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    <title>Transport Research International Documentation (TRID)</title>
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    <atom:link href="https://trid.trb.org/Record/RSS?s=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" rel="self" type="application/rss+xml" />
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    <language>en-us</language>
    <copyright>Copyright © 2026. National Academy of Sciences. All rights reserved.</copyright>
    <docs>http://blogs.law.harvard.edu/tech/rss</docs>
    <managingEditor>tris-trb@nas.edu (Bill McLeod)</managingEditor>
    <webMaster>tris-trb@nas.edu (Bill McLeod)</webMaster>
    <image>
      <title>Transport Research International Documentation (TRID)</title>
      <url>https://trid.trb.org/Images/PageHeader-wTitle.jpg</url>
      <link>https://trid.trb.org/</link>
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    <item>
      <title>Design Optimization and Performance Analysis of Synchronous Reluctance Machine With Hybrid Rotor Cores</title>
      <link>https://trid.trb.org/View/2665541</link>
      <description><![CDATA[In this article, the synchronous reluctance machine (SynRM) based on a hybrid material rotor core (hybrid core) is proposed, whose innovative rotor structure is designed using a combination of nongrain-oriented (NGO) silicon steel sheets and grain-oriented (GO) silicon steel sheets. The high permeability characteristics of GO along the rolling direction (RD) optimize the magnetic circuit distribution and significantly improve the electromagnetic performance of the SynRM. Meanwhile, the equivalent magnetic network (EMN) model is improved for accurately predicting the performance of SynRM with a hybrid core, which combines the computational efficiency of the magnetic equivalent circuit (MEC) with the accuracy advantage of the reluctance network (RN) model to achieve an optimal balance between computational speed and accuracy. In particular, a new air gap modeling method is proposed to further enhance the efficiency and convenience of dynamic analysis. On the other hand, the initial structure of the SynRM is determined based on the EMN modeling method, and the performance of SynRM with an NGO rotor core (NGO core) and a hybrid core is further analyzed and optimized by the finite element method. Moreover, the radial basis function neural network-assisted optimization method based on the optimization space reduction strategy at the Pareto frontier is improved to reduce the number of samples and improve the accuracy of the agent model. The results show that the torque output capability, efficiency, and power factor of the SynRM with a hybrid core are significantly improved compared to the SynRM with an NGO core. Finally, the prototype is fabricated and tested to verify the accuracy of the proposed EMN model and the validity of the hybrid rotor core design.]]></description>
      <pubDate>Thu, 04 Jun 2026 11:57:21 GMT</pubDate>
      <guid>https://trid.trb.org/View/2665541</guid>
    </item>
    <item>
      <title>CrowdMagMap 2.0: Crowdsourced Magnetic Mapping for Multi-Floor Underground Parking Lot Navigation</title>
      <link>https://trid.trb.org/View/2658763</link>
      <description><![CDATA[Location-based services (LBS) have become an integral part of daily life and work for the general public. However, achieving widespread and accurate positioning in typical indoor environments remains a significant challenge, particularly in multi-floor indoor parking lots where radio frequency signals like WiFi are often unavailable. Indoor magnetic matching presents a viable solution, but it requires reducing mapping costs through the use of crowdsourced data. To tackle this issue, we propose an innovative method for constructing magnetic maps using crowdsourced vehicle data. Our approach introduces a multi-user joint vehicle dead reckoning technique based on graph optimization, which provides consistent directional estimates of crowdsourced vehicle trajectories. Subsequently, we establish associations between different vehicle trajectories using multi-attribute features of the magnetic field. Building on this foundation, we propose a global trajectory optimization with inequality and equality constraints to achieve precise estimation of crowdsourced vehicle trajectories. Testing with simulated data from two three-floor underground parking lots demonstrates that the proposed method, utilizing only on-board smartphone sensor data, achieves plane and elevation errors of less than 2.75 meters (95%) and 0.59 meters (95%), respectively. Additionally, the magnetