Vehicle Braking Research Articles

Pedestrian-ground contact injury protection method under the constraint of the vehicle-front end shape and its robust analysis

This research paper introduces a novel approach for protecting pedestrians from ground contact injuries through incorporating vehicle front-end shape constraints in the braking control method. The feasibility of the proposed method is evaluated through simulations, and its resilience to different disturbances is studied. Results indicate that the proposed method effectively manages vehicle movement by monitoring the first head-vehicle contact time (t1 ) between the pedestrian’s head and the vehicle. The simulations show that the proposed method reduces ground-related Weighted Injury Cost (WIC) by up to 84%, and the proportion of ‘safe’ mechanisms increases to 74%. The robustness of the braking control method is also evidenced by its high performance during disturbances such as changes in friction coefficient, lag time, and t1 . Furthermore, the anti-disturbance abilities of each vehicle shape are comparable except for t1 disturbance. There are no significant variations in the braking control protection effect between different friction coefficients and each t1 , but noticeable differences exist between lag times. Further analysis reveals that parameter disturbances affect the deceleration of the vehicle and the impact speed of the pedestrian and vehicle, leading to changes in the Vz (Vertical head-to-ground impact velocity), thorax acceleration, pelvic lateral impact force, and knee lateral bending angle, which subsequently increases the risk of pedestrian ground contact injuries. The proposed method offers an efficient solution for protecting pedestrians from ground contact injuries by controlling vehicle braking, highlighting its practical utility.

Analyzing Tribological and Mechanical Properties of Polymer Nanocomposite for Brake Pad Applications - A Critical Review

A brake pad is an integral component of a vehicle's braking system, designed to impart controlled friction and, ultimately, assist in slowing or stopping a vehicle. Their constituents include binder, filler, abrasive, lubricant, and reinforcing fiber. Materials for brake pads must have excellent wear resistance, increased heat dissipation, a consistent coefficient of friction, low noise and vibration, durability, compatibility, minimal environmental impact, and cost-effectiveness. This paper aims to examine the various materials used in brake pad applications. They are composed of matrix, ceramic, and polymer composites, and are manufactured using various processes. In addition to mechanical and tribological testing, there are various methods for testing the mechanical and tribological properties of brake pads. Various instruments, such as SEM, TEM, AFM, and XRD, were surveyed in order to analyse the morphology and crystal structure of nanoscale brake pads. Various applications such as automobiles, railroads, and aerospace utilise brake pads. The study reveals that integrating nano-fillers into polymer composites significantly enhances the mechanical and tribological properties of automotive brake pads, offering a promising route toward more durable, efficient, and safer braking systems. Through this analysis, researchers will gain a deeper understanding of the materials used in brake pads and their adaptability for various applications.

Design And Development of Overheat Detector based on Arduino Mega On Four-Wheeled Vehicle Brake System

The aim of this research is to design a motor vehicle braking system trainer, so that it can be used as a test site for overheating detection devices in the brake system. The preparation of the design concept is divided into two designs, namely the designof the braking trainer andalso the design of the software application in the form of asimulation of an overheating detection system. The stages of sensor installation are carried outto the setting and installation of the heat detection sensoris in accordance with the systemplan that has been made, while the placement of the sensor is intended to detect heat from the front and rear braking systems. Programming is also carried out using Arduino software to program the input obtained from the sensor so that it can be displayed on the monitor display. Based on this, a prototype for detecting the brake system for four-wheeled motorized vehicles based on Arduino Mega can be produced. temperature. The four sensors used are able to provide data input and serve as a thermal reference for the conditions of the front and rear brakes, both from brake lining thermals and brake fluid thermals. This prototype also needs further developmentto be applied to real vehicles and to be able to determine the mosteffective location for the sensor so that it can provide the bestinformation to the driver.

RBS and ABS Coordinated Control Strategy Based on Explicit Model Predictive Control.

