Vehicle Traction Control Research Articles

Traction Control for Electric Vehicles with Dual-Mode Coupling Drive System on Split Ramps

Traction Control for Electric Vehicles with Dual-Mode Coupling Drive System on Split Ramps

Low energy consumption traction control for centralized and distributed dual-mode coupling drive electric vehicle on split ramps

Distributed drive electric vehicle has good pass ability on split ramps, but it has high power demand for driving motors, resulting in low driving efficiency and limited economy. To address the aforementioned deficiencies, a centralized and distributed dual-mode coupling drive system (DMC) capable of drive force transfer between coaxial wheels is designed to avoid single-side wheel high-intensity driving and reduce vehicle energy consumption. To guarantee the longitudinal dynamics performance, a traction control based on online sliding mode extremum search (SES) was proposed, and the traction control based on the ρ_SES algorithm as a means to suppress the controller damage caused by the steady-state oscillation has also been adopted. The simulation and experiment results show that the dual-mode coupling drive electric vehicle significantly reduces the energy consumption and reduces the power demands of the drive motor than those of the distributed drive electric vehicle. Apparently, the ρ_SES algorithm based traction control suppresses the steady-state oscillations of the SES, which can protect the motor controller. This study lays the theoretical foundation for the use of DMC and achieving superior traction control for electric vehicles.

Pavement Friction Evaluation Based on Vehicle Dynamics and Vision Data Using a Multi-Feature Fusion Network

The tire–road friction coefficient is a critical evaluation index of the service performance of roads: it governs the stopping distance, traction control, and stability of vehicles. Moreover, friction information is also needed in many function units of modern vehicles. This paper proposes a novel data-driven approach for inference of the maximum tire–road friction coefficient using a combination of vehicle dynamics signal and machine vision data. The approach is aimed at robust road condition perception that can provide frequent measurements over large areas across all weather conditions. Two different neural network architectures were adopted to extract in-depth features behind vehicle dynamics signals and road surface images. Features from these two types of data were then fused in two different levels, namely feature level and decision level, forming a multi-feature fusion neural network. The proposed network performs better than models based only on dynamic signals or vision data. The method proposed was applied to real data obtained from an electric car in a highway driving scenario. For classification of the maximum tire–road friction coefficient, the proposed network can yield F1-score increments of 0.09 and 0.18 from dynamics-based and vision-based sub-models, respectively. For the maximum tire–road friction coefficient value regression, the proposed model also achieves the highest R-square score of 0.71. Of these two types of data collected under highway driving scenarios, the vision data contribute more to the overall performance of the proposed model. Nevertheless, the dynamics data possess excellent potential in poor lighting conditions. With these fused features, the proposed multi-feature fusion network can not only improve the accuracy of maximum tire–road friction coefficient estimation but also is deemed workable for a broader range of scenarios.

Centralized and distributed coupling traction control of electric vehicles on split ramps

The pass ability on split ramps is an important dynamics performance of electric vehicles (EVs). However, the current traction control system (TCS) requires the vehicle to have a high-intensity driving capability and sometime the braking assistance control is required, which is a waste of cost and energy. To improve the traction control ability and reduce the power demands, a centralized and distributed coupling TCS based on the designed dual-mode coupling drive system is proposed. The coupling traction control mechanism is analyzed, the TCS based on S-line control and optimal slope-seeking algorithm is designed, and the traction control effect is verified by the co-simulation and real vehicle road test. The research results show that adopting coupling traction control can give full play to system power to improve the pass ability, the optimal slip rate control can be realized and the control difficulty also be reduced. It laid a theoretical basis for the pass ability improvement of EVs on split ramps and the new application of dual-mode coupling drive system.

Traction Control and Total Driving Force Distribution Strategy for Electric Vehicles with Four in Wheel Motors based on PD-Fuzzy Control

In this paper, a torque distribution control for electric vehicles with an in-wheel motor installed for each wheel is proposed in order to manage the traction operation of the vehicle and avoid slip ratio from reach extreme values while the vehicle moves into slippery surfaces. The driving force control is the main controller in the proposed control system and its consist from two control loop. PD-Fuzzy logic and PI-control are applied as a wheel speed controller in the inner control loop with the aim of improving stability and support the traction operation of the wheels. To retain the total requested driving force, force distribution strategies are obtained when the vehicle moves on the road with a low friction coefficient surface. Also, it works as a yaw moment suppressor when the vehicle runs into split slippery surfaces which improves vehicle performance. The proposed vehicle dynamics and torque distribution control system are simulated using Simulink-MATLAB Program. The results of the simulation demonstrated the effectiveness of the proposed method.

