Traction Motor Research Articles

Fast Stator Flux Trajectory Control for PMSM Operating Under Low Switching Frequency

In rail transit traction field, the traction motor needs to operate at a very low switching frequency to decrease the switching loss. Hybrid pulse-width modulation scheme is usually applied for decreasing switching frequency and providing good harmonic performance. However, when the transition between different modulation schemes occurs or the voltage command steps, the stator flux would deviate from the expected trajectory, which would lead to current/torque shock even overcurrent. This paper proposes a method which can provide more flexible choice of voltage vector and can fast eliminate the stator flux deviation. Compared with the previous method, the proposed one can provide better dynamic performance (less oscillation in current/torque and less settling time), especially when the pulse number is relatively low, like square-wave mode, and is easy to implement. The effectiveness of the proposed method is verified by the simulation and experimental results.

Design and Optimization of Electric Vehicle Traction Motor Considering Rotor Topology and Manufacturing Uncertainty

Design and Optimization of Electric Vehicle Traction Motor Considering Rotor Topology and Manufacturing Uncertainty

Prediction of remaining useful life of metro traction motor bearings based on DCCNN-GRU and multi-information fusion

Prediction of remaining useful life of metro traction motor bearings based on DCCNN-GRU and multi-information fusion

Modeling the Dynamic Voltage Distribution in Electric Traction Motor Windings and Its Application on HV Optimal Winding Design

Modeling the Dynamic Voltage Distribution in Electric Traction Motor Windings and Its Application on HV Optimal Winding Design

Weak faults diagnosis of traction motor bearings based on SR-MED-EEMD method

Rolling bearings play a crucial role in ensuring the security and reliable operation of high-speed trains (HST) as a vital component of their traction system. In response to the difficulty in extracting early fault features of high-speed train traction motor bearings under strong background noise, a method based on sparse representation (SR), minimum entropy deconvolution (MED), and ensemble empirical mode decomposition (EEMD) is proposed. The method reduces the dimensionality of complex signals through SR and then filters the sparse signals through MED to improve the fault signal pulse amplitude. The processed signals are decomposed by using EEMD to remove noise components, further improving the peak signal-to-noise ratio of fault characteristic frequency. The method’s efficacy in detecting faults within rolling bearings has been confirmed through simulation experiments and demonstrated its high precision.

Simulation Analysis and Experimental Study on the Fluid–Solid–Thermal Coupling of Traction Motor Bearings

The traction motor is a crucial component of high-speed electric multiple units, and its operational reliability is directly impacted by the temperature increase in the bearings. To accurately predict and simulate the temperature change process of traction motor bearings during operation, a fluid–solid–thermal simulation analysis model of grease-lubricated deep groove ball bearings was constructed. This model aimed to simulate the temperature rise of the bearing and the grease flow process, which was validated through experiments. The results from the simulation analysis and tests indicate that the temperature in the contact zone between the bearing rolling element and the raceway, as well as the ring temperature, initially increases to a peak and then gradually decreases, eventually stabilizing once the bearing’s heat generation power and heat transfer power reach equilibrium. Furthermore, the established fluid–solid–thermal coupling simulation analysis model can accurately predict the amount of grease required for effective lubrication in the bearing cavity, which stabilizes along with the bearing temperature. The findings of this research can serve as a theoretical foundation and technical support for monitoring the health status of high-speed EMU traction motor bearings.

Motor Current Time-Varying Quadratic Phase Coupling Analysis and Its Application in Traction Motor Fault Detection Under Varying-Speed Condition

Motor Current Time-Varying Quadratic Phase Coupling Analysis and Its Application in Traction Motor Fault Detection Under Varying-Speed Condition

СРАВНЕНИЕ ТЯГОВЫХ ПРИВОДОВ С ОПОРНО-ОСЕВЫМ И ОПОРНО-РАМНЫМ ПОДВЕШИВАНИЕМ ДВИГАТЕЛЯ

The problem of finding criteria for choosing between axial support and frame support drives for locomotives in the conditions of import substitution is considered. The changes in the criteria for choosing a traction drive design depending on the changes in requirements for locomotives are analyzed. The types of drives are compared according to their technological simplicity, weight and size indicators, the effect on the coupling properties of the locomotive and the damage to the track structure, taking into account the production of new traction drive designs of an integrated type with collector-free traction electric motors having an increased rotation frequency. It was found that for locomotives with collector traction motors, it is advisable to use a frame support drive with an axial gearbox or a hollow driveshaft, and when using collector-free traction motors , the axial support traction drive of an integrated layout on freight locomotives with 1250 mm wheel diameter to improve coupling properties by 12...15%, it is advisable to replace it with the one proposed by the authors, which is an aggregate-type drive with an elastic link in the shaft lines, or with frame support drives with an axial gearbox, and with a wheel diameter of 1050 mm – frame support drives with a hollow shaft on the axis. An application for a patent has been submitted for the proposed solutions.

