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.     

Longitudinal vehicle state estimation using nonlinear and parameter-varying observers

A corner-based velocity estimation approach is proposed which is used for vehicle’s traction and stability control systems. This approach incorporates internal tire states within the vehicle kinematics and enables the velocity estimator to work for a wide range of maneuvers without road friction information. Tire models have not been widely implemented in velocity estimators because of uncertain road friction and varying tire parameters, but the current study utilizes a simplified LuGre model with the minimum number of tire parameters and estimates velocity robust to model uncertainties. The proposed observer uses longitudinal forces, updates the states by minimizing the longitudinal force estimation error, and provides accurate outcomes at each tire. The estimator structure is shown to be robust to road conditions and rejects disturbances and model uncertainties effectively. Taking into account the vehicle dynamics is time-varying, the stability of the observer for the linear parameter varying model is proved, time-varying observer gains are designed, and the performance is studied. Road test experiments have been conducted and the results are used to validate the proposed approach.     

Model predictive control of vehicle stability using coordinated active steering and differential brakes

This paper studies model predictive control of lateral stability of vehicles using coordinated active front steering and differential brakes. The controller is designed based on a bicycle model of the vehicle and the moment of the differential brakes is considered as an external torque. The prediction model calculates the prospective values of the vehicle’s yaw rate, lateral velocity, and tire slip angles over the prediction window. The sideslip angle of the vehicle is enforced within a permissible range using soft constraints on the lateral velocity in order to guarantee persistent feasibility. Using computer simulations, the controller is shown to provide proactive control actions to control the vehicle’s sideslip angle. The closed-loop response of the controller is also studied in experimental tests on an instrumented test vehicle. The results show satisfactory performance in various combinations of active front steering and differential brakes. In addition, the computational time of the controller is measured and shown to be safely below the sample time of the controller.     

Adaptive vehicle traction force control for intelligent vehicle highway systems (IVHSs)

This paper is concerned with robust longitudinal control of vehicles in intelligent vehicle highway systems by adaptive vehicle traction force control. Two different traction force controllers, adaptive fuzzy logic control and adaptive sliding-mode control, are proposed and applied to the fastest stable acceleration/deceleration and robust vehicle platooning problems. The motivation for investigating adaptive techniques arises from the unknown time-varying nature of the tire/road surface interaction that governs vehicle traction. Synchronous application of the engine or brake torques is also proposed for more stable vehicle maneuvers. The lack of controllability during braking (only one net input torque for the two control objectives, i.e., front and rear wheel slips) is partly overcome by applying auxiliary engine torque. Simulations of the two control methods are conducted using a complex nonlinear vehicle model which fully describes the dynamic behavior of the vehicle. Both controllers result in good performance under time-varying operating conditions.     

Seismic source model for moving vehicles

We develop a method for the loading of ground by moving vehicles in large finite-difference time-domain simulations of seismic wave propagation. The objective is to realistically produce two distinct types of ground loading for either wheeled or tracked vehicles in our propagation models: lower frequency loading associated with suspension dynamics and higher frequency impulsive loading associated with tire treads or wheels rolling over individual track blocks. These loading characteristics are important because field measurements show that vehicle ground forcing in both frequency bands produces seismic surface waves that networked sensors can remotely process for security applications. The method utilizes a vehicle-dynamics model to calculate a response to vehicle acceleration and ground features such as bumps; calculates forces transmitted to the ground; distributes these forces to staggered points of a finite-difference model; and simulates seismic wave propagation away from the vehicle. We demonstrate the method using bounce-and-pitch models of wheeled and tracked vehicles. We show that by carefully preprocessing force inputs, we can accurately simulate wave propagation and seismic signatures in finite-difference analyses of vehicles moving continuously over terrain.     

Real-time slip-based estimation of maximum tire-road friction coefficient

This paper presents a real-time maximum tire-road friction coefficient estimation method and field test results. The estimator is based on the relationship between the wheel slip ratio and the friction coefficient. An effective tire radius observer and a tire normal force observer have been designed for the computation of the slip ratio from wheel speed and vehicle speed measurements. The effective tire radius observer has been used so that the proposed method works for all driving situations. A tractive force estimator, a brake gain estimator, and a normal force observer have been used for the estimation of the friction coefficient. The proposed estimation method for the maximum tire-road friction coefficient has been implemented using a fifth wheel and typical vehicle sensors such as engine speed, carrier speed, throttle position, and brake pressure sensors.     

