Research on Compensation Redundancy Control for Basic Force Boosting Failure of Electro-Booster Brake System

<div class="section abstract"> <div class="htmlview paragraph">This Recommended Practice provides a taxonomy for motor vehicle <i>driving automation systems</i> that perform part or all of the <i>dynamic driving task</i> (<i>DDT</i>) on a <i>sustained</i> basis and that range in level from <i>no driving automation</i> (level 0) to <i>full driving automation</i> (level 5). It provides detailed definitions for these six levels of <i>driving automation</i> in the context of <i>motor vehicles</i> (hereafter also referred to as “<i>vehicle</i>” or “<i>vehicles</i>”) and their <i>operation</i> on roadways. These level definitions, along with additional supporting terms and definitions provided herein, can be used to describe the full range of <i>driving automation features</i> equipped on <i>motor vehicles</i> in a functionally consistent and coherent manner. “On-road” refers to publicly accessible roadways (including parking areas and private campuses that permit public access) that collectively serve users of <i>vehicles</i> of all classes and <i>driving automation</i> levels (including no <i>driving automation</i>), as well as motorcyclists, pedal cyclists, and pedestrians.</div> <div class="htmlview paragraph">The levels apply to the <i>driving automation feature(s)</i> that are engaged in any given instance of on-road <i>operation</i> of an equipped <i>vehicle</i>. As such, although a given <i>vehicle</i> may be equipped with a <i>driving automation system</i> that is capable of delivering multiple <i>driving automation features</i> that perform at different levels, the level of <i>driving automation</i> exhibited in any given instance is determined by the <i>feature(s)</i> that are engaged.</div> <div class="htmlview paragraph">This document also refers to three primary actors in <i>driving</i>: the (human) <i>driver</i>, the <i>driving automation system</i>, and other <i>vehicle</i> systems and components. These other <i>vehicle</i> systems (or the <i>vehicle</i> in general terms) do not include the <i>driving automation system</i> in this model, even though as a practical matter a <i>driving automation system</i> may actually share hardware and software components with other <i>vehicle</i> systems, such as a processing module(s) or operating code.</div> <div class="htmlview paragraph">The levels of <i>driving automation</i> are defined by reference to the specific role played by each of the three primary actors in performance of the <i>DDT</i>. “Role” in this context refers to the expected role of a given primary actor, based on the design of the <i>driving automation system</i> in question and not necessarily to the actual performance of a given primary actor. For example, a <i>driver</i> who fails to <i>monitor</i> the roadway during engagement of a level 1 adaptive cruise control (ACC) system still has the role of <i>driver</i>, even while s/he is neglecting it.</div> <div class="htmlview paragraph"><i>Active safety systems</i>, such as electronic stability control and automated emergency braking, and certain types of driver assistance systems, such as lane keeping assistance, are excluded from the scope of this <i>driving automation</i> taxonomy because they do not perform part or all of the <i>DDT</i> on a <i>sustained</i> basis and, rather, merely provide momentary intervention during potentially hazardous situations. Due to the momentary nature of the actions of <i>active safety systems</i>, their intervention does not change or eliminate the role of the <i>driver</i> in performing part or all of the <i>DDT,</i> and thus are not considered to be <i>driving automation</i>.</div> <div class="htmlview paragraph">It should, however, be noted that crash avoidance features, including intervention-type <i>active safety systems</i>, may be included in <i>vehicles</i> equipped with <i>driving automation systems</i> at any level. For <i>ADS</i>-equipped <i>vehicles</i> (i.e., levels 3-5) that perform the complete <i>DDT</i>, crash avoidance capability is part of <i>ADS</i> functionality.</div></div>

<div class="htmlview paragraph">The performance of an integrated Vehicle Stability Control (VSC) system depends on not only control logic itself, but also the performance of state estimator and control threshold. In conventional VSCs, a control threshold is designed by vehicle characteristics and is centered on average drivers. A VSC algorithm with variable control threshold has been investigated in this study. The control threshold can be determined by phase plane analysis of side slip angle and angular velocity. Vehicle side slip angle estimator has been evaluated using test data. Estimated side slip angle has been used in the determination of the control threshold. The performance of the proposed VSC algorithm has been investigated by human-in-the-loop simulation using a vehicle simulator. The simulation results show that the control threshold has to be determined with respect to the driver steering characteristics. A VSC with variable control thresholds would provide an improvement compared to a VSC with a constant threshold.</div>

