Tire Slip Ratio Research Articles

An Improved Adaptive Sliding Mode Control Approach for Anti-Slip Regulation of Electric Vehicles Based on Optimal Slip Ratio

To optimize the acceleration performance of independently driven electric vehicles with four in-wheel motors, this paper proposes an anti-slip regulation (ASR) strategy based on dynamic road surface observer for more efficient tracking of the optimal slip ratio and enhanced vehicle acceleration. The method uses the Unscented Kalman Filter (UKF) observer to estimate vehicle speed and calculate the actual slip ratio, while a fuzzy controller based on the Burckhardt tire model identifies road surfaces. The road’s peak adhesion coefficient and optimal slip ratio curve are fitted using a Back Propagation Neural Network (BPNN) optimized by Particle Swarm Optimization (PSO). The control strategy further refines torque management through an adaptive sliding mode control (ASMC) that integrates adaptive laws and a super-twisting sliding mode approach to track the optimal slip ratio. Joint simulations with MATLAB/Simulink and Carsim on low-adhesion, joint, and split road surfaces demonstrate that the strategy quickly and accurately identifies the optimal slip ratio across various road surfaces. This enables the tire slip ratio to approach the optimal value in minimal time, significantly improving vehicle dynamic performance. Compared to conventional sliding mode controllers, the optimized ASMC reduces chattering and improves control precision.

Optimizing driving conditions for distributed drive electric vehicles using minimum unit encoded neural networks

Optimizing the control algorithm for distributed drive electric vehicles (DDEVs) is challenging due to the numerous parameters involved and the variability of optimal parameters across different vehicle operating states. Traditional expert-based methods fall short, and the extensive state space of vehicle operations adds complexity to the optimization process. This paper proposes a novel approach using minimum unit encoded neural networks to optimize vehicle control parameters. By reconstructing the data space of typical operating conditions and implementing Monte Carlo minimum unit encoding within the total operating condition space, this method significantly enhances the optimization efficiency of weight coefficients for multi-motor control systems. Our results show that the proposed algorithm outperforms traditional methods, achieving superior optimization across all operating conditions. Simulation results indicate that the vehicle’s longitudinal velocity error is reduced to less than 1.2%, the sideslip angle remains below 8°, and the tire slip ratio is kept under 0.08%.

Coordination control strategy of motion stability and tire slip for electric vehicle driven by in-wheel-motors

The Direct Yaw Moment Control (DYC) is a widely utilized technique for electric vehicles equipped with In-Wheel Motors (IWMs) to ensure yaw stability. However, most existing DYC studies calculate the additional control yaw moment solely based on the torque differences among the four IWMs, disregarding the nonlinear tire characteristics and wheel rotational movements. When road conditions are poor or torque changes are intense, the tire may slip, preventing it from generating the required longitudinal tire force for the desired direct yaw moment. To address this challenge, this study introduces a coordination control strategy for motion stability and tire slip in electric vehicles (EV) driven by IWMs. To enhance control performance, the nonlinear tire characteristics and wheel rotational movements are integrated into the modeling process when calculating tire forces. Additionally, a state and parameter coupling estimation algorithm is utilized to obtain feedback on tire parameters and vehicle motion states. Using these estimation results as a foundation, a coordination strategy is developed to achieve reference motion state tracking and tire slip ratio control. The Model Predictive Control (MPC) algorithm is employed to solve the control problem with constraints on both control inputs and outputs, and the estimation results are utilized to update the control model as well as provide states feedback. Simulations and experiments are conducted to validate the feasibility and effectiveness of the proposed control strategy. The results demonstrate that our proposed control strategy effectively improves vehicle stability by coordinating vehicle motion and tire slip control.

A spoke strain-based method to estimate tire condition parameters for intelligent tires

Previous research on intelligent tires has shown that strain gauges are suitable for measuring the dynamic characteristics of tires. However, the process of mounting these gauges onto the tire tread and connecting the necessary wiring is relatively challenging. Furthermore, the high-speed rotation of the tire can generate significant heat and noise, which can adversely affect signal acquisition. With the aim of developing an innovative intelligent tire system, we endeavored to install strain gauges onto the spokes. Through analyzing the spokes' deformation characteristics in various driving conditions and directions (radial, longitudinal, and lateral), we determined the strain-sensitive areas and characteristic points on the spokes. Estimation models for tire forces, slip ratio, and slip angle were constructed successfully by considering the relationship between radial force, longitudinal force, lateral force, and strain on the spokes. Our simulation experiments validated these models. Unlike previous models for intelligent tires, the proposed estimation model achieves high accuracy without requiring complex algorithms to estimate tire condition parameters. Furthermore, incorporating strain gauges onto the spokes removes numerous superfluous complexities. The addition of this feature broadens the study scope of intelligent tires and presents novel insights for potential future advancements.

An Adaptive Unscented Kalman Filter for the Estimation of the Vehicle Velocity Components, Slip Angles, and Slip Ratios in Extreme Driving Manoeuvres.

This paper presents a novel unscented Kalman filter (UKF) implementation with adaptive covariance matrices (ACMs), to accurately estimate the longitudinal and lateral components of vehicle velocity, and thus the sideslip angle, tire slip angles, and tire slip ratios, also in extreme driving conditions, including tyre-road friction variations. The adaptation strategies are implemented on both the process noise and measurement noise covariances. The resulting UKF ACM is compared against a well-tuned baseline UKF with fixed covariances. Experimental test results in high tyre-road friction conditions show the good performance of both filters, with only a very marginal benefit of the ACM version. However, the simulated extreme tests in variable and low-friction conditions highlight the superior performance and robustness provided by the adaptation mechanism.

Dynamic Characteristics of Three-Body Braking System Considering Tire-Road Friction

Abstract With the significant increase in vehicle ownership in our country, urban traffic conditions have become increasingly congested. Low-speed driving has become more prevalent, leading to more frequent instances of starting and braking. Consequently, the issue of low-speed braking flutter has become increasingly prominent. While the brake is in an open environment, the dust and particles in the air and the debris generated by the brake itself will have an impact on the braking behavior. In addition, according to the theory of modal coupling, the braking stability of the vehicle is also affected by other components. In this paper, different dynamic torsional models of braking system are established according to different braking conditions. Through numerical calculation, the influence of tire parameters, road conditions, and particles on the friction dynamics characteristics of braking system pairs is explored. The results show that the instability of the brake pair system often occurs at low speed. Different tire slip ratios, tire offset factors, and road conditions will lead to different relative motions of the braking system, but the existence of particles in the brake lining-disk interface can enhance the motion stability of the system.

Anti-slip regulation method for electric vehicles with four in-wheel motors based on the identification of slip ratio

To improve the stability and acceleration of electric vehicles with four in-wheel motors under various road conditions, an anti-slip regulation method considering the identification of slip ratios is proposed. In this paper, the model of the whole vehicle is built, and the road identifier based on the tire model established by Burckhardt is designed. Then, an anti-slip controller that utilizes the variable universe fuzzy proportional integral derivative (PID) algorithm is established to adjust the driving torques of the four in-wheel motors according to the road conditions. The simulation results show that the designed control strategy can quickly and accurately identify the optimal slip ratio under each typical road condition and make the tire slip ratio approach the optimal slip ratio in a short time, so as to effectively improve the driving stability and dynamic performance of the vehicle.

Personalized Velocity and Energy Prediction for Electric Vehicles With Road Features in Consideration

Electric vehicles (EVs) seem to be an eminent alternative for ground transportation. Yet, human drivers may suffer from EV’s range anxiety, which is engendered by EV’s limited battery capacity and sparse charging stations, particularly in rural areas. A reliable prediction of EV battery energy consumption for the intended route before traveling can alleviate this feeling of unease. Fundamentally, an accurate speed trajectory forecast lays the foundation for a dependable energy consumption prediction. This paper originates a data-driven paradigm to predict an EV’s speed considering various road features and individuals’ driving characteristics. Two modified Transformer neural networks, which outperform the traditional recurrent neural networks, are exploited to extract information from input features and predict vehicular acceleration or velocity profile. Additionally, a novel energy consumption model is suggested to pioneeringly take the tire slip ratio into account and utilize a neural-network-predicted powertrain efficiency map. By design, the proposed algorithm can offer EV speed and energy consumption estimations prior to the start of a trip. Experimental data collected in real-world EV test drives on four different routes are employed to validate the method. Compared to a baseline approach, the proposed scheme yields a superior accuracy on both velocity and energy consumption predictions for EVs.