Optimization of a parametric product design using techno-economic assessment and environmental characteristics with application to an electric traction motor

Accelerating the development of clean energy technologies is required to move towards a sustainable future. New design approaches are being proposed to help accelerate these developments. This study proposes and improves a novel design framework that incorporates life cycle assessment (LCA) and techno-economic assessment (TEA) with an optimization-based design model of a discrete product at early stage. Objective functions for mass, energy consumption, and two metrics relating to manufacturer and consumer economic interests are considered; these objectives are minimized using a Pareto-like approach. An electric traction motor for an electric vehicle is used as a discrete product case study. The approach demonstrates the utility of considering four-objectives in the design problem, uncertainty analysis via Monte Carlo simulation, and the power of employing LCA and TEA methods. Future work may explore a broader range of objective functions to cover different challenges that inhibit the development of clean energy technologies.

Robust engine slipping start control of hybrid electric vehicles with uncertainty in clutch slipping torque and change in driver demand torque

The engine slipping start (ESS) benefits parallel hybrid electric vehicles from stable ignition and emission reduction. However, inappropriate coordination between the traction motor torque and clutch slipping torque during the ESS will lead to poor smoothness of the vehicle and failed start of the engine. Uncertainty in clutch slipping torque and change in driver demand torque bring tough challenges with sluggish convergence and intensive vehicle jerk in practice. To deal with this problem, a novel two-layer model reference adaptive controller (MRAC) which contains two parallel reference models is proposed to improve robustness and convergence rate simultaneously. On one hand, uncertainties of clutch slipping torque are divided into a low-frequency part and a high-frequency part, and adaptive laws based on the output feedback are designed contrapuntally to enhance robustness. On the other hand, two parallel reference models are designed to accelerate the tracking error convergence rate without changing the reference profiles, which is generated according to the driver demand torque in real time. To test the robustness and convergence rate, the proposed two-layer MRAC is compared with the classical MRAC and proportional–integral controller under the driving scenario with uncertain clutch slipping torque and abrupt change in driver demand torque. The sensitivity with different adaptive gains and low-frequency and high-frequency uncertainties in clutch slipping torque are examined. Finally, hardware-in-the-loop experiments are performed to verify the effectiveness of the proposed two-layer MRAC.

Имитационная модель поезда для учета влияния ветровых нагрузок

Objective: this article is devoted to the problem of regulation of fuel resources for train traction in the presence of variable factors affecting consumption. As an example, the wind-dependent section of the Privolzhskaya railway Pallasovka–Verkhniy Baskunchak was considered, using simulation modeling, the presence of the influence of variable factors on fuel consumption, which are often standard-setting. To propose a methodology for their practical accounting in locomotive depots. Methods: the article proposes a train simulation model to take into account the influence of wind loads. For this purpose, the traction electric drive of a diesel locomotive with direct-alternating current transmission was presented as a speed controller that maintains the movement of the train at a constant speed equal to the technical average. To determine fuel consumption in order to determine the reliability of the results obtained and the possibility of comparison with experimental statistical values, the electromechanical characteristics of the ED133 traction motor, the GS501 generator and the operational characteristics of the D49 diesel engine were used. Results: as a result of modeling the operation of the diesel locomotive power plant, the values of absolute and specific fuel consumption were obtained. The results obtained have a high degree of reliability, as evidenced by earlier statistical data. Practical significance: the results obtained once again demonstrated the influence of wind on train traction, and on wind-dependent sections of railways they are standard-forming. The considered model can be applied for the purpose of rationing the consumption of fuel and energy resources in the presence of variable factors affecting consumption.

Towards multi-scene learning: A novel cross-domain adaptation model based on sparse filter for traction motor bearing fault diagnosis in high-speed EMU

Fault diagnosis of traction motor bearing is of great significance to improve the reliability and safety of high-speed electric multiple units (EMU). While the fault diagnosis method based on cross-domain adaptation has been successful in scenarios involving speed or load fluctuations, existing methods ignore the independence and diversity of features, resulting in unsatisfactory diagnostic results under multi-scene learning, thereby reducing the generalization ability. Moreover, the development of complex models is time-consuming, and their computational efficiency is low. To address these issues, this study proposes a novel cross-domain adaptation model based on sparse filtering (SFCDA), which consists of only two fully connected (FC) layers. Firstly, pre-training is conducted to utilize the soft reconstruction penalties to constrain the weights of sparse filtering and improve the independence of features. The weights of unsupervised training are used to initialize the parameters of the first FC layer of the SFCDA model. Secondly, a multiple sparse regularization (MSR) algorithm is proposed and used to constrain the SFCDA. Then, fine-tuning is conducted, in which the structural alignment function is used to measure the distribution distance between the source and target domain data. Minimizing the kernel norm can improve the diversity of features and enhance the robustness. Finally, the effectiveness of SFCDA in multi-scene learning is proved theoretically. It is validated in three fault diagnosis scenes in four different bearing datasets. The results show that the suggested approach is more straightforward and has a better fault diagnosis effect than the state-of-the-art domain adaptive approaches.