<div class="section abstract"><div class="htmlview paragraph">The longitudinal dynamics control is an essential task of vehicle dynamics control. In present, it is usually applied by adjusting the slip ratio of driving wheels to achieve satisfactory performances both in stability and accelerating ability. In order to improve its performances, the coordination of different subsystems such as engine, transmission and braking system has to be considered. In addition, the proposed algorithms usually adopt the threshold methods based on less road condition information for simpleness and quick response, which cannot achieve optimal performance on various road conditions.</div><div class="htmlview paragraph">In this paper, an integrated longitudinal vehicle dynamics control algorithm with tire/road friction estimation was proposed. First, a road identification algorithm was designed to estimate tire forces of driving wheels and the friction coefficient by the method of Kalman Filter and Recursive Least Squares (RLS). Then, a rule based integrated control algorithm which coordinate the engine torque control, brake pressure control and transmission shifting control was built to improve the vehicle driving performance.</div><div class="htmlview paragraph">During the longitudinal dynamics control procedure, the transmission shifting control algorithm firstly decided whether the vehicle starting up at the 1st or the 2nd gear based on the road condition. Then, the engine torque control algorithm was applied to adjust the slip ratio of driving wheels. Its process was divided into three phases. In each phase, different control rules with integration of engine torque and brake torque were set by the combined feed forward and feedback methods to meet with requirements of accuracy, rapidity and stability. The brake pressure was determined by method of sliding mode control (SMC) for its rapid adjustment characteristic, in the meanwhile, it was also an important component to be considered in engine torque feedback control.</div><div class="htmlview paragraph">Finally, the algorithm was verified by using Matlab/Simulink and CarSim co-simulation, the results show that the proposed control algorithm could regulate slip ratios of driving wheels fast and accurately, and the vehicle driving performance could be improved effectively.</div></div>

<div class="section abstract"><div class="htmlview paragraph">Heavy truck rollover remains a primary factor in truck driver fatalities and injury. Roll stability control (RSC) and electronic stability control (ESC) are technologies that have been introduced to reduce the incidence of rollover in heavy truck crashes. This report provides an analysis of the real-world experience of a large for-hire company that introduced RSC into its fleet starting in 2004. The carrier provided a well-documented set of data on the operations of its truck-tractors, including both those equipped with RSC and those that did not have RSC installed. The purpose of the analysis is to determine the effect of RSC on the probability of rollover, as well as to identify other factors that either contribute to rollover or help reduce its incidence.</div><div class="htmlview paragraph">This study presents results on the incidence of rollover both in terms of rollovers per 100 million miles traveled and the percentage of crashes that resulted in rollover. The effect on rollover of cargo loading, driver tenure at the company, driver age, type of operation (bulk, truckload, LTL, etc.), and other factors are examined.</div><div class="htmlview paragraph">Recent hires had a 50% higher rollover rate than more experienced drivers; younger drivers had higher rollover rates than older drivers. Cargo loading and curved roads were both strongly associated with rollover. A statistical model was developed to estimate the separate effect of RSC, controlling for driver age, tenure, cargo loading, roadway curvature, and type of operation. The results showed that RSC reduced the probability of rollover by about 25%.</div></div>

<div class="section abstract"><div class="htmlview paragraph">Residual brake torque (RBT) is generated in disc brakes as a result of contact between brake disc and brake pads when the braking pressure is not applied. Among the negative implications of RBT are, notably, dispensable additional fuel consumption as well as increased pad (taper) wear.</div><div class="htmlview paragraph">Several properties of the brake system have a direct influence on the level of residual torque [<span class="xref">1</span>].</div><div class="htmlview paragraph">A major effect is connected to the caliper properties determining the clearance gap. This is characterized by the default air gap between pads and disc and its distribution regarding vehicle inner and outer sides (piston and fist sides for floating type calipers).</div><div class="htmlview paragraph">Initial air gap is mainly influenced by the sealing grove design (between piston and housing, where the sealing ring is positioned). The retraction of the piston due to the sealing ring, also called rollback, mainly depends on the load case (e.g. applied pressure and temperature). Insufficient air gap will lead to residual clamping forces between pads and rotor and thus the friction coefficient itself influences residual brake torque directly.</div><div class="htmlview paragraph">In addition, there are also parameters which can exert influence on the residual brake torque, which are not caliper, but primarily rotor-related. These include axial thermal deformation, thermal coning effects, lateral runout (LRO) of the rotor due to geometrical tolerances and also LRO excited by tensioning the rotor to the wheel hub.</div><div class="htmlview paragraph">These influences on caliper drag are typically well known and understood at least qualitatively. To accomplish customer’s requirements according to RBT, the calipers are extensively tested (e.g. NEDC/WLTP, coast down, ATE) in different test procedures on dynamometers. Some test specifications contain ambitious requirements on the intended drag torque (e.g. less than 0.1 Nm).</div><div class="htmlview paragraph">In contrast to the requirements, other possible influencing parameters, e.g. disc deflection, caused by vehicle dynamics, is currently not included in any RBT-test on dynamometer. During e.g. curve driving, the lateral forces are generated between tire and road and can also be transferred into the rim/rotor/hub/bearing contact at the knuckle.</div><div class="htmlview paragraph">Additionally this is overlain by the wheel load. It must be assumed that the side forces and wheel load affect the deflection of the disc. Thus, this may also influence residual brake torque.</div><div class="htmlview paragraph">This paper shows Continental’s setup for application of wheel load and side forces on dynamometer. Based on previous results from vehicle test, the disc deflection during dynamic driving is characterized and the wheel load setup was enhanced to apply side forces. Different positions of side force and wheel load induction are compared.</div><div class="htmlview paragraph">Furthermore the setup is used to identify the influence of dynamic disc deflection on caliper drag. This is done with an exemplarily floating-type caliper (FN). Dedicated measurements illustrate the influence of left and right curve driving on the level of RBT.</div></div>

This paper presents a wheel cylinder pressure control algorithm for application to the vehicle electronic stability control (ESC) systems for commercial trucks. An ESC system is an active system that improves the driving stability by distributing the appropriate braking pressure to each wheel, which is an essential system for safe driving. It is important that the ESC system, through proper braking pressure supply, delivers the correct pressure under control. However, to reduce the cost involved, commercial trucks use a solenoid valve of the on/off-type, rather than a proportional valve that has good pressure control capability. The performance of a proposed wheel pressure control system based on an on/off solenoid valve control was verified by means of experiments conducted using the wheel pressure control algorithm presented in this paper.

<div class="section abstract"><div class="htmlview paragraph">As vehicles become electrified and more intelligent in terms of sensing, actuation and processing; a number of interesting possibilities arise in controlling vehicle dynamics and driving behavior. Over-actuation with in- wheel motors, all wheel steering and active camber is one such possibility, which facilitate the control strategies that push boundaries in energy consumption and safety. Optimal control can be used to investigate the best combinations of control inputs to an over-actuated system. This paper shows how an optimal control problem can be formulated and solved for an over-actuated vehicle case, and highlights the translation of this optimal solution to a real-world scenario, enabling intelligent means to improve vehicle efficiency.</div><div class="htmlview paragraph">This paper gives an insight into Dynamic Programming (DP) as an offline optimal control method that guarantees the global optimum. Therefore the optimal control allocation to minimize an objective function and simultaneously fulfill the defined constraints can be achieved. As a case study the effects of over-actuation on the cornering resistance were investigated in two different maneuvers i.e. step steer and sine with dwell, where in both cases the vehicle assumes to be in steady state situation. In this work the cornering resistance is the main objective function and maintaining the reference trajectory is the constraint which should be fulfilled. A parameter study is conducted on the benefits of over-actuation, and depending on the type of over-actuation about 15% to 50% reduction in cornering resistance were observed during step steer and sine with dwell maneuver respectively. From a second parameter study that focused on COG position from a safety perspective, it is more beneficial for the vehicle to be designed to under-steer than over-steer. Finally, a method is described to translate the offline optimal results to vehicle implementable controllers in the form of both feed-through lookup-tables and rule-based feed-forward control.</div></div>