Coordinated Longitudinal and Lateral Stability Improvement for Electric Vehicles Based on a Real-Time NMPC Strategy

Under extreme conditions, such as low friction surfaces, violent steering and urgent acceleration/deceleration, vehicle states change rapidly and are influenced by nonlinear and coupled vehicle dynamics. To improve vehicle stability under extreme conditions, a hierarchical control strategy is proposed for DDEVs. In the upper layer controller, a combined-slip tire model is adopted to improve the model accuracy under extreme conditions. A nonlinear model predictive control based controller is then designed to generate the desired tire slip ratios with the main objectives of tracking the desired yaw rate and suppressing the lateral velocity and tire slip ratios. In the lower layer controller, the disturbance on the driver’s torque requirement, which is disregarded by existing studies, is taken into account. Next, a linear predictive controller is designed to track the desired tire slip ratios by adjusting the motor torques. To improve the computational efficiency of the nonlinear predictive controller, a PMP-based, fast solving algorithm is proposed. The effectiveness of the proposed solving algorithm is checked by comparing the control performance with IPOPT. The proposed control strategy is evaluated by a series of HIL experiments. The HIL results show better performance in overall stability improvement and minimize the disturbance on the driver’s torque requirement.

An integrated analytical model for friction characteristics of aircraft tire on wet runway pavement

The challenging issues of tire-water-pavement interaction are to determine the deformed tire profile and the effective tire-pavement contact length due to water intrusion. This study aims to develop an integrated analytical model to analyze friction characteristics of an aircraft tire on wet runway pavement. The tire-water-pavement interaction model consists of the Brush model for tire-pavement contact and the flexible ring model for tire deformation with consideration of hydrodynamic force. The feasible contact footprint and the deformed tire profile are solved using an interactive process between two models. The model shows the ability to predict friction coefficients at various tire loading, velocity, slip ratio, and water conditions. The thicker water film depth causes the reduction of tire-pavement contact length and the increases of hydrodynamic force, resulting in poor skid resistance of pavement. The calculated friction coefficients at different velocities are further used to predict hydroplaning speed and calculate braking distance of aircraft tire. The results demonstrate the significant effect of water depth on the risk of aircraft hydroplaning and overrun on runway pavement.

Direct tire slip ratio estimation using intelligent tire system and machine learning algorithms

Accurate estimation of the tire slip ratio is critical for vehicle safety, as it is necessary for vehicle control purposes. In this paper, an intelligent tire system is presented to develop a novel slip ratio estimation model using machine learning (ML) algorithms. The accelerations, generated by a triaxial accelerometer installed onto the inner liner of the tire, are varied when the tire rotates to update the contact patch. Meanwhile, the reference value of slip ratio can be measured by the MTS Flat-Trac tire test platform. Then, by analyzing the variation between the accelerations and slip ratio, highly useful features are discovered, which are especially promising for assessing vertical acceleration. For these features, ML algorithms are trained to build the slip ratio estimation model, in which the ML algorithms include artificial neural networks (ANNs), gradient boosting machines (GBMs), random forests (RFs), and support vector machines (SVMs). Finally, the estimated NRMS errors are evaluated using 10-fold cross-validation (CV). The proposed estimation model is able to estimate the slip ratio continuously and stably using only the acceleration from the intelligent tire system, and the estimated slip ratio range can reach 30%. The estimation results have high robustness to vehicle velocity and load, where the best NRMS errors can reach 4.88%. In summary, the present study with the fusion of an intelligent tire system and machine learning paves the way for the accurate estimation of the tire slip ratio under different driving conditions, which creates new opportunities for autonomous vehicles, intelligent tires, and tire slip ratio estimation.