A Vehicle Roll-Stability Indicator Incorporating Roll-Center Movements

In the development of active-passive roll control systems, a vehicle model that can represent realistic roll behavior is essential for predicting the impending rollover and for accurately applying the control force to avoid vehicle rollover. The vehicle roll center is a key parameter that influences the vehicle roll dynamics. Since the roll center movement becomes important as the vehicle roll angle increases, it is desirable to include this effect in the roll control system. This paper proposes a dynamic roll stability indicator (RSI) incorporating roll center movement that generates rollover threshold in terms of lateral acceleration. A robust parameter identification algorithm using a disturbance observer is designed to estimate the lateral and vertical roll center movements. These estimates are later used in the RSI to update the rollover threshold. The effectiveness of the proposed method is demonstrated through simulations, and its performance is compared with other rollover warning algorithms.     

Load sensing bearing based road-tyre friction estimation considering combined tyre slip

This work discusses a road-tyre friction estimator considering combined tyre slip. The friction estimator design is motivated by its importance in vehicle dynamics control as accurate friction estimation can improve performance and safety. The estimator uses tyre force measurements from Load Sensing Bearing (LSB) technology and does not rely on parameterized tyre model. The tyre force measurements benefit the estimator mainly because of the uncertainties and nonlinearities of the tyre force characteristics. The proposed estimator uses tyre slip and tyre force representations where the longitudinal and lateral tyre slips and forces are combined into a single tyre slip and tyre force values. This representation makes the method effective during pure longitudinal dynamics, pure lateral dynamics and for combined slip. In addition, individual tyre-road friction estimation is possible with the proposed estimator and a computationally inexpensive algorithm, suitable for real-time implementation, is used to estimate the friction. The estimator is studied in simulation during pure braking, pure cornering and for combined slip. Further, the estimator is simulated in closed loop with a yaw rate controller to study whether the estimator improves vehicle safety. Finally the estimator is validated using test data from several maneuvers performed on a test vehicle instrumented with LSB technology.     

Direct Yaw-Moment Control of an In-Wheel-Motored Electric Vehicle Based on Body Slip Angle Fuzzy Observer

A stabilizing observer-based control algorithm for an in-wheel-motored vehicle is proposed, which generates direct yaw moment to compensate for the state deviations. The control scheme is based on a fuzzy rule-based body slip angle (beta) observer. In the design strategy of the fuzzy observer, the vehicle dynamics is represented by Takagi-Sugeno-like fuzzy models. Initially, local equivalent vehicle models are built using the linear approximations of vehicle dynamics for low and high lateral acceleration operating regimes, respectively. The optimal beta observer is then designed for each local model using Kalman filter theory. Finally, local observers are combined to form the overall control system by using fuzzy rules. These fuzzy rules represent the qualitative relationships among the variables associated with the nonlinear and uncertain nature of vehicle dynamics, such as tire force saturation and the influence of road adherence. An adaptation mechanism for the fuzzy membership functions has been incorporated to improve the accuracy and performance of the system. The effectiveness of this design approach has been demonstrated in simulations and in a real-time experimental setting.     

A new strategy for traction control in turning via engine modeling

The driving stability is affected by driven wheel slip, which can be controlled by the driven wheel torque. In a vehicle powered by an internal combustion engine, the torque can be controlled by an engine management system. The sliding mode algorithm is the mechanism behind the design of the traction control system (TCS). The longitudinal slip is controlled by the position of the throttle valve. The vehicle model used has seven degrees of freedom and a two-state engine model, i.e., the mass of air in the intake manifold and the engine speed. Time-delay transport is considered in the engine model used. A nonlinear tire model for combined slip is used for tire force computation. Due to the nonlinear dynamic of the tire, vehicle, and engine, the control method of sliding mode is used for its robustness. A controller is designed based on the dynamic surface control, for which two first-order surfaces are defined. The effectiveness of the controller is demonstrated with simulation results for different maneuvers. Results show that for different road conditions, the acceleration performance, directional stability, and steerability of a vehicle equipped with TCS is improved. The reason is that the slip is controlled by keeping it in a desired range     