This publication describes a process to develop and calibrate a physics-based model for vehicle stability control. The approach consists of two phases. In the first phase, a baseline vehicle dynamics model was established using the commercially available simulation tool CarSim. The model was calibrated and validated for a variety of steady state, transient state and brake intervention handling manoeuvres using experimental test data. In the second phase, a simplified stability control model was developed in Matlab/Simulink based on representative control strategies. Subsequently, the vehicle dynamics model and stability control model developed were coupled using co-simulation and calibrated such that the closed-loop system behaviour approximated the real world test behaviour as best as possible. Finally, two case studies were developed to showcase the application of the closed-loop mathematical model in simulation-based studies addressing aftermarket vehicle modifications and their implication on vehicle stability control behaviour.

<div class="section abstract"><div class="htmlview paragraph">A novel type of regenerative braking system for electric vehicles is proposed in this paper. Four pressure-difference-limit valves, two relief valves and two brake pedal simulators, are added to the layout of a conventional four-channel hydraulic modulator. The cooperation of relief valves and hydraulic pumps provides a stabilized high-pressure source. Pressure-difference-limit valves ensure that the pressure in each wheel cylinder can be modulated separately at a high precision. Besides, the functions of anti-lock braking system and electronic stability program are integrated in this regenerative braking system. The models of regenerative braking controller and vehicle dynamics are built in MATLAB/Simulink. Hydraulic brake model is built in AMESim through a parameterized and modularized method. Meanwhile, the control strategy of hydraulic brake modulation and brake force distribution are designed. Simulations are conducted via co-simulation interface between MATLAB and AMESim under scenarios of typical braking and ECE driving cycle. Simulation results show that regenerative and hydraulic braking forces are coordinated well during typical braking process, verifying the feasibility and effectiveness of the models built and strategies proposed. Under an ECE driving cycle, the proposed RBS can recover more than 75% of the total recoverable braking energy, which lengthen the vehicle’s driving range by more than 24%.</div></div>

<div class="htmlview paragraph">Because of an increasing popularity of the SUV and Light Truck with an elevated center of gravity, the rollover propensity is higher than before. This paper proposes a method to detect and prevent an impending rollover of the vehicle using Electronic Stability Control(ESC). The Rollover Prevention(ROP) function determines a rollover critical situations based on the rollover propensity index and generates a proper activation through a brake and engine intervention. The rollover propensity index is calculated from the relative distance of the trajectory to the threshold on the phase plane of roll-angle and roll-rate. The threshold on the phase plane is deduced from the conservation of energy in the roll model. For the detection and activation of continuous rollover phenomena, we use the TTL concept that presents a gradually increasing rollover propensity. The proposed algorithm in this paper is shown to be effective in mitigating a rollover through the simulation and vehicle test.</div>

ESP (Electronic Stability Program) is a kind of active safety device which can comprehensively improve the brake, drive and high speed performance of the automobile. Mastering the key technologies of ESP control system plays an important role in improving the performance of ESP. Vehicle dynamic model and engine model with 9 degrees of freedom of one passenger car were established based on vehicle and tire force analysis. The ESP fuzzy control model based on slip ratio control was established. The optimal slip ratio under different pavements was confirmed by using road recognition method. ESP fuzzy control was realized under MATLAB/Simulink environment. And through the split road simulation prove the validity of the road recognition algorithm, and the effectiveness of the fuzzy control strategy. The simulation and the debugging results indicate that the ESP fuzzy control strategy based on road automatic identification works well, and obviously improves automobile stability under critical situations. And all of these make a good foundation for the further research and development for the ESP integrated control system.