Driving behavior oriented torque demand regulation for electric vehicles with single pedal driving

Driving behaviors, induced by psychological activities and environment stimulation, impose the dominant impact on vehicle driving performance. To exhaustively improve the performance of electric vehicles (EVs), information unscrambled from various driving behaviors is recommended to be incorporated into the controlling process. In this context, a novel method is presented to regulate the torque demand of EVs with single pedal driving (SPD) by efficiently interpreting intention from different driving behaviors for eco driving. Specifically, a brand-new driving behavior identifier (DBI) is constructed by integrally employing the binary dragonfly algorithm (BDA) and adaptive neuro-fuzzy inference system with particle swarm optimization (ANFIS-PSO). Simultaneously, the whale optimization algorithm (WOA) generates the torque demand look-up tables (TDLTs) offline under different driving behaviors for SPD by referring to the constraints from drivability and energy efficiency. In the instant implementation, the driving behaviors are identified instantaneously by the DBI, and the homologous TDLTs are assigned to vehicle controller, thereby attaining efficient control of vehicle powertrain. A case study about the vehicle traction control is performed to validate the prospective optimal performance of the proposed method and further evaluate the impact on vehicle performance from driving behaviors.

Traction control and diagnostics of electric and unmanned vehicles on roller stands

The authors of the article analyze the possibilities of correct diagnostics of unmanned and electric vehicles on modern power and inertia roller stands. The purpose of this work is to improve the quality of monitoring the technical condition of wheeled vehicles by providing highly informative and stable test modes of operation in the process of their diagnostics. To achieve this target, we have developed test methods for wheeled vehicles, the implementation of which it is proposed to use a hybrid designed stand allows to measure force and power to the drive wheels of the vehicle, and temporal and kinematic parameters of the process of its functioning. The study proved that, to ensure a highly stable and test modes of operation of wheeled vehicles in the process of control of technical condition and diagnosis, you should use hybrid stands.

TRACTION CONTROL OF VEHICLES USING SUPER TWISTING SLIDING MODE CONTROLLER AND A NOVEL TIRE FORCE ESTIMATION STRATEGY

Traction control is an active vehicle safety feature designed to help vehicles makeeffective use of all the traction available on the road when accelerating on lowfrictionroad surfaces. TCS becomes increasingly important to maintain the driver'scontrollability of the vehicle to enhance the driving experience under different roadconditions and avoid road crashes by correcting human errors. This paper utilizes theusage of PID, sliding mode and super twisting sliding mode controllers with anonlinear observer to obtain traction control for electric vehicles. The results of thesimulation illustrate the success of the controllers to operate the vehicle at the desiredwheel slip ratio. When a sudden change occurs, a super twisting controller yields thebest control performance comparing to the other systems. A novel unified structure isused to estimate tire forces by using a nonlinear observer. The independence of theestimates from the vehicle tire model is the reason for the novelty of this structure.

A Novel Wheel Skid Control Based On Motor Energy Distribution Model

How to realize wheel slip control with parameters that can be measured and not relying on hardware precision is a difficult problem in vehicle traction control. From the point of view of energy transfer, this paper uses the feature that the rotational kinetic energy of the wheel increases abnormally when slipping, and proposes a wheel slip control method based on the motor energy distribution model. This method only requires the motor phase voltage, phase current and wheel speed that are easy to measure. In this paper, a complete vector control model of permanent magnet synchronous motor is established, and a PI control algorithm framework is built based on one-wheel vehicle model. The simulation results verify the effectiveness of the proposed algorithm.

