Electric Braking Research Articles

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.

Analysis of the influence of brake pipe pressure gradient on heavy haul combined train and optimisation of operation strategy

In this paper, based on the theory of gas flow, the action logic of the control valve, and the principle of multi-rigid-body dynamics, a simulation model of the 20,000 t heavy haul combined train is established to investigate the influence of brake pipe pressure gradient on the braking performance, longitudinal dynamics, braking and release characteristics of trains. Utilizing real railway line conditions, locomotive and vehicle parameters, and train operation monitoring equipment (LKJ) record data of Shuozhou-Huanghua Railway, the cyclic braking condition of the train on the long and steep downhill is simulated, and the control strategy of reducing the coupler force during the cyclic braking is proposed. The results indicate that a larger brake pipe pressure gradient before braking leads to weaker braking capability, and higher coupler forces during braking, but smaller coupler forces during release. As the brake pipe pressure gradient before braking increases, braking synchronicity decreases, while release synchronicity slightly improves. By adjusting the locomotive’s electric braking force during the cyclic braking, it is possible to appropriately increase the brake pipe pressure gradient before braking, effectively reducing coupler forces during release and reducing the number of braking cycles, thus lowering the operational difficulty of the train.

INVESTIGATION OF A DC TRACTION MOTOR IN THE SERIES EXCITATION GENERATOR MODE

The article analyses electrical braking systems with direct current motors. It develops an experimental setup for studying the self-excitation of a traction motor. The research demonstrated the feasibility of implementing electrical braking systems with motors operating in the series excitation generator mode. Keywords: electric motor, excitation windings, electrical braking, series excitation generator, DC-DC converter.

Thermostatic interaction analysis of electric wheel brakes

Abstract The electric wheel needs to integrate the hub motor and brake in the wheel hub, so the integration scheme and structural parameters of each component in the electric wheel determine whether the electric wheel can coordinate the operation and the quality of the running performance. In this paper, the thermosolid interaction analysis of the brakes of electric wheels is carried out through ABUQUS software, so as to judge that the performance of the brakes meets the requirements, which will play a decisive role in the safety performance and life limit of the automobile, so as to effectively improve the working stability of electric wheels.

Design and Research of an Elevator Electric Brake Release Device with a Safety Detection Function

Abstract In response to the issues and defects associated with the current method of using electrical devices to open brakes for emergency rescue, a new elevator electric brake release device applicable to any elevator employing an electromechanical braking system has been designed. This device mainly consists of a car data collection unit and a brake release control unit, utilizing the STM32F103C8T6 microcontroller as the control core for elevator emergency rescue operations. The car data collection unit is responsible for gathering and transmitting data on the car’s operational state and position, while the brake release control unit evaluates the conditions for emergency rescue operations and performs real-time analysis, storage, and display of the collected data. It also decides whether to output a brake release signal. Experimental results show that when conditions for rescue operations are met, the electric brake release device can ensure the safe operation of the elevator; when abnormalities are detected, it outputs a signal to stop operations, alongside fault alarms and status displays.

Electric vehicle charger energy management by considering several sources and equalizing battery charging

This article proposes a novel energy management structure for electric vehicles, consisting of a supercapacitor and two types of batteries, to improve efficiency and navigable distance. The key features of a suitable energy storage system include high power and energy density, low cost and weight, minimal maintenance, and long life. Although batteries are the primary power storage source in electric vehicles, they have power limitations. Therefore, a high-power-density supercapacitor is added to the battery to create a hybrid energy storage system, reducing stress on the battery and increasing its lifespan. The article presents a new structure for hybrid storage systems based on the existence of a main battery, a replaceable battery, a supercapacitor, and a DC-DC converter. An active charge equalizer circuit for the main battery in standby mode and its control system are also introduced. A control method for dividing power between different sources under different conditions is presented, and the design parameters of the energy storage system are optimized using a genetic algorithm to minimize mass and losses. The performance of the proposed system is evaluated through simulation in MATLAB software and tested in a small-scale laboratory sample. The article also provides a detailed analysis of different working modes, including electric motor acceleration, low-speed mode, high-speed mode, and electric regenerative braking mode. Additionally, a control method for energy management between batteries and supercapacitors is introduced, aiming to increase efficiency. The results show that the proposed hybrid energy storage system can effectively improve the efficiency of electric vehicles.

