Electric Braking System Research Articles

Research on Direct-Drive Brake Actuator and Its Control Method for Electric Vehicles

<div>With the rapid development of electric vehicles, the need for improved reliability and safety performance of electric vehicle braking systems has become paramount. In response to this demand, a novel direct-drive brake-by-wire actuator based on linear motors was designed to address these challenges. This article presents the structure and principles of the proposed braking actuator. Leveraging the traditional electromechanical brake systems as a foundation, the prototype was modified and fabricated. Additionally, the control and drive system for the braking actuator was established using the TMS320F28335 digital signal processor. Moreover, the current-position dual closed-loop control algorithm was devised to regulate the braking force accurately. Experimental results demonstrate that the direct-drive brake-by-wire actuator exhibits rapid responsiveness and precise braking force modulation, showcasing promising prospects in the field of electric vehicle braking.</div>

Thermal Fault-Tolerant Asymmetric Dual-Winding Motors in Integrated Electric Braking System for Autonomous Vehicles

A conventional dual-winding (DW) motor has two internal windings consisting of a master part and a slave part, each connected to a different electronic control unit (ECU) to realize a redundant system. However, existing DW motors have a problem related to heat generation in both the healthy mode and the faulty mode of the motor operation. In the healthy mode, unexpected overloads can cause both windings to burn out simultaneously due to equal heat distribution. If the current sensor fails to measure correctly, the motor may exceed the designed current density of 4.7 [Arms/mm2] under air-cooling conditions, further increasing burnout risk. External factors such as excessive load cycles or extreme heat conditions can further exacerbate this issue. In the faulty mode, the motor requires double the current to generate maximum torque, leading to rapid temperature increases and a high risk of overheating. To address these challenges, this paper proposes the design of a thermal fault-tolerant asymmetric dual-winding (ADW) motor, which improves heat management in both healthy and faulty modes for autonomous vehicles. A lumped-parameter thermal network (LPTN) with a piecewise stator-housing model (PSMs) was employed to evaluate the coil temperature during faulty operation. An optimal design approach, incorporating kriging modeling, Design of Experiments (DOE), and a genetic algorithm (GA), was also utilized. The results confirm that the proposed ADW motor design effectively reduces the risk of simultaneous burnout in the healthy mode and overheating in the faulty mode, offering a robust solution for autonomous vehicle applications.

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.

A novel linear displacement sensor based on double-threshold decoding algorithm

Purpose Most of the linear encoders are based on optics. The accuracy and reliability of these encoders are greatly reduced in polluted and noisy environments. Moreover, these encoders have a complex structure and large sensor volume and are thus not suited to small application scenarios and do not have universality. This paper aims to present a new absolute magnetic linear encoder, which has a simple structure, small size and wide application range. Design/methodology/approach The effect of swing error is analyzed for the sensor structural arrangement. A double-threshold interval algorithm is then proposed to synthesize multiple interval electrical angles into absolute angles and convert them into actual displacement distances. Findings The final linear encoder measurement range is 15.57 mm, and the resolution reaches ± 2 µm. The effectiveness of the algorithm is demonstrated experimentally. Originality/value The linear encoder has good robustness, and high measurement accuracy, which is suitable for industrial production. The linear encoder has been mass-produced and used in an electric power-assisted braking system.

Practical hybrid model predictive control for electric pneumatic braking system with on-off solenoid valves

Accurate and rapid braking pressure regulation in electric pneumatic braking systems (EPBS) is vital to vehicle safety. Due to the switching behaviors of the on-off solenoid valves, the operation of the EPBS shows a hybrid nature with both continuous variables and discrete events, which raises the hybrid control problem. One of the possible solutions is to employ the hybrid model predictive controller with the mixed logical dynamical (MLD) model based on the linear approximation of the system dynamics. However, the nonlinearity and complexity of the EPBS make the MLD model obtained by linearizing the system equations directly require high storage and computing capacity. To address these issues, this article presents a practical hybrid model predictive controller based on the system dynamics simplified expressions considering the EPBS pressure variations caused by on-off solenoid valve states at the current sampling time and the last sampling time. The relationship between the pressure variations and the on-off solenoid valve states is first studied by the system mathematical model, followed by applying the mixed logic dynamical modeling approach to establish the hybrid model of the pressure continuous dynamics with discrete features of on-off solenoid valves. Based on these, a hybrid model predictive controller is formulated to solve the EPBS pressure control problem. The simulations and bench experiments are carried out to verify the controller. Besides, an existing model predictive control (MPC) controller is compared with the proposed controller. All the results demonstrate the effectiveness of the hybrid model predictive controller.

Analysis of Regenerative Braking Strategy for Electric Vehicles Based on Braking Intention

Improved energy recovery during braking is the goal of the distributed drive electric car regenerative braking system. A regenerative braking control strategy for electric vehicles based on braking intention is suggested in order to address various drivers’ braking intents and road adhesion circumstances. An electric vehicle regenerative braking model for distributed drives is created using the MATLAB/Simulink tools. On the basis of FTP-75 operating conditions and open road operating conditions, the efficacy and economy of the control strategy in this study are verified. According to the various road adhesion coefficients, the simulation results demonstrate that the control method in this work can appropriately divide the braking torque of the wheels on both sides of the coaxial wheel. The technique can successfully enhance the regenerative braking recovery of distributed drive electric vehicles as compared to the often employed regenerative braking control method.

System-Level Fault Tolerance Design Process of Dual-Winding Motors for Overheat in Integrated Electric Braking System of Autonomous Vehicle

System-Level Fault Tolerance Design Process of Dual-Winding Motors for Overheat in Integrated Electric Braking System of Autonomous Vehicle

Single Winding Type Determination of Dual Winding Three-Phase Motor Considering Overheat Problem in Integrated Electric Braking System of Autonomous Vehicles

This article introduces the single winding type selection process for a dual winding (DW) three-phase motor. Unlike the conventional DW three-phase motor design, the proposed single winding type selection process simultaneously considers the mechanical length determined by the single winding type and the motor overheating problem caused by faulty operation for each single winding type. The proposed single winding type selection process consists of four steps. First, all single-winding combinations, the point symmetry dual winding (PSDW), line symmetry dual winding (LSDW), and cross symmetry dual winding (CSDW) for DW three-phase motor are investigated. Second, according to the selected single winding types, the busbar height (BH) required for dual division is designed and compared. Third, the stack length (SL) of motor in the PSDW, the LSDW, and the CSDW obtained through overheat compensation design to avoid the overheating problem in the faulty mode are investigated and compared. In the overheat compensation design, the thermal map utilizing measurement data from the previously developed motor for an integrated electric brake (IEB) system, a radial basis function, an overheat compensation coefficient, and the IEB brake load condition are utilized. Finally, the single winding type of DW three-phase motor is selected as the CSDW by comparing the total motor size. As a result of the DW three-phase motor design, the proposed CSDW can reduce the motor size by 12.0% and 4.2%, respectively, compared with the PSDW and LSDW.

Cascaded fast terminal sliding mode control for UAV electric braking system

The anti-skid braking system of unmanned aerial vehicle has the characteristics of high-order nonlinearity, strong coupling, and time-varying parameters, which will seriously affect the taxiing performance of the unmanned aerial vehicle. This paper proposed a cascaded fast terminal sliding mode control method for unmanned aerial vehicle electric braking system, which can accurately track the optimal slip rate and ensure the stability of the braking system. First, the electric braking system model of unmanned aerial vehicle is established, which gets rid of the reliance on the ground binding coefficient fitting formulas. Second, combined with the concept of the inversion control, the cascaded fast terminal sliding mode controller is designed to achieve the accurate tracking of the slip rate and the reference braking force. The stability of the controller is proved by the Lyapunov stability theory. Finally, the effectiveness of the proposed control strategy is verified by simulation results.

The application of machine learning-based energy management strategy in a multi-mode plug-in hybrid electric vehicle, part II: Deep deterministic policy gradient algorithm design for electric mode

Machine learning (ML)-based methods have attracted great attention in the multi-objective optimization problems, which is the key challenge in the energy management strategy (EMS) of the multi-power hybrid system. Our recently published research in this journal verified the effectiveness and feasibility of a Deep Deterministic Policy Gradient (DDPG)-based EMS in the charge-sustaining (CS) stage of a multi-mode plug-in hybrid vehicle (PHEV). However, the application of ML-based-EMS in the charge-depletion (CD) stage and the regenerative braking mode of PHEV are still missing. This study proposes a discrete-continuous hybrid actions-based hierarchical EMS to optimally distribute the dual-motor driving force in battery electric driving and regenerative braking. In the upper layer of EMS, DDPG is trained to learn the torque distribution principles of dual-motor operation to achieve better energy efficiency without losing dynamic performance. Meanwhile, the total recoverable braking torque is also determined by the upper layer EMS considering the braking demand, mechanical and electrical braking system conditions, vehicle safety, and the provisions of law. In the lower level of EMS, the driving mode is determined under the guidance of energy consumption optimization. The verified results show that the proposed EMS outperforms other deep reinforcement learning (DRL)-based hierarchical and non-hierarchical EMSs.

Research Results of the Operating Efficiency of the Electric Train Brake Air System

This document discusses the identified problems of the electric train braking system operation in the autumn-winter period. The results of observations are presented. The results of calculating the amount of condensate released during the 1 hour of compressor operation at a certain ambient air temperature are presented. Engineering calculations were performed. A technical solution is proposed to improve the efficiency of pneumatic equipment by installing heated purge valves on the main instead of conventional valves to prevent freezing.