Cooperative Vehicle Speed Fault Diagnosis and Correction

Reliable estimation of vehicle speed is an active topic of research in the automotive industry and academia due to its technical challenges as well as applications to vehicle traction and stability control. In this direction, the emergence of new generations of communication technologies has brought new perspectives to traditional studies on vehicle speed estimation and control. To this end, this paper introduces a cooperative vehicle speed fault diagnosis and correction algorithm. The distributed part of the algorithm is based on a distributed function calculation algorithm for vehicle networks. The introduced algorithm enables each vehicle to gather some information from other vehicles in the network in a distributed manner and is robust to communication failures. A procedure to use such information for a single vehicle to diagnose and correct a possible fault in its own speed estimation/measurement is discussed. The functionality and performance of the proposed algorithms are verified via illustrative examples and simulation results.

Two-Wheel Traction Control for Electric Vehicles

This paper deals with a detailed dynamic design of traction control strategies based on fuzzy logic controller. The proposed system is motorized by two independent DC motors associated with two static DC converters and an electrical differential. An approach to both longitudinal and lateral controls of two wheels is described. In fact, by using electric motors, it is possible to have a torque control in each wheel drive, enabling the implementation of a traction control. Consequently, the stability and the safety of the vehicle will be improved. Obtained simulation results confirm the efficiency of the proposed controller.

Optimal traction control for heavy-duty vehicles

Heavy-duty vehicles such as tractors, bulldozers, certain construction and municipal vehicles, soil millers, forestry machinery etc. have a high demand for propulsion force and consequently a high fuel consumption. The current work presents a traction control approach based on motion dynamics estimation for optimizing propulsion force and energy efficiency according to a user-defined strategy. Unscented Kalman filter augmented with a fuzzy-logic system for adaptive estimation is used as the state observer. Simulation case study with an electrically driven tractor is presented. The new method of traction control showed considerable improvement of balancing energy efficiency and propulsion force.

Integrated model predictive control and velocity estimation of electric vehicles

In this paper, an integrated estimation and control system is developed for the stability and traction control of electric vehicles. A model predictive control technique is used to track the desired vehicle yaw rate while maintaining small lateral velocity and tire slip ratios. This paper proposes a new method to control the lateral stability of the vehicle. In this method, the lateral vehicle velocity is controlled indirectly by adjusting the reference yaw rate. This reduces the size of the prediction model and its computational complexity. The controller requires the vehicle’s lateral and longitudinal velocities as well as its tire forces for stability and traction control. This paper also proposes a novel velocity estimation scheme that uses the combined vehicle kinematics and tire model. The developed Kalman-based estimator provides velocities and lateral forces at each corner that are robust to changes in the road condition. The combined model-based and kinematic-based estimation structure mitigates some common problems of the widely used kinematic-based estimators such as the spikes and drifting issues. The stability of the proposed time-varying estimator is also investigated. The designed control and estimation scheme are experimentally validated on various driveline configurations and proven to provide reliable results.

Coordinative traction control of vehicles based on identification of the tyre–road friction coefficient

Traction control, which can be performed by different types of chassis control system, plays an important role in vehicle motion control. Since the propulsive force is actually produced by the friction between the tyre and road, information on the tyre–road friction is crucial for traction control. In this paper, a robust and effective tyre–road friction coefficient identification algorithm for straight acceleration is proposed, and a coordinative traction control method is designed by integrated usage of gear shifting control, engine control and braking control. For different driving conditions, the tyre forces were observed by a sliding-mode observer or calculated from the states of the vehicle directly, and the tyre–road friction coefficients were estimated by the recursive least-squares method or calculated from the linear characteristics between the friction coefficient and the slip ratio consequently. Based on the estimated tyre–road information, a practical and systematic coordinative traction control algorithm was designed to integrate shifting control, engine torque control and braking pressure control. Finally, the proposed methods are verified by both simulations and road tests. The results show that the estimation algorithms can identify the variation in the road conditions with considerable accuracy and response speed, and the controller successfully adjusted the slip ratios of the driving wheels in the stable region with good performances on different types of road.

Integrated stability and traction control for electric vehicles using model predictive control

In this paper, an integrated vehicle and wheel stability control is developed and experimentally evaluated. The integrated structure provides a more accurate solution as the output of the stability controller is not altered by a separate unit, therefore its optimality is not compromised. Model predictive control is used to find the optimal control actions. The proposed control scheme can be applied to a wide variety of vehicle driveline and actuation configurations such as: four, front and rear wheel drive systems. Computer simulations as well as experiments are provided to show the effectiveness of the proposed control algorithm.