Optimal allocation method of electric/air braking force of high-speed train considering axle load transfer

Reasonable distribution of braking force is a factor for a smooth, safe, and comfortable braking of trains. A dynamic optimal allocation strategy of electric-air braking force is proposed in this paper to solve the problem of the lack of consideration of adhesion difference of train wheelsets in the existing high-speed train electric-air braking force optimal allocation strategies. In this method, the braking strategy gives priority to the use of electric braking force. The force model of a single train in the braking process is analyzed to calculate the change of adhesion between the wheel and rail of each wheelset after axle load transfer, and then the adhesion of the train is estimated in real time. Next, with the goal of maximizing the total adhesion utilization ratio of trailer/motor vehicles, a linear programming distribution function is constructed. The proportional coefficient of adhesion utilization ratio of each train and the application upper limit of braking force in the function is updated according to the change time point of wheelset adhesion. Finally, the braking force is dynamically allocated. The simulation results of Matlab/Simulink show that the proposed algorithm not only uses the different adhesion limits of each trailer to reduce the total amount of braking force undertaken by the motor vehicle, but also considers the adhesion difference of each wheelset. The strategy can effectively reduce the risk and time of motor vehicles during the braking process and improve the stability of the train braking.

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 Supercapacitor Model Parameters Identification Method Using Metaheuristic Gradient-Based Optimization Algorithms

This paper addresses the critical role of supercapacitors as energy storage systems with a specific focus on their modeling and identification. The lack of a standardized and efficient method for identifying supercapacitor parameters has a definite effect on widespread adoption of supercapacitors, especially in high-power density applications like electric vehicle regenerative braking. The study focuses on parameterizing the Zubieta model for supercapacitors, which involves identifying seven parameters using a hybrid metaheuristic gradient-based optimization (MGBO) approach. The effectiveness of the MGBO method is compared to the existing particle swarm optimization (PSO) and to the following algorithms proposed and developed in this work: ‘modified MGBO’ (M-MGBO) and two PSO variations—one combining PSO and M-MGBO and the other incorporating a local escaping operator (LCEO) with PSO. Metaheuristic- and gradient-based algorithms are both affected by problems associated with locally optimal results and with issues related to enforcing constraints/boundaries on solution values. This work develops the above-mentioned innovations to the MGBO and PSO algorithms for addressing such issues. Rigorous experimentation considering various types of input excitation provides results indicating that hybrid PSO-MGBO and PSO-LCEO outperform traditional PSO, showing improvements of 51% and 94%, respectively, while remaining comparable to M-MGBO. These hybrid approaches effectively estimate Zubieta model parameters. The findings highlight the potential of hybrid optimization strategies in enhancing precision and effectiveness in supercapacitor model parameterization.

Matlab-Simulink Simulation Model of Electric Multiple Unit (EMU) Series ŽS 413/417 in Traction and Electric Braking Mode

Matlab-Simulink Simulation Model of Electric Multiple Unit (EMU) Series ŽS 413/417 in Traction and Electric Braking Mode

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.