Ein Beitrag zur nichtlinearen Fahrdynamik-regelung: die differentielle Flachheit des Einspurmodells

This contribution is primarily concerned with the system analysis of the bicycle dynamics, revealing the differential flatness property as a main result. A physically relevant representative for the flat output is introduced, with its components given as the lateral and the longitudinal velocity of a distinguished point located on the longitudinal axis of the vehicle. This flatness property is shown for the front-, rear- and all-wheel driven vehicle, without referring to particular representatives of the functions modelling the lateral tire forces. Following the flatness based control theory, a novel approach to nonlinear vehicle dynamics control is discussed.     

Estimation of sideslip angle and cornering stiffness of an articulated vehicle using a constrained lateral dynamics model

In this paper, a constrained lateral dynamics model of articulated vehicles and an algorithm for estimating sideslip angle and cornering stiffness are proposed. The articulated vehicle was modeled using the bicycle model, linear tire model, and modified Dug-off model. The normal force of each axle included in the model was estimated based on the longitudinal load transfer model. Physical constraints were applied to reduce model states. Accurate sideslip angle and cornering stiffness are essential for vehicle control safety and autonomous driving performance. The sideslip angle and cornering stiffness were simultaneously estimated using a dual linear time-varying (LTV) Kalman filter. The observability matrix guaranteed the convergence of the proposed estimation algorithm. The estimation performance was verified by simulation with TruckSim and an experiment using an articulated bus.     

A fuzzy logic control for antilock braking system integrated in the IMMa tire test bench

The use of fuzzy control strategies has recently gained enormous acknowledgement for the control of nonlinear and time-variant systems. This article describes the development of a fuzzy control method for a tire antilock system in vehicles while braking, integrated in a tire test bench, thereby allowing us to imitate the functioning and to understand the behavior of these systems in a reliable way. One of the inconveniences found in the development of these systems has been the difficulty of adjustment to the real conditions of a functioning vehicle. The main advantage obtained when using the tire test bench is the possibility of being able to reproduce the conditions established as fundamental to the operation of the antilock brake system (ABS) in a reliable and repetitive way, and to adjust these systems until optimal performance is obtained. The fuzzy control system has been developed and tested in the tire test bench to be able to refine its fundamental parameters, obtaining adequate results in all the studied conditions. The ease of the bench for the development and verification of new control systems for ABS has been demonstrated.     

Real-Time Clamp Force Measurement in Electromechanical Brake Calipers

In brake-by-wire systems, central controllers require accurate information about the clamp force between the brake pad and the disc as a function of pad displacement, which is usually denoted as the characteristic curve of the caliper. Due to aging, temperature, and other environmental variations, caliper characteristic curves vary with time. Therefore, automatic caliper calibration in real-time is vital for high-performance braking action and vehicle safety. Due to memory and processing-power limitations, the calibration technique should be memory efficient and of low computational complexity. In a typical electromechanical-braking-system design, clamp force measurement variations with actuator displacement is hysteretic. This paper introduces a simple and memory-efficient real-time calibration technique in which a clamp-force model is fitted to the data samples around each hysteresis cycle. The model includes a Maxwell-slip model for the hysteresis caused by friction. Experimental results from the data recorded in various temperatures show that the proposed technique results in clamp force measurements with less than 0.7% error over the range of clamp-force variations. It is also shown that, by using these measurements, the characteristic curve can be accurately calibrated in real-time.     

Real-time monitoring of the ionospheric state with the use of a 3D assimilation model

The 3D assimilation ionospheric model is improved to assimilate in real time the ionospheric total electron content (TEC) measurement data from the International GNSS Service (IGS) network of ground-based stations of the global positioning system (GPS). This model makes it possible to calculate the space-time electron-concentration distributions of electrons, concentration of the seven main ions, and the temperature and velocity of electrons and ions in the ionosphere at altitudes of 100–1000 km. The model calculations of the ionospheric TEC are compared to the TEC measured on slant paths with the use of two-frequency receivers of the ground-based IGS network of stations not included into the assimilation scheme. The model calculations of ionospheric electron-concentration height profiles are compared to the data measured by an incoherent-scatter radar. It is shown that the ionospheric parameters calculated without using experimental data are in worse agreement with the radar measurement data than the results obtained with the assimilation model of the ionosphere. The model-calculated electron concentrations are compared to the data from the FORMOSAT-3/COSMIC system of medium-Earth-orbit satellites.     