A Study of Coordinated Vehicle Traction Control System Based on Optimal Slip Ratio Algorithm

Under complicated situations, such as the low slippery road surface and split-μroad surface, traction control system is the key issue to improve the performance of vehicle acceleration and stability. In this paper, a novel control strategy with engine controller and active pressure controller is presented. First and foremost, an ideal vehicle model is proposed for simulation; then a method for the calculation of optimal slip ratio is also brought. Finally, the scheme of control method with engine controller and active brake controller is presented. From the results of simulation and road tests, it can be concluded that the acceleration performance and stability of a vehicle equipped with traction control system (TCS) can be improved.

A Survey of Traction Control and Antilock Braking Systems of Full Electric Vehicles With Individually Controlled Electric Motors

Wheel slip control for ground vehicles with individually controlled electric motors can be realized with strategies that can significantly differ from the conventional antilock braking system (ABS) and traction control (TC) system. This paper provides a review of state-of-the-art technology and recent developments in TC and ABSs using the actuation of electric motors. Particular attention is paid to the realization of slip estimators, the formalization of torque demand, and the control methods applied for the implementation of TC and ABSs. The performed analysis allowed for the differentiation of several most elaborated methods for slip and torque control and defining still imperfectly investigated problems to be covered by the further development of TC and ABSs for full electric vehicles.

Interaction of In-wheel permanent magnet synchronous motor with tire dynamics

Drive wheel systems combined with the in-wheel permanent magnet synchronous motor (I-PMSM) and the tire are highly electromechanical-coupled. However, the deformation dynamics of this system, which may influence the system performance, is neglected in most existing literatures. For this reason, a deformable tire and a detailed I-PMSM are modeled using Matlab/Simulink. Furthermore, the influence of tire/road contact interface is accurately described by the non-linear relaxation length-based model and magic formula pragmatic model. The drive wheel model used in this paper is closer to that of a real tire in contrast to the rigid tire model which is widely used. Based on the near-precise model mentioned above, the sensitivity of the dynamic tire and I-PMSM parameters to the relative error of slip ratio estimation is analyzed. Additionally, the torsional and longitudinal vibrations of the drive wheel are presented both in time and frequency domains when a quarter vehicle is started under conditions of a specific torque curve, which includes an abrupt torque change from 30 N · m to 200 N · m. The parameters sensitivity on drive wheel vibrations is also studied, and the parameters include the mass distribution ratio of tire, the tire torsional stiffness, the tire damping coefficient, and the hysteresis band of the PMSM current control algorithm. Finally, different target torque curves are compared in the simulation, which shows that the estimation error of the slip ratio gets violent, and the longitudinal force includes more fluctuation components with the increasing change rate of the torque. This paper analyzes the influence of the drive wheel deformation on the vehicle dynamic control, and provides useful information regarding the electric vehicle traction control.

Super-twisting sliding-mode traction control of vehicles with tractive force observer

This paper utilizes the super-twisting algorithm (STA) to obtain a traction controller for road vehicles. The control objective is to operate the vehicles such that a desired wheel slip ratio is achieved. The controller comprises a STA-based sliding-mode control law and a nonlinearity observer. Simulation and experimental studies illustrate the success of the controller. The controller successfully drove the vehicle to operate at the desired wheel slip ratio and efficiently handled the sudden changes of tire–road frictions. The results are compared to those obtained with a conventional sliding-mode controller. It is shown that the STA-based controller yields better control performance.

Traction Control of Electric Vehicles Using Sliding-Mode Controller with Tractive Force Observer

Traction control is an important element in modern vehicles to enhance drive efficiency, safety, and stability. Traction is produced by friction between tire and road, which is a nonlinear function of wheel slip. In this paper, a sliding-mode control approach is used to design a robust traction controller. The control objective is to operate vehicles such that a desired wheel slip ratio is achieved. A nonlinearity observer is employed to estimate tire tractive forces, which are used in the control law. Simulation and experimental results have illustrated the success of the proposed observer-based controller.