During the braking process of electric vehicles, both the regenerative braking system (RBS) and anti-lock braking system (ABS) modulate the hydraulic braking force, leading to control conflict that impacts the effectiveness and real-time capability of coordinated control. Aiming to enhance the coordinated control effectiveness of RBS and ABS within the electro-hydraulic composite braking system, this paper proposes a coordinated control strategy based on explicit model predictive control (eMPC-CCS). Initially, a comprehensive braking control framework is established, combining offline adaptive control law generation, online optimized control law application, and state compensation to effectively coordinate braking force through the electro-hydraulic system. During offline processing, eMPC generates a real-time-oriented state feedback control law based on real-world micro trip segments, improving the adaptiveness of the braking strategy across different driving conditions. In the online implementation, the developed three-dimensional eMPC control laws, corresponding to current driving conditions, are invoked, thereby enhancing the potential for real-time braking strategy implementation. Moreover, the state error compensator is integrated into eMPC-CCS, yielding a state gain matrix that optimizes the vehicle braking status and ensures robustness across diverse braking conditions. Lastly, simulation evaluation and hardware-in-the-loop (HIL) testing manifest that the proposed eMPC-CCS effectively coordinates the regenerative and hydraulic braking systems, outperforming other CCSs in terms of braking energy recovery and real-time capability.

Designing user interfaces for partially automated Vehicles: Effects of information and modality on trust and acceptance

Trust and perceived safety are pivotal in the acceptance of automated vehicles and can be enhanced by providing users with automation information on the (safe) operation of the vehicle. This study aims to identify how user interfaces (UI) can enhance drivers' trust and acceptance and reduce perceived risk in partially automated vehicles. Four interfaces were designed with different levels of complexity. These levels were achieved by combining automation information (surrounding information vs surrounding and manoeuvre information) and modality (visual vs visual and auditory). These interfaces were evaluated in a driving simulator in which a partially automated vehicle reacted to an event of a merging and braking vehicle in its front. The criticality of the events was manipulated by the factors merging gap (in meters) and deceleration (m/s2) of the vehicle in front. The reaction of the automation was either to brake or to change lanes. The results show that an optimal combination of automation information and modality enhances drivers' trust and acceptance. More specifically, the most advanced UI, which provided surrounding and manoeuvre information via the visual and auditory modalities, was associated with the highest trust and acceptance ranking and the lowest perceived risk. Manoeuvre information delivered through the auditory modality was particularly effective in enhancing trust and acceptance. The benefits of the UIs were consistent over events. However, in the most critical events, drivers did not feel entirely safe and did not trust the automation completely. This study suggests that the design of UIs for partially automated vehicles shall include automation information via visual and auditory modalities.

Saddle-node bifurcation control of macroscopic traffic flow model considering vehicle braking effect

Saddle-node bifurcation control of macroscopic traffic flow model considering vehicle braking effect

Topology Optimization of Three-Wheeler Electric Vehicle Brake Pedal

Abstract: In this project a new design is proposed on the 3 wheeler vehicle brake pedal for weight reduction and it is analyzed by software named FEA. In this an electrical three-wheel vehicle brake pedal is designed for weight optimization. Brake pedals are widely used in all automotive, which acts as a linkage between occupant and brake mechanism. Existing design seems to be overdesigned as per requirement, FEA used to apply cantilever load Optistruct solver used to perform topology optimization. The model of an existing brake pedal was generated using SolidWorksV5 modeling. Furthermore, the FEA analysis is done using ANSYS workbench 21. A three-wheel vehicle brake pedal was analyzed and optimized for weight reduction in this study. Optimize design will be tested using compression loading UTM machine.

Variable Universe Fuzzy-Proportional-Integral-Differential-Based Braking Force Control of Electro-Mechanical Brakes for Mine Underground Electric Trackless Rubber-Tired Vehicles.