Research on uninterruptable power supply technology for auxiliary winding of electric locomotive while passing the neutral section

PurposeAuxiliary power system is an indispensable part of the train; the auxiliary systems of both electric locomotives and EMUs mainly are powered by one of the two ways, which are either from auxiliary windings of traction transformers or from DC-link voltage of traction converters. Powered by DC-link voltage of traction converters, the auxiliary systems were maintained of uninterruptable power supply with energy from electric braking. Meanwhile, powered by traction transformers, the auxiliary systems were always out of power while passing the neutral section of power supply grid and control system is powered by battery at this time.Design/methodology/approachUninterrupted power supply of auxiliary power system powered by auxiliary winding of traction transformer was studied. Failure reasons why previous solutions cannot be realized are analyzed. An uninterruptable power supply scheme for the auxiliary systems powered by auxiliary windings of traction transformers is proposed in this paper. The validity of the proposed scheme is verified by simulation and experimental results and on-site operation of an upgraded HXD3C type locomotive. This scheme is attractive for upgrading practical locomotives with the auxiliary systems powered by auxiliary windings of traction transformers.FindingsThis scheme regenerates braking power supplied to auxiliary windings of traction transformers while a locomotive runs in the neutral section of the power supply grid. Control objectives of uninterrupted power supply technology are proposed, which are no overvoltage, no overcurrent and uninterrupted power supply.Originality/valueThe control strategies of the scheme ensure both overvoltage free and inrush current free when a locomotive enters or leaves the neutral section. Furthermore, this scheme is cost low by employing updated control strategy of software and add both the two current sensors and two connection wires of hardware.

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.

Modelling of Energy Recovery in Electric Vehicles for Various Braking Scenarios on Changing Road Surfaces

Due to great braking energy losses caused by traffic jams, changing velocity, and frequent start-stop modes, recovery of braking energy has become a top priority. In this paper, the universal braking system is described that operates at various driving scenarios including smooth braking and emergency antilock braking on different road surfaces and integrates both the friction and the electric braking strengths. The vehicle model reflects multiple factors, such as air resistance, road slope, and variable friction. The refined tire model recognizes changing road surfaces at different velocities. In the motor and battery model, the state of charge and electric current/voltage restrictions of the hybrid energy storage are taken into account. Braking torque generated by the Sugeno’s fuzzy logic controller established in the Simulink environment is allocated between friction and electric brakes. Often cited torque oscillations at low vehicle velocities have found their description, being reduced and evenly distributed throughout the braking process with the help of torque stabilisation loop. The outcomes of this study can be considered in the design of braking systems for electric vehicles with superior energy recovery capacity.

Neural Network Control of Green Energy Vehicles with Blended Braking Systems

A neural network-based control system is offered, which ensures high quality blended braking of the green energy vehicles in both the intensive and the gradual deceleration scenarios, with energy recovery at the changing road pavement. In this study, a neural network controller provides the torque gradient control without a tire model resulting in returning maximal energy to the hybrid energy source during the braking process. To meet the conflicting requirements of different braking modes and road surfaces, an allocation algorithm determines how to distribute the driver’s torque request between the friction and electrical brakes. Simulation demonstrates effectiveness of the proposed braking system. Model states and inputs are used as a guidance to learn a coupled two-layer neural network capable to capture various dynamic behaviours that could not be included in the simplified physics-based model. An experimental part of the research proves the model and simulation validity.

Method of electric vehicle braking energy recovery

Currently, the issue of the use of network power storage devices is increasingly being discussed. A promising technical solution is to combine a flywheel and a motor-generator motor on one shaft. Such an association can be called electromachine energy accumulators. Its main disadvantage should be considered the management difficulties. In this regard, it is possible to use an energy recovery system to reduce energy consumption and increase stability in the power grid. Such devices can reduce energy costs and increase the efficiency of vehicles. Such a solution can be useful and effective for the development of electric transport and reducing energy costs.

