when confronted with strong nonlinear coupling and external disturbances. To address these issues, this paper proposes an integrated longitudinal–lateral coordinated stability control framework for distributed drive vehicles. A dual unscented Kalman filter-based estimator is developed to identify the tire–road friction coefficients and construct a friction-adaptive reference model for yaw rate and sideslip angle. An adaptive fractional power speed controller with resistance compensation is designed to generate the total longitudinal driving torque, while an adaptive neural sliding mode controller produces the corrective yaw moment for lateral stability enhancement. Furthermore, a pseudoinverse-based torque distribution strategy is employed to allocate the longitudinal torque and yaw moment to individual wheels. Simulation results demonstrate that the proposed framework significantly improves vehicle stability and tracking accuracy compared with conventional control methods under varying road conditions. Full article

Off-road vehicle control designs often neglect the complex tire–soil interactions inherent to soft terrain. This paper proposes a Variable Impedance Control (VIC) strategy integrated with a high-fidelity terramechanics model. First, a real-time sinkage estimation algorithm is derived using experimentally identified Bekker parameters and the quasi-rigid wheel assumption to capture the nonlinear feedback between soil deformation and vehicle dynamics. Building on this, the VIC strategy adaptively regulates virtual stiffness, damping, and inertia parameters based on real-time suspension states. Comparative simulations on an ISO Class-C soft soil profile demonstrate that this framework effectively balances ride comfort and safety constraints. Specifically, the VIC strategy reduces the root-mean-square of vertical body acceleration by 46.9% compared to the passive baseline, significantly outperforming the Linear Quadratic Regulator (LQR). Furthermore, it achieves a 48.6% reduction in average power relative to LQR while maintaining suspension deflection strictly within the safe range. Moreover, unlike LQR, the VIC strategy improves tire deflection performance, ensuring superior ground adhesion. These results validate the method’s robustness and energy efficiency for off-road applications. Full article

The vehicle sideslip angle is a core state parameter in vehicle dynamics control. Its accurate estimation is critical for vehicle stability control and the development of active safety systems. In the vehicle sideslip angle estimation method using the traditional Unscented Kalman Filter (UKF), the process noise covariance matrix Q and observation noise covariance matrix R are difficult to adjust adaptively, leading to estimation accuracy degradation under complex driving conditions. This paper proposes a vehicle sideslip angle estimation method that integrates UKF and Deep Reinforcement Learning (DRL), leveraging the adaptive decision-making capability of DRL to dynamically optimize the noise parameters in UKF. A state space incorporating vehicle motion states and filtering performance metrics is constructed, along with an action space that outputs adjustment quantities for the noise covariance matrices. A reward function based on estimation errors and uncertainties is formulated, and the Proximal Policy Optimization (PPO) algorithm is employed to train the policy network. The results indicate that the proposed method effectively improves vehicle sideslip angle estimation accuracy under various driving conditions, including different vehicle speeds, road surface adhesion coefficients, and sensor noise disturbances. Compared with the traditional UKF method, the Root Mean Square Error (RMSE) is reduced by over 30%, and the method demonstrates strong stability and robustness under complex scenarios. This approach provides a new solution for the accurate estimation of key vehicle state parameters and can be extended to fields such as autonomous driving and vehicle active safety. Full article

To improve recognition accuracy, convergence speed, and numerical stability in estimating the road adhesion coefficient for distributed-drive electric vehicles, a nonlinear seven-degree-of-freedom vehicle dynamics model was developed based on a modified Dugoff tire model. Using the Unscented Kalman Filter (UKF) as a foundation, a Square-Root Unscented Kalman Filter (SR-UKF) algorithm was derived through covariance-square-root processing and Singular Value Decomposition (SVD). A co-simulation platform was built with CarSim and Simulink, and a vehicle speed-following model was developed for simulation analysis. The results show that the SR-UKF algorithm for road identification consistently maintains matrix positive definiteness, ensures numerical stability, speeds up convergence, and fully utilizes measurement information. Simulations under various road conditions (high-adhesion, low-adhesion, split-μ, and opposite-μ) and driving scenarios demonstrate that, compared to the traditional UKF, the SR-UKF converges faster and provides higher estimation accuracy, enabling real-time, accurate estimation of the road adhesion coefficient across multiple scenarios. Final results confirm that the SR-UKF exhibits excellent estimation accuracy and robustness on low-adhesion surfaces, confirming its superiority under high-risk conditions. This offers a dependable basis for improving vehicle active safety. Full article

Tire–road friction is a fundamental factor in vehicle safety, energy efficiency, and environmental sustainability. This narrative review synthesizes current knowledge on the tire–road friction coefficient (TRFC), emphasizing its dynamic nature and the interplay of factors such as tire composition, tread design, road surface texture, temperature, load, and inflation pressure. Friction mechanisms, adhesion, and hysteresis are analyzed alongside their dependence on environmental and operational conditions. The study highlights the challenges posed by emerging mobility paradigms, including electric and autonomous vehicles, which demand specialized tires to manage higher loads, torque, and dynamic behaviors. The review identifies persistent research gaps, such as real-time TRFC estimation methods and the modeling of combined environmental effects. It explores tire–road interaction models and finite element approaches, while proposing future directions integrating artificial intelligence and machine learning for enhanced accuracy. The implications of the Euro 7 regulations, which limit tire wear particle emissions, are discussed, highlighting the need for sustainable tire materials and green manufacturing processes. By linking bibliometric trends, experimental findings, and technological innovations, this review underscores the importance of balancing grip, durability, and rolling resistance to meet safety, efficiency, and environmental goals. It concludes that optimizing friction coefficients is essential for advancing intelligent, sustainable, and regulation-compliant mobility systems, paving the way for safer and greener transportation solutions. Full article

To address the issue of poor yaw stability in distributed-drive electric pickup trucks at medium-to-high speeds, particularly under the influence of continuously varying tire forces and road adhesion coefficients, a novel Kalman filter-based method for estimating the road adhesion coefficient, combined with a Tube-based Model Predictive Control (Tube-MPC) algorithm, is proposed. This integrated approach enables real-time estimation of the dynamically changing road adhesion coefficient while simultaneously ensuring vehicle yaw stability is maintained under rapid response requirements. The developed hierarchical yaw stability control architecture for distributed-drive electric pickup trucks employs a square root cubature Kalman filter (SRCKF) in its upper layer for accurate road adhesion coefficient estimation; this estimated coefficient is subsequently fed into the intermediate layer’s corrective yaw moment solver where Tube-based Model Predictive Control (Tube-MPC) tracks desired sideslip angle and yaw rate trajectories to derive the stability-critical corrective yaw moment, while the lower layer utilizes a quadratic programming (QP) algorithm for precise four-wheel torque distribution. The proposed control strategy was verified through co-simulation using Simulink and Carsim, with results demonstrating that, compared to conventional MPC and PID algorithms, it significantly improves both the driving stability and control responsiveness of distributed-drive electric pickup trucks under medium- to high-speed conditions. Full article