Currently, the main solution for braking systems for underground electric trackless rubber-tired vehicles (UETRVs) is traditional hydraulic braking systems, which have the disadvantages of hydraulic pressure crawling, the risk of oil leakage and a high maintenance cost. An electro-mechanical-braking (EMB) system, as a type of novel brake-by-wire (BBW) system, can eliminate the above shortcomings and play a significant role in enhancing the intelligence level of the braking system in order to meet the motion control requirements of unmanned UETRVs. Among these requirements, the accurate control of clamping force is a key technology in controlling performance and the practical implementation of EMB systems. In order to achieve an adaptive clamping force control performance of an EMB system, an optimized fuzzy proportional-integral-differential (PID) controller is proposed, where the improved fuzzy algorithm is utilized to adaptively adjust the gain parameters of classic PID. In order to compensate for the deficiency of single-close-loop control and adjusting the brake gap automatically, a cascaded three-closed-loop control architecture with force/position switch technology is established, where a contact point detection method utilizing motor rotor angle displacement is proposed via experiments. The results of the simulation and experiments indicate that the clamping force response of the proposed multi-close-loop Variable Universe Fuzzy-PID (VUF-PID) controller is faster than the multi-closed-loop Fuzzy-PID and cascaded three-close-loop PID controllers. In addition, the chattering of braking force can be suppressed by 17%. This EMB system may rapidly and automatically finish the operation of the overall braking process, including gap elimination, clamping force tracking and gap recovery, which can obviously enhance the precision of the longitudinal motion control of UETRVs. It can thus serve as a BBW actuator of mine autonomous driving electric vehicles, especially in the stage of braking control.

A Multi-Source Braking Force Control Method for Electric Vehicles Considering Energy Economy

Advancements in electric vehicle technology have promoted the development trend of smart and low-carbon environmental protection. The design and optimization of electric vehicle braking systems faces multiple challenges, including the reasonable allocation and control of braking torque to improve energy economy and braking performance. In this paper, a multi-source braking force system and its control strategy are proposed with the aim of enhancing braking strength, safety, and energy economy during the braking process. Firstly, an ENMPC (explicit nonlinear model predictive control)-based braking force control strategy is proposed to replace the traditional ABS strategy in order to improve braking strength and safety while providing a foundation for the participation of the drive motor in ABS (anti-lock braking system) regulation. Secondly, a grey wolf algorithm is used to rationally allocate mechanical and electrical braking forces, with power consumption as the fitness function, to obtain the optimal allocation method and provide potential for EMB (electro–mechanical brake) optimization. Finally, simulation tests verify that the proposed method can improve braking strength, safety, and energy economy for different road conditions, and compared to other methods, it shows good performance.

Exploring the technology changes of new energy vehicles in China: Evolution and trends

In the sustainable development context, the automotive industry is shifting towards new energy vehicles (NEVs) to reduce carbon emissions. China leads in NEVs production and technology but faces challenges in innovation capacity due to increasing market competition and technological demands. Consequently, to explore the situation of technology innovation of China’s NEVs, this paper delves into the technology changes in this field with the invention granted patents from 2000 to 2021, employing the basic patent-metrics method and Latent Dirichlet Allocation (LDA) topic model for evolution analysis and vector autoregression (VAR) model for trend predictions. The research findings indicate that: (1) the patents pertaining to NEVs have experienced an average annual growth rate of 23%, with notable regional disparities in the spatial distribution of said patents. (2) Among the 8 key technology topics identified, battery equipment technology, brake control system technology, and supplying system technology emerge as the top three technology topics. (3) As NEVs progressively become more intelligent, there is an anticipated surge in interest regarding vehicle braking control technology over the ensuing five years.

Study of Road Accidents by Analysis of Effects of Driving Parameters On Stopping Distance Using the Experimental Design Method and Driving Simulator

This paper aims to investigate the key factors contributing to road accidents and their interplay. A significant proportion of road accidents can be attributed to non-compliance with speed regulations, in conjunction with vehicle braking components (such as grip, road conditions, ..), as well as weather conditions. We conducted a speed measurement campaign on peri-urban roads in multiple Algerian cities to examine driving behavior and adherence to speed limits. To comprehensively understand the impact of speed, anti-lock systems, weather conditions, grip, and their interactions on a vehicle's braking distance, we employed an experimental design methodology. This approach was complemented with driving simulator tests. We expanded the experimental design to cover a range of speed variations, from 70 to 130 km/h, enabling us to explore the influence of various driving parameters on braking distance. Through statistical analysis of influence coefficients from 16 driving simulator tests, we established a model that represents the relationship between "objective functions" and "influence factors." This mathematical model was validated using simulator results. Our study revealed that braking distance is primarily affected by speed and weather conditions. Notably, the analysis of the road speed measurement campaign demonstrated that 55% of road users fail to adhere to speed limits.