<div class="section abstract"><div class="htmlview paragraph">This paper describes an optimal torque vectoring strategy for 4WD electric vehicles (EV) in order to improve vehicle maneuverability, lateral stability and at the same time prevent vehicle rollover. The 4WD EV is driven using an in-line motor at a front driving shaft and in-wheel motors at rear wheels. Many previous studies have been conducted to determine a desired traction force and a yaw moment input for human driver's intention or vehicle stability control. The driving control algorithm consists of three parts: a supervisory controller that determines the control mode, admissible control region, and desired dynamics, such as the desired speed and yaw rate, an upper-level controller that computes the traction force input and yaw moment input to track the desired dynamics and an optimal torque vectoring algorithm that determines actuator commands, such as the front in-line motor, rear in-wheel motors and independent brake modules. The optimal torque vectoring algorithm is developed to map the desired traction force and the yaw moment input to the actuators, taking into account the actuator constraints. Also, a wheel slip controller is designed to keep the slip ratio at each wheel below a limit value. An optimization-based control allocation method is used to determine the actuator command in order to apply the desired traction force, the yaw moment input and the wheel slip control inputs to the test platform. Numerical simulation studies have been conducted to evaluate the proposed optimal torque vectoring algorithm. It has been shown from simulation studies that the vehicle maneuverability and vehicle stability can be improved by the proposed driving control algorithm.</div></div>

<div class="section abstract"><div class="htmlview paragraph">Tread depth, inflation pressure, tire temperature, and road surface condition are among the most notable factors that have a noticeable effect on the tire force and moment characteristics. They can vary significantly during the operation of a tire and can effectively modify tire (and thus vehicle) performance. This study presents details of an adaptive magic formula (MF) tire model capable of coping with changes to the tire operating condition. More specifically, extensions have been made to the magic formula expressions for tire cornering stiffness and peak grip level, to account for variations in the tire inflation pressure, load, tread-depth and temperature. As a next step, the benefits of using an adaptive tire model for vehicle control system applications is demonstrated through simulation studies for enhanced vehicle control systems using an adaptive tire model in comparison to traditional control systems based on a non-adaptive tire model with fixed model parameters. In particular, this paper investigates the improvements an adaptive tire model would bring into the following vehicle control system strategies: Electronic Stability Control (ESC) system, Brake Torque Vectoring (BTV) system, and Rear Wheel Steer (RWS) system.</div></div>

<div class="section abstract"><div class="htmlview paragraph">This paper presents a novel sensitivity analysis framework for differential braking as a backup steering solution in fail-operational Steer-by-Wire systems. The fault-tolerant design approach of Steer-by-Wire and steering systems for highly automated driving relies on the availability of road wheel actuators (RWA). Redundancies are therefore commonly used to ensure fail-operationality. Since its widespread implementation in production vehicles through electronic stability control, the use of differential braking as a cost-effective measure is desirable to increase functional diversity. However, feasible lateral accelerations through this backup solution are limited compared to conventional steering systems and lie close to ordinary driving scenarios. To address this limitation, this work investigates the influence of chassis parameters on differential braking performance. After defining characteristic values and a simulation test plan, a preliminary analysis using a linear single-track model incorporating differential braking is conducted. The most influential vehicle parameters are identified, including the newly derived effect of rear axle cornering stiffness—an aspect not previously quantified in this context. A second, more detailed sensitivity analysis is performed using a characteristic-based dual-track model equipped with representative brake, steering, and chassis subsystems. Parameter variation ranges are derived from distributed data-based sources. The resulting total effects reveal new insights into the sensitivity of chassis design parameters, including scrub radius, steering friction, rear axle cornering stiffness, and coupling effects of suspension and kinematics. The findings provide valuable guidance for future chassis design in fail-operational steering systems and highlight the importance of underexplored parameters in achieving reliable lateral dynamics through differential braking.</div></div>

Many vehicle state parameters will be used in the electronic stability control (ESC) system. ESC system must estimate some important parameters based on a few sensors and help to develop the control strategies. ESC can estimate the work state of vehicle with tire forces based on the estimating of adhesion coefficient and wheel slip ratio, then determine the control strategy on wheels. The tire forces to be estimated include vertical force, lateral force and longitudinal force on tires. In this article, a method of estimating vertical tire force based on vehicle geometric parameters and vehicle working state, a method of estimating tire lateral force based on vehicle yaw rate and the change of tire longitudinal forces caused by the change of vertical forces and lateral forces have researched. Co-simulation shows that the trend of the tire forces changing is correct and small error on high and low adhesive pavement. The ESC system is available on HIL bench and testing vehicle, so the estimates of tire forces are practical methods.