Deep neural network-based modeling and optimization methodology of fuel cell electric vehicles considering power sources and electric motors

This study proposes a modeling and optimization methodology for fuel cell electric vehicles (FCEVs). Among FCEV components, the traction motor, lithium-ion battery, fuel cell stack, and air supply system are mainly investigated. The FCEV modeling is performed based on the vehicle specifications, electromagnetic finite element analysis, and experimental data. To conduct design optimization, deep neural networks (DNNs) are adopted and trained to predict vehicle performance considering the fluctuation of applied direct current voltage. At this stage, the adaptive layering and sampling algorithm was suggested, which enables efficient DNN construction. To confirm the feasibility of the suggested training algorithm, the number of hidden layers and sampling points of constructed DNNs are investigated. Finally, DNN-based fuel economy optimization is performed considering the driving performance. The effectiveness of the proposed optimization methodology is validated by additional optimization results.

Requirements for vibroacoustic methods of the quality assessment of vehicles traction electric motors

The progression and refinement of traction electric motors play a pivotal role in the enhancement of vehicles. With constant advancements in power efficiency, diagnostic tools, and reliability, electric traction motors are becoming more economical in terms of operational costs. This article underscores the significance of methods used in assessing the technical proficiency of traction electric motors, coupled with their testing techniques. The vibration characteristics of these motors serve as a prime criterion in assessing their quality throughout their lifecycle – from design and manufacturing to operation. A groundbreaking proposition made in this article is the evaluation of electric traction motors’ health based on their vibration acceleration within an expansive frequency range of 5 Hz to 10 kHz. This contrasts the conventional approach which focuses on vibration speed in a restricted frequency spectrum up to 1000 Hz, as outlined by current standards. Such permissible vibration classes are essential in ensuring the high-quality design, technical finesse, and dependability of traction electric motors. Exceeding these vibration thresholds can result in rapid wear and tear, diminishing the motor’s reliability and lifespan. Hence, motors that surpass these permissible vibrations are not advised for vehicular use. Additionally, the article sheds light on requisite equipment controls for accurately gauging vibrations and noise levels in electric motors. Delving deeper into the nuances, the methods for mounting vibration transducers and electric motors on testing platforms are elucidated. These methods are pivotal in obliterating any hindrances that could impede the precise measurement of vibrations induced by an electric motor.

Design and Performance Analysis of an Outer-Rotor PMaSynRM

Permanent magnet synchronous motors (PMSMs) are consciously used as traction motors in electric vehicles (EVs) and hybrid electric vehicles (HEVs). The rotor position (inner-rotor, outer-rotor) and topology of PMSMs significantly impact their torque profile, efficiency, and demagnetization characteristics. This article focuses on designing an outer-rotor permanent-magnet-assisted synchronous reluctance motor (PMaSynRM) under traction motor requirements and investigating its electromagnetic performance using the finite element method (FEM). This study's primary challenge is achieving optimal machine performance, considering high maximum torque and the risk of demagnetization at low levels. The design, derived from analytical calculations, was subjected to Finite Element Method (FEM) analyses. These analyses investigated motor performance in terms of efficiency, the ability to generate torque at different drive currents, and the risk of demagnetization. As a result of the study, the proposed PMaSynRM design was obtained that provides an adequate demagnetization performance even under 300% loading, has a maximum torque exceeding 35 Nm, and achieves an efficiency of 90% at rated conditions.

Model-based closed-loop process control for the manufacturing of hairpin coils

As a consequence of the increasing market share of hybrid and all-electric mobility solutions, there is a need for robust manufacturing technologies that enable the efficient production of powerful electric traction motors in automotive industry. In the context of high-quality distributed stator windings, the so-called hairpin technology already meets the automotive demand regarding productivity – but process reliability is still limited by variations in the mechanical and geometric properties of the enameled rectangular copper wire leading to different springback effects within the forming processes. Against this background, a model-based closed-loop process control for the sequential tool-bound bending of hairpin coils is introduced and validated in this paper.