Look-Up Table-Based Tire-Road Friction Coefficient Estimation of Each Driving Wheel

The tire-road friction coefficient conveys critical information for advanced vehicular active safety systems to significantly enhance driving safety and maneuverability. In this letter, a look-up table based method is proposed to estimate the tire-road friction coefficient of each driving wheel in real time. The table that represents the relations among the normalized longitudinal tire force, tire slip ratio and slip angle, and the tire-road friction coefficient is constructed off-line by collecting data from common onboard sensors under different driving scenarios. By applying the perspective projection procedure, only data from two different road surfaces are required, which facilitates table construction. Then the table is used for estimating the tire-road friction coefficient in real time and the results are post-processed by the Kalman filter to render smooth and reliable estimates. Simulations in the CarSim-Simulink environment are conducted to verify the satisfactory performance of the proposed estimation method for various driving scenarios and sudden changes of road conditions.

Robustness Analysis for Adaptive Model Predictive and Sliding Mode Controllers Regulating Longitudinal Tire Slip Ratio

This study aims to evaluate the robustness of Adaptive Model Prediction Controllers (AMPC) and First Order Sliding Mode Controllers (FOSMC) facing modelling uncertainties or external disturbances when used to track longitudinal tire slip ratio. Changing road conditions create uncertainties in the friction coefficient of the tire-road interface, and the uncertainty of mass is caused by over/under loads on the nominal mass of the vehicle. the external perturbations are generally linked to the angle of inclination of the road. The efficiency of the controllers, in terms of ensuring the minimum braking time in the face of uncertainties and perturbations, is evaluated by numerical simulation and detailed comparison.

Robust Control with Uncertain Disturbances for Vehicle Drift Motions

Professor drivers, including racing drivers, can drive cars to achieve drift motions by taking control of the steering angle in high tire slip ratios, which provides a way to improve the driving safety of autonomous vehicles. The existing studies can be divided into two kinds based on analysis methods, and the theory-based is chosen in this study. Because the recent theory based is most applied for planar models with neglect of the rollover accident risk, the nonlinear vehicle model is established by considering longitudinal, lateral, roll, and yaw motions and rolling safety with the nonlinear tire model UniTire. The drift motion mechanism is analyzed in steady and transient states to obtain drift motion conditions, including the velocity limitation and the relationship between sideslip angle and yaw rate, and vehicle main status parameters including the velocity, side-slip angle and yaw rate in drift conditions. The state-feedback controller is designed based on robust theory and LMI (linear matrix inequation) with uncertain disturbances to realize circle motions in drift conditions. The designed controller in simulations realizes drift circle motions aiming at analyzed status target values by matching the front-wheel steering angle with saturated tire forces, which satisfies the Lyapunov stability with robustness. Robust control in drift conditions solves the problem of how to control vehicles to perform drift motions with uncertain disturbances and improves the driving safety of autonomous vehicles.

Coordinated longitudinal and lateral vehicle stability control based on the combined-slip tire model in the MPC framework

In extreme driving situations, the coupling between the longitudinal and lateral vehicle motion becomes significant with the highly nonlinear tire forces and influences vehicle overall stability. To address the above problem, a model predictive controller is proposed for four wheels independent motor-drive electric vehicles. Firstly, a LuGre combined-slip tire model is developed to describe the coupling nonlinearity of the tire. The variation of longitudinal velocity is considered a disturbance term in the vehicle dynamics model to consider its effect on vehicle stability. Then, the multiple objectives, including tracking the reference values of the yaw rate and lateral velocity, suppressing the tire slip ratios, and reducing the torque energy consumptions, are balanced by the additional torque generated by the designed model-based controller. The safety and actuator constraints are considered concurrently. Finally, the proposed controller is tested via co-simulation with CarSim and MATLAB/Simulink, and a hardware-in-loop system with extreme maneuvers. The results show that the handling performance, longitudinal and lateral stability are effectively improved under extreme conditions.

Modified Dynamic Model for Longitudinal Motion of Ground Vehicles

The present study aims to provide a modified model for analysing the longitudinal dynamics of ground vehicles. Bearing in mind that an inevitable tire slip occurs under the transmission of driving torque to the drive wheels, the pure rolling assumption employed in many previous works is modified in this research. This paves the way through the development of a more realistic simulation framework with promising performance when used in vehicle and powertrain related topics. The modified equation of motion is an explicit function of tire slip ratio, and as a result, by rewriting the power balance equation, a dissipation term due to tire slip appears, which is consistent with the outcome of the recent contributions. Simulation results indicate a significant difference between the modified and simplified models in the case of a relatively high tractive force. Moreover, tire slip loss is obviously large in such a case, so that its neglect would lead to a noticeable inaccuracy in the response of the traditional model. An average of 35% improvement in the accuracy of prediction of tire consumed energy is observed in 0 to 100 km/h half-throttle acceleration manoeuvre using the modified model.