Co-simulation of giant magnetostrictive autonomous emergen-cy braking system for intelligent vehicles based on Prescan/Simulink /CarSim

The combination of giant magnetostrictive actuator (GMA) and autonomous Emergency brak-ing system (AEB) can achieve the lightweight body and fast braking of intelligent vehicle. In this essay, simulation scenarios are established in PreScan, AEB algorithm and GMA actuator are combined in simulink, vehicle dynamics model is provided by CarSim, and co-simulation analysis of CCRm, CCRs and CCRb working conditions is carried out. When AEB adopts TTC algorithm only, in the stationary vehicle scenario (CCRs) where the vehicle approaches the front at a certain speed, the relative distance between the two vehicles is between 1.86 and 2.85m until stopping. When the front vehicle is driving slowly and the ego vehicle is faster than the front (CCRm), the relative distance between the two vehicles until stopping is between 2.28 and 2.83m; When the ego vehicle is driving at the same speed as the front, the front vehicle suddenly braking (CCRb), and the relative distance between the two vehicles until stopping is 1.98~2.54m; The above scenarios can achieve effective collision avoidance, and the safety distance is within an effective and reasonable range. In the scenario of two vehicles following at the same speed and close distance, TTC algorithm fails. AEB adopts TTC algorithm and Seungwukmoonalgo-rithm for collaborative control to achieve effective collision avoidance. The co-simulation re-sults show that the AEB system can intervene in time and avoid collisions effectively under the above three working conditions, and the relative distance between the two vehicles until stop-ping is between 1.86 and 2.85m, which verifies the timeness and effectiveness of the braking of the giant magnetostrictive actuator when AEB algorithm intervenes, and significantly improves the active safety performance of the vehicle.

Fuzzy Logic-Based Energy Management System for Regenerative Braking of Electric Vehicles with Hybrid Energy Storage System

Electric vehicles (EVs), which are environmentally friendly, have been used to minimize the global warming caused by fossil fuels used in vehicles and increasing fuel prices due to the decrease in fossil resources. Considering that the energy used in EVs is obtained from fossil resources, it is also important to store and use energy efficiently in EVs. In this context, recovery from a regenerative braking system plays an important role in EV energy efficiency. This paper presents a fuzzy logic-based hybrid storage technique consisting of a supercapacitor (SC) and battery for efficient and safe storage of a regenerative braking system. First, the constraints of the battery to be used in the EV for fuzzy logic control are identified. Then, the fuzzy logic system is created and tested in the ADVISOR and Siemens Simcenter Flomaster programs in the New European Driving Cycle (NEDC) driving cycle. A SC was selected for primary storage to prevent the battery from being continuously charged from regenerative braking, thus reducing its lifetime. In cases where the vehicle consumes more energy than the average energy consumption, energy consumption from the battery is reduced by using the energy stored in the SC, and the SC energy is discharged, making preparations for the energy that will come from the next regenerative braking. Thus, the high current values transferred to the battery during regenerative braking are effectively limited by the SC. In this study, the current values on the battery in the EV with a hybrid storage system decreased by 29.1% in the ADVISOR program and 28.7% in the Simcenter Flomaster program. In addition, the battery generated 46.84% less heat in the hybrid storage system. Thus, the heating and capacity losses caused by this current on the battery were minimized. The presented method provides more efficient energy management for EVs and plays an important role in maintaining battery health.

Wheel slip ratio control considering time delay characteristic for electro-mechanical braking system

Electro-mechanical braking (EMB) system is a novel brake-by-wire (BBW) system which is the development direction of the electrification and intelligent vehicle braking system. However, as the EMB contains a mechanical transmission mechanism, there is an inevitable time delay in the braking process, which will cause problems of the wheel slip ratio control. To address this issue, this paper proposes a novel wheel slip ratio control system based on a hierarchical control framework, comprising a slip ratio control layer and a clamping force control layer. Firstly, an EMB actuator model considering the stiffness and damping characteristics of the ball screw is constructed and the correspondence between the model and the EMB object is verified. Subsequently, a model-based compensate active disturbance rejection control (MCADRC) algorithm based on the EMB actuator model is designed to compensate the uncertainty of the EMB and improve the tracking accuracy of the clamping force. Furthermore, the influence of time delay on slip ratio control is analyzed and a slip ratio controller considering time delay characteristic is developed to improve the slip ratio control quality. Finally, the HiL test results show that the proposed controller can effectively reduce the jitter of slip ratio by 1.2% and improve the response speed of slip ratio control 7.8%, which prove the real-time and effectiveness of the controller.

Coordinated control strategy of electro-mechanical composite braking for four-wheel drive electric vehicles

Regenerative braking energy recovery technology is critical for electric vehicles, which can significantly improve vehicle mileage. In addition, the regenerative braking system is often used with the friction braking system to provide vehicle braking force. This paper presents an electro-mechanical compound brake control strategy for four-wheel drive electric vehicles equipped with an electro-mechanical braking system. Firstly, the configuration of a four-wheel drive electric vehicle with an electro-mechanical braking system is introduced. The mathematical models of the driving motor, battery, and electro-mechanical braking are established. Secondly, a new control method is introduced by optimizing torque allocation based on maximum energy recovery with considering battery limits and a cooperative control method of the electro-mechanical composite braking system. It can be ensured that the braking energy recovery capability and braking comfort. Finally, simulation results indicate that the introduced control strategy can increase the braking recovered energy by 3% and 4%, respectively, under NEDC and WLTC conditions, compared to the ideal braking force distribution strategy. The introduced control method can effectively decrease the braking impact and ensure braking comfort in the coordination control section.

Regenerative Braking in Electric Vehicles using BLDC motor with Modified Torque and Adaptive-Neuro-Fuzzy-Control

Regenerative Braking in Electric Vehicles using BLDC motor with Modified Torque and Adaptive-Neuro-Fuzzy-Control

A Study on the Vehicle Antilock System Based on Adaptive Neural Network Sliding Mode Control

Vehicle antilock systems play a very important role in the stability and reliability during vehicle braking. Due to the complexity of the braking process, antilock braking system (ABS) usually face the problems such as nonlinearity, time-varying, and uncertain parameter modeling. Thus, aiming at the parameter model uncertainty problem of ABS, an adaptive neural network sliding mode controller (ADRBF-SMC) is designed in this paper. On this basis, establishing the quarter-vehicle model and the seven-degree-of-freedom vehicle model, and treating the difference between the two models as a kind of disturbance, carrying out vehicle braking performance simulation experiments to analyze the variation of braking performance parameters such as vehicle and wheel speeds, slip ratio, braking distance, braking torque, under the three cases of adaptive neural network sliding mode controller, traditional sliding mode controller, and no control. Simulation results show that the adaptive neural network sliding mode controller (ADRBF-SMC) proposed in this paper can play an effective control role in both vehicle dynamics models. In addition, the control method proposed in this paper has stronger anti-interference capability and higher robustness compared with the sliding mode controller (SMC).

Design, Simulation, Building, and Testing of a Microcontroller-Based Automatic Drowsiness Detection, Vehicle Braking, and Alert System

Aims: The premier practical aspect of this research drive is to propose, build, simulate, and evaluate an Arduino-built automatic vehicle driver drowsiness detection and alert system.
 Study Design: The pivotal role of a reliable braking system in vehicular safety cannot be overstated, particularly considering the escalating frequency of traffic accidents, notably prevalent in Indonesia where human factors take center stage as the primary accident catalyst. An insightful poll underscores physical fatigue or drowsiness while driving as the foremost concern. Acknowledging the nuanced disparities between conventional air systems and electric systems, this research strategically directs its attention toward crafting a new system characteristic that mirrors the efficiency of the existing systems.
 Place and Period of Study: The research action engaged by the authors in a bunch of two students under the command of a professor as a part of one of his course capstone projects for the Bachelor of Science in Electrical and Electronic Engineering degree at the American International University Bangladesh (AIUB), Dhaka, Bangladesh. The authors performed their investigative tasks at AIUB from June 2023 to January 2024.
 Methodology: Recognizing the imperative to fortify vehicular safety, the integration of artificial intelligence emerges as a vital solution to assist drivers in ensuring the safety of individuals both within and outside the vehicle. This focused study, tailored specifically for Electric Vehicles (EVs), centers on the innovative application of object and distance identification methodologies to provide a comprehensive braking action indicator. Leveraging a minicomputer and a sophisticated neural network approach, images captured by a stereo camera undergo meticulous machine learning processes. This facilitates the precise categorization and measurement of distances between objects, with subsequent priority decisions determining the optimal degree of braking action. Employing an intelligent methodology, specifically fuzzy logic, the study demonstrates a successful outcome by constructing a curve while concurrently enhancing the dynamics of the pre-existing system. Moreover, a finely tuned Pulse Width Modulation (PWM) signal for braking, lasting 10 milliseconds, is intricately devised based on the study's discerning results.
 Results: The system is simulated in Proteus and tested in real time to check its functionality. The outcomes of the test showed that the system can produce the appropriate signals based on the detection of any kind of driver’s drowsiness and can stop the car.
 Conclusion: This comprehensive approach ensures the seamless replacement of components while concurrently elevating the overall performance of the braking system

Uncertainty modeling of connected and automated vehicle penetration rate under mixed traffic environment

Accurate knowledge of the penetration rate of connected and automated vehicles (CAVs) is crucial for effective control applications during the transition from mixed traffic to full CAV deployment. Previous studies have focused on characterizing or controlling mixed traffic with a fixed CAV penetration rate. However, in reality, the on-road penetration rate of CAVs varies, even if their market share remains constant. This study presents a mathematical model that estimates the CAV penetration rate while considering this variability. We propose an uncertainty-based penetration rate estimation model to assess the variability of CAV numbers on the road. This model utilizes a probabilistic modified random walk approach to estimate the distribution. To enhance the realism of mixed traffic flow, we incorporate realistic vehicle braking and starting behaviors using an improved cellular automata-based mixed traffic flow model. Simulation results demonstrate that our uncertainty-based penetration rate estimation model accurately describes CAV numbers and estimates the variability in mixed traffic flow, specifically within opened circular boundaries and on-ramps. Moreover, we demonstrate the practical applicability of the uncertainty models in real-world situations, showcasing their potential to enhance system optimizations.

Research on the Multi-Mode Composite Braking Control Strategy of Electric Wheel-Drive Multi-Axle Heavy-Duty Vehicles

Electric wheel-drive multi-axle heavy-duty vehicles have the characteristics of strong maneuverability and good passability, thereby they are widely used in heavy equipment transportation. However, current research on the composite braking of multi-axle heavy-duty vehicles is rare, which is not conducive to improving braking performance and braking energy utilization efficiency. This work proposes a multi-mode composite braking control strategy for the five-axle distributed electric wheel-drive heavy-duty vehicle. Firstly, given the differences in braking dynamics between two-axle vehicles and multi-axle vehicles, the brake dynamics characteristics of multi-axle vehicles are analyzed, and the vehicle dynamics model of multi-axle vehicles is constructed. Next, a multi-mode composite braking control strategy including a fully electric braking state and hybrid electro–hydraulic braking state is proposed in order to improve the braking energy recovery and braking stability. Finally, a hardware-in-the-loop simulation system is established, and the single-braking conditions and China heavy-duty commercial vehicle test cycle-heavy truck (abbreviated as CHTC-HT) are conducted to verify the performance of the braking control strategy. The results indicate that the recaptured braking energy and braking stability are significantly increased by applying the control strategy proposed in this work.