Imbalanced deep transfer network for fault diagnosis of high-speed train traction motor bearings

Transfer learning-based fault diagnosis methods have been increasingly utilized for major equipment, including high-speed trains, turbine machines, and aircraft engines. However, most traditional transfer methods based on implicitly balanced data only consider feature shift. When applied to high-speed train traction motor bearing fault diagnosis, the cross-domain generalization ability of these transfer methods is weakened by label shift. Due to the complex operating conditions of high-speed trains, these transfer methods often fail under multiple operating conditions, resulting in reduced cross-domain diagnostic accuracy when faced with feature shift and label shift simultaneously. Therefore, we propose the imbalanced deep transfer network (IDTN) to tackle the aforementioned problem in cross-domain fault diagnosis of high-speed train traction motor bearings. Firstly, IDTN overcomes the influence of imbalanced distributions in source domain samples through deep imbalanced learning. Then, batch nuclear-norm maximization is introduced to enhance the prediction discriminability and diversity of the target domain samples. Finally, case studies of the high-speed train traction motor bearing fault dataset and the Case Western Reserve University bearing fault dataset are conducted. Experimental results prove the effectiveness and superiority of IDTN in the cross-domain fault diagnosis field with both feature shift and label shift.

Modeling of an Integrated Traction Motor Protection System

Purpose. The article is devoted to the creation of traction motor protection by modern methods and means based on a programmable logic controller (PLC). Methodology. To simulate motor fault monitoring in laboratory conditions, magnetic field and temperature sensors were installed on a small powerful induction motor, and then the data obtained from the sensors in normal and overloaded motor modes were analyzed. Based on the research, protection methods were developed. Findings. Previously used simple motor protection systems based on components such as timers, contactors, electromagnetic switches, voltage and current transformers were slow and inaccurate, and had low sensitivity. However, the production of PLCs and their application in this industry has eliminated these problems. The intensive operation of motors as the main executive equipment requires the creation of modern automated protection systems to ensure their reliable and stable operation. Originality. The paper improves the method of protection of traction motors based on the use of programmable logic controllers. In addition to the voltage and current parameters, the proposed method involves monitoring the magnetic field. It also provides for the possibility of adjusting the protection response time. As a result, in the event of overload, short circuit, and other non-standard situations, the improved method provides the system with the ability to make more accurate and reliable decisions. Practical value. The TIA Portal software package has modeled a traction motor protection system by temperature and current and considered its practical application. To make such systems more effective, in the future, comprehensive protection systems based on the diagnostic state of the traction motor can be developed. In addition, an effective system can be created by providing SCADA control of the engines of several vehicles simultaneously.

A deep targeted transfer network with clustering pseudo-label learning for fault diagnosis across different Machines

In recent years, deep transfer models have addressed the issue of distribution shift between the source and target domains by learning domain-invariant features. However, in cross-machine fault diagnosis scenarios, existing deep learning models struggle to fit the conditional distribution of target domain samples, limiting the performance and generalization of domain adaptation models. To address these problems, we propose a deep targeted transfer network with clustering pseudo-label learning (DTTN-CPLL). DTTN-CPLL consists of three parts. First, a deep transfer network is constructed to extract cross-domain features. To reduce the intra-class distribution gap, a clustering pseudo-label learning algorithm is proposed to create subdomain labels within the target domain features. Then, we simultaneously minimize and maximize the entropy of features and the number of linearly independent vectors in the target domain to reduce the distance between subdomain features. Finally, under the constraint of local maximum mean discrepancy, we reduce the distribution discrepancy between the source and target domains at the subdomain feature level. We conducted 12 cross-machine transfer tasks on three open bearing datasets and a private high-speed train traction motor bearing dataset. The results demonstrate that, compared to other state-of-the-art models, DTTN-CPLL is effective and superior in cross-machine fault diagnosis.

Regeneration of Hybrid and Electric Vehicle Batteries: State-of-the-Art Review, Current Challenges, and Future Perspectives

Batteries have been integral components in modern vehicles, initially powering starter motors and ensuring stable electrical conditions in various vehicle systems and later in energy sources of drive electric motors. Over time, their significance has grown exponentially with the advent of features such as “Start & Stop” systems, micro hybridization, and kinetic energy regeneration. This trend culminated in the emergence of hybrid and electric vehicles, where batteries are the energy source of the electric traction motors. The evolution of storage for vehicles has been driven by the need for larger autonomy, a higher number of cycles, lower self-discharge rates, enhanced performance in extreme temperatures, and greater electrical power extraction capacity. As these technologies have advanced, so have they the methods for their disposal, recovery, and recycling. However, one critical aspect often overlooked is the potential for battery reuse once they reach the end of their useful life. For each battery technology, specific regeneration methods have been developed, aiming to restore the battery to its initial performance state or something very close to it. This focus on regeneration holds significant economic implications, particularly for vehicles where batteries represent a substantial share of the overall cost, such as hybrid and electric vehicles. This paper conducts a comprehensive review of battery technologies employed in vehicles from their inception to the present day. Special attention is given to identifying common failures within these technologies. Additionally, the scientific literature and existing patents addressing regeneration methods are explored, shedding light on the promising avenues for extending the life and performance of automotive batteries.