Tire Dynamic Deflection and Its Impact on Vehicle Longitudinal Dynamics and Control

Vehicle longitudinal dynamics are shaped by tire characteristics. A number of empirical tire models have been proposed to explain longitudinal tire behaviors. These models typically explain the static tire behaviors and overlook the behaviors during transients. As a result, they often do not reflect the dynamic interactions between the tire and the vehicle under operational environments, most noticeably, the longitudinal overshoots and oscillations that occur immediately after a vehicle is stopped with hard braking. This paper proposes a dynamic-deflection tire (DDT) model that not only specifies the known longitudinal tire characteristics but also captures the dominant tire transient properties. When incorporating this DDT model into a conventional vehicle longitudinal model, these often-ignored tire-vehicle structure modes can be predicted accurately. The resultant model shows that the tire transient characteristics do impact ride comfort and affect longitudinal control designs at low vehicle speeds. A passenger car was tested under both open-loop and closed-loop scenarios, and the experimental results verified the model predictions.     

Iterative learning control of antilock braking of electric and hybrid vehicles

Hybrid electric vehicles (HEVs) use multiple sources of power for propulsion which provides great ease and flexibility to achieve advanced controllability and additional driving performance. In this paper, the electric motor in HEV and electric vehicle (EV) propulsion systems is used to achieve antilock braking performance without a conventional antilock braking system (ABS). The paper illustrates that the antilock braking of HEV can be easily achieved using iterative learning control for various road conditions. A vehicle model, a slip ratio model, and a vehicle speed observer were developed to control the antilock performance of HEV during braking. Through iterative learning process, the motor torque is optimized to keep the tire slip ratio corresponding to the peak traction coefficient during braking. Simulations were performed on a compact size vehicle to validate the proposed control method. The control algorithm proposed in this paper may also be used for the ABS control of conventional vehicles.     

Gain-scheduled robust control for lateral stability of four-wheel-independent-drive electric vehicles via linear parameter-varying technique

This paper proposes a robust gain-scheduled H_∞ controller for lateral stability control of four-wheel-independent-drive electric vehicles via linear parameter-varying technique. The controller aims at tracking the desired yaw rate and vehicle sideslip angle by controlling the external yaw moment. In the design of controller, uncertain factors such as vehicle mass and tire cornering stiffness in vehicle lateral dynamics are represented via the norm-bounded uncertainty. To address the importance of time-varying longitudinal velocity for vehicle lateral stability control, a linear parameter-varying polytopic vehicle model is built, and the built vehicle model depends affinely on the time-varying longitudinal speed that is described by a polytope with finite vertices. In order to reduce conservative, the hyper-rectangular polytope is replaced by a hyper-trapezoidal polytope. Simultaneously, the quadratic D-stability is also applied to improve the transient response of the closed-loop system. The resulting gain-scheduling state-feedback controller is finally designed, and solved utilizing a set of linear matrix inequalities derived from quadratic H_∞ performance and D-stability. Simulations using Matlab/Simulink-Carsim® are carried out to verify the effectiveness of the proposed controller with a high-fidelity, CarSim®, full-vehicle model. It is found from the results that the robust gain-scheduled H_∞ controller suggested in this paper provides improved vehicle lateral stability, safety and handling performance.     

Lateral control of higher order nonlinear vehicle model in emergency maneuvers using absolute positioning GPS and magnetic markers

The performance of an automatic steering system based on an absolute positioning global positioning system (GPS) and a magnetic marker reference system during emergency situations is examined in this paper, as it is a vital safety issue in highway automation. Robust control technique in the form of parameter space approach in an invariance plane is utilized for lateral controller design based on a higher order nonlinear vehicle model. In addition, the control system incorporates an exponential smoothing algorithm based on road curvature preview for vehicle-handling enhancement. The proposed estimation and control system is shown, in computer simulations, to be effective in handling vehicle emergency situations.