Tire–road friction coefficient estimation based on designed braking pressure pulse

Knowledge of tire–road friction coefficient (TRFC) is valuable for autonomous vehicle control and design of active safety systems. This paper investigates TRFC estimation on the basis of longitudinal vehicle dynamics. A two-stage TRFC estimation scheme is proposed that limits the disturbances to the vehicle motion. A sequence of braking pressure pulses is designed in the first stage to identify desired minimal pulse pressure for reliable estimation of TRFC with minimal interference with the vehicle motion. This stage also provides a qualitative estimate of TRFC. In the second stage, tire normal force and slip ratio are directly calculated from the measured signals, a modified force observer based on the wheel rotational dynamics is developed for estimating the tire braking force. A constrained unscented Kalman filter (CUKF) algorithm is subsequently proposed to identify the TRFC for achieving rapid convergence and enhanced estimation accuracy. The effectiveness of the proposed methodology is evaluated through CarSim™-MATLAB/Simulink™ co-simulations considering vehicle motions on high-, medium-, and low-friction roads at different speeds. The results suggest that the proposed two-stage methodology can yield an accurate estimation of the road friction with a relatively lower effect on the vehicle speed.

Hybrid Control-Based Acceleration Slip Regulation for Four-Wheel-Independent-Actuated Electric Vehicles

In this article, an enabling hybrid control-based acceleration slip regulation (ASR) method is proposed for four-wheel-independent-actuated electric vehicles by combining the advantages of the maximum-torque-based and slip-ratio-based ASR methods. Considering the dramatic fluctuation of tire slip ratio at low speeds caused by the poor signal-to-noise ratio (SNR) for the vehicle speed, an adaptive maximum torque search method is employed to ensure the acceleration regulation performance at low speeds. With the increasing vehicle speed, the SNR would have diminishing influence so that the tire slip ratio gradually approaches its true value. Under such scenarios, a robust sliding mode control method is proposed to regulate the real-time slip ratio to its optimal value, so as to maximize the tire-road adhesive force. In order to coordinate the driving torques and guarantee a smooth transition process in between, a finite-state machine-based control scheme is further developed. Hardware-in-Loop test results show that the proposed hybrid control-based ASR scheme exhibits good performance and high reliability under various driving conditions.

A Robust Traction Control Platform for AWD Vehicles With AWD TMU and eLSD

This paper addresses the traction control of All-Wheel-Drive (AWD) vehicle equipped with AWD Torque Management Unit (TMU), Traction Control System (TCS) and electronic Limited Slip Differential (eLSD). In the existing schemes, they are separately designed, ineffectiveness of control coupling exists, and the integration of them is time consuming and hard to achieve at a good driving performance such as good acceleration and high driving energy efficiency. To avoid such problems, a novel, effective and robust traction control platform is proposed. A decoupling method is adopted, which is achieved by presetting slip ratio thresholds and axle differential speed difference. State machines are designed according to preset high and low thresholds to select among integral increasing control, integral decreasing control, or keeping control. It has no vehicle parameters, no variants hard to measure, and no road identification. The design only counts tire slip ratios, wheel speeds and vehicle speed. So, it's one-platform-fits-all, including the vehicle designs, the road types, and so on. This design has been shown by results of high-fidelity simulations that the proposed design improves energy-economy, driving, handling and stability. Also, the design is shown to be effective, stable and robust.

Vehicle Stability Control: Model Predictive Approach and Combined-Slip Effect

This article presents a model predictive vehicle stability controller that considers tire force nonlinearities and the combined-slip effect. Loss of cornering forces caused by increased longitudinal slip and changes in the slip angle due to the vehicle lateral response are considered in the prediction model of the controller. The developed controller adjusts tire slip ratios based on forces of the front and rear axles, monitors tire capacities, and normal forces, and exhibits excellent performance in maintaining lateral responses within a stable region. The performance of the proposed predictive controller is assessed in software simulations as well as road experiments in various pure-slip and combined-slip driving scenarios and under different road friction conditions.