Extremum Seeking Based Braking Torque Distribution for Electric Vehicles’ Hybrid Anti-lock Braking System

To ensure braking stability and maximize energy recovery, electric vehicles combine two braking modes, friction and regenerative braking. This paper proposes a novel braking torque distribution (BTD) algorithm between frictional/regenerative brake systems. Security and regeneration energy are the two main issues in braking torque allocation design. Based on the extremum search (ES) technique, the objectives and constraints of the hybrid ABS brake torque distribution are well handled. The offered BTD can maximize the regeneration energy and improve the battery's state of charge (SOC) by optimizing the electric braking system efficiency, while respecting the braking actuators constraints. The validity and effectiveness of the proposed BTD has been demonstrated by simulation in the MATLAB/Simulink environment.

Research on Electric Control Brake System Based on Railway Vehicle

The trackless train may be mixed with other vehicles in the city. Therefore, developing a vehicle braking system with high reliability, fast response, and powerful braking force is necessary. In this research, the chip module control is added to each control part to realize rapid electric signal collection and response, and then the pneumatic control of each actuator is used to realize rapid and flexible electric braking control of the vehicle. The braking system has been tested on the test bench in the factory, and its response speed and braking effect have increased by 10% compared with the traditional bus braking system. When the performance test is carried out on the real vehicle, the braking effect is also improved by nearly 10%. Through the design of the braking system, the braking system of domestic trackless trains can be perfectly solved.

Fault-Tolerant Design Process of Spoke-Type IPM Motor Considering Irreversible Demagnetization of PM in Integrated Electric Brake System

In this article, a novel fault-tolerant design process of the spoke-type interior permanent magnet (IPM) motor is proposed to prevent the increase of cogging torque and torque ripple due to partial demagnetization fault and to minimize the motor size. First, unlike other research papers on the irreversible partial irreversible demagnetization of permanent magnet (PM), in this article, the effect of partial irreversible demagnetization faults on the cogging torque and torque ripple for the fault cases is investigated. Then, a novel fault-tolerant design process is proposed to solve the problem of partial demagnetization faults that increase the cogging torque and torque ripple. In the novel fault-tolerant design process, first, to solve the problem of increasing cogging torque and torque ripple due to the partial demagnetization fault, a process for implementing a uniform irreversible PM demagnetization is proposed. Second, to compensate for the motor torque constant decreased by degraded PM, which is caused by irreversible demagnetization of PM, a torque compensation coefficient is also proposed. The proposed fault-tolerant design process is applied to the motor design for the integrated electric brake (IEB) system that requires high torque density. In the optimal design process, Latin hypercube sampling (LHS), a kriging method, and a genetic algorithm are utilized. To verify the effectiveness of the proposed fault-tolerant design process, it is compared with the conventional optimized design results in which irreversible demagnetization of PM is not allowed at all to prevent the partial demagnetization faults. As a result of comparing the optimal design results, the proposed optimization model utilizing the fault-tolerant design process can significantly reduce the motor volume by 12.9%, compared to the conventional optimization model.

Analysis of Heat Generation on Unipolar Axial Eddy Current Brake Disc and Its Effect on Braking Performance

The braking system is one of the most important components of a vehicle. In general, the brakes will generate heat due to the braking process. The heat generated must be released into the environment to maintain braking performance at optimal conditions. In extreme conditions, braking will fail. The braking system can be developed as a braking support system is a non-contact braking system. One form of the non-contact braking system is the eddy current brake (ECB). ECB is an electric braking system with the principle of eddy current. In the ECB, overheating will result in decreased performance. The approach that can be taken to determine braking performance during heat generation is the modeling process using FEM. This study uses FEM to analyze the heat generated during braking. In addition to using FEM, research was carried out using experiments as a comparison. Analysis of heat generation in braking is needed to determine whether braking with ECB can be a backup and its potential as a substitute for friction brakes. The results show that the ECB heat generation event that affects the temperature rise reduces the braking torque performance. Research indicates that when overheating occurs, braking performance will decrease by up to 10% when the disk surface temperature rises more than 20 °C. It shows the importance of managing heat that occurs in the ECB.