Mixed Integer Linear Programming Based Speed Profile Optimization for Heavy-Haul Trains

Automatic heavy-haul train (HHT) operation technology has recently received considerable attention in the field of rail transportation. In this paper, a discrete-time-based mathematical formulation is proposed to address the speed profile optimization problem in order to ensure the safe, efficient, and economical operation of heavy-haul trains (HHTs). Due to the presence of long and steep downgrades (LSDs) on some heavy-haul lines, the brake forces of the HHT are typically jointly determined by air braking and electric braking. The time characteristics of the air braking, such as the command delay and the change process caused by the air pressure, are taken into account, and then formulas are presented to calculate the air brake force. In addition, the influence of the neutral section on the control of the electric braking is considered via space-based constraints. The resulting problem is a nonlinear optimal control problem. To achieve linearization, auxiliary 0-1 binary variables and the big-M approach are introduced to transform the nonlinear constraints regarding slope, curve, neutral section, air brake force, and air-filled time into linear constraints. Moreover, piecewise affine (PWA) functions are used to approximate the basic resistance of the HHT. Finally, a mixed integer linear programming (MILP) model is developed, which can be solved by CPLEX. The experiments are carried out using data from a heavy-haul railway line in China, and the results show that the proposed approach is effective and flexible.

Simulations of the operation of the fast light innovative regional train from “Serbian Railways” in traction and electric braking mode

In the paper, the MATLAB-Simulink model of simulation of operation of the fast light innovative regional train from “Serbian Railways” in traction and braking mode is exposed where changes are observed: stator currents of three-phase traction motors, traction electric motor speeds and electric multiple unit, electromagnetic torque on the rotor shaft of the traction electric motor and diirect current bus voltage. The model allowed review of the listed parameters for: different allowed values of contact network voltage and total voltage distortion at the place of connection of the electric multiple unit to the contact network, different mechanical loads of electric multiple unit and traction electric motor and different train speeds and rotation of traction electric motors. Appropriate conclusions were made through the analysis of the simulation results obtained.

Estimating the effectiveness of electric vehicles braking when determining the circumstances of a traffic accident

In the vast majority of cases, the braking process is used to prevent traffic accidents. The effectiveness of this process depends on the design and functionality of vehicle braking systems (presence of anti-lock braking system, emergency braking system, preventive safety systems, etc.) and is limited by the amount of frictional forces in contact of tires with the road. The improvement of methodical approaches to evaluating the effectiveness of braking of cars contributes to increasing the accuracy and objectivity of establishing the circumstances of the occurrence of emergency situations. The paper analyses existing methods of evaluating the braking parameters of vehicles (including those with an electric drive) and modern methods of evaluating electric vehicle braking parameters and conducting auto-technical investigations of traffic accidents, which relate to using different methodological approaches and digital technologies at all stages of expert research. In contrast to existing models, the proposed mathematical model for estimating the trajectory of two-axle cars during braking allows for considering various types of input parameter uncertainty, reducing the range of possible modeling errors by 39%. Comparing simulation results and experimental data showed that the average relative error is 4.58%, and the maximum error did not exceed 7.82%. The performed study of the stability of the electric vehicles' movement during emergency braking with the help of developed mathematical models in the Mathcad software environment reveals the content of the algorithm of a similar calculation in specialized computer programs of auto technical examination. Conducting such calculations is relevant in the analysis of real accident situations, where specific circumstances and features that cannot be considered during modeling in specialized software must be taken into account. Simultaneously, the probability of type I errors is reduced by 2–19%, and type II errors are reduced by 43–68%.

Regenerative braking control strategy for pure electric vehicles based on fuzzy neural network

This study investigates the efficiency and safety of regenerative brake energy recuperation systems for electric vehicles. A three-input single-output fuzzy controller is developed to allocate hydraulic and electric braking forces, considering brake intensity, vehicle speed, and battery SOC's impact on regenerative braking performance. Fuzzy neural networks are utilized due to their effectiveness in solving complex, nonlinear, and fuzzy systems, along with their robustness to parameter changes and external disturbances. The fuzzy process of the controller is optimized using a self-tuning algorithm for designing membership function parameters, resulting in a fuzzy neural network controller. Moreover, electric and hydraulic braking forces are redistributed. Simulation using AVL Cruise software is conducted under NEDC and FTP-75 working conditions. The suggested brake energy recovery control approach using fuzzy neural networks successfully recovers braking energy, achieving energy recovery efficiencies of 14.52% and 39.61% under NEDC and FTP-75 conditions, respectively.