Brake Power and Load Analysis of Electromagnetic Braking System

This study aimed to develop a model and prototype of a more sustainable electromagnetic braking system which can cope with the future demands of the automobile industry due to the increasing number of e-vehicles. The braking mechanism is been used in almost every type of mechanical vehicle and it utilizes crude oil as fuel which when burnt causes air pollution. To save the environment from air pollution e-vehicles have been introduced to the market. Electromagnetic brakes are been used in e-vehicles as these vehicles incorporate huge electric systems which may be damaged or ignited through any leakage from the hydraulic brake. In this study, engineering knowledge has been implemented to design and fabricate a model of the electromagnetic braking system (EMS) which works on the principle of eddy current. A 3D model of EMS was developed. Further, static displacement and brake power analyses were performed to check the strength of EMS and the performance of the prototype developed, respectively. At an average speed of 31 RPM, brake power produced was 3.9 W. Static structural analysis on a frame made of grey cast iron showed that there was negligible deformation for load up to 200 N. Electromagnetic braking system based on this analysis and model could meet the market needs for conveyor belt application as well. The observations from this study might be useful in the development of a more efficient electrical vehicle braking system with varying demands of momentum.

Surrogate role of machine learning in motor-drive optimization for more-electric aircraft applications

Motor drives form an essential part of the electric compressors, pumps, braking and actuation systems in the More-Electric Aircraft (MEA). In this paper, the application of Machine Learning (ML) in motor-drive design and optimization process is investigated. The general idea of using ML is to train surrogate models for the optimization. This training process is based on sample data collected from detailed simulation or experiment of motor drives. However, the Surrogate Role (SR) of ML may vary for different applications. This paper first introduces the principles of ML and then proposes two SRs (direct mapping approach and correction approach) of the ML in a motor-drive optimization process. Two different cases are given for the method comparison and validation of ML SRs. The first case is using the sample data from experiments to train the ML surrogate models. For the second case, the joint-simulation data is utilized for a multi-objective motor-drive optimization problem. It is found that both surrogate roles of ML can provide a good mapping model for the cases and in the second case, three feasible design schemes of ML are proposed and validated for the two SRs. Regarding the time consumption in optimizaiton, the proposed ML models can give one motor-drive design point up to 0.044 s while it takes more than 1.5 mins for the used simulation-based models.

Trajectory Tracking within a Hierarchical Primitive-Based Learning Approach

A hierarchical learning control framework (HLF) has been validated on two affordable control laboratories: an active temperature control system (ATCS) and an electrical rheostatic braking system (EBS). The proposed HLF is data-driven and model-free, while being applicable on general control tracking tasks which are omnipresent. At the lowermost level, L1, virtual state-feedback control is learned from input–output data, using a recently proposed virtual state-feedback reference tuning (VSFRT) principle. L1 ensures a linear reference model tracking (or matching) and thus, indirect closed-loop control system (CLCS) linearization. On top of L1, an experiment-driven model-free iterative learning control (EDMFILC) is then applied for learning reference input–controlled outputs pairs, coined as primitives. The primitives’ signals at the L2 level encode the CLCS dynamics, which are not explicitly used in the learning phase. Data reusability is applied to derive monotonic and safely guaranteed learning convergence. The learning primitives in the L2 level are finally used in the uppermost and final L3 level, where a decomposition/recomposition operation enables prediction of the optimal reference input assuring optimal tracking of a previously unseen trajectory, without relearning by repetitions, as it was in level L2. Hence, the HLF enables control systems to generalize their tracking behavior to new scenarios by extrapolating their current knowledge base. The proposed HLF framework endows the CLCSs with learning, memorization and generalization features which are specific to intelligent organisms. This may be considered as an advancement towards intelligent, generalizable and adaptive control systems.

Application of regenerative braking on electric rolling stock

This article provides an overview of ways to improve the efficiency of regenerative braking on electric rolling stock. The advantages and disadvantages of this type of braking are considered. The requirements for the electric braking system are listed. Some analysis of the work of JSC Russian Railways on the implementation of the Energy Saving and Energy Efficiency Program is presented.

Fault Diagnosis in Regenerative Braking System of Hybrid Electric Vehicles by Using Semigroup of Finite-State Deterministic Fully Intuitionistic Fuzzy Automata.

Regenerative braking is one of the most promising and ecologically friendly solutions for improving energy efficiency and vehicle stability in electric and hybrid electric cars. This research describes a data-driven method for detecting and diagnosing issues in hybrid electric vehicle regenerative braking systems. Early fault identification can help enhance system performance and health. This study is centered on the construction of an inference system for fault diagnosis in a generalized fuzzy environment. For such an inference system, finite-state deterministic fully intuitionistic fuzzy automata (FDFIFA) are established. Semigroup of FDFIFA and its algebraic properties including substructures and structure-preserving maps are investigated. The inference system uses FDFIFA semigroups as variables, and FDFIFA semigroup homomorphisms are employed to illustrate the relationship between variables. The newly established model is then applied to diagnose the possible fault and their nature in the regenerative braking systems of hybrid electric vehicles by modeling the performance of superchargers and air coolers. The method may be used to evaluate faults in a wide range of systems, including autos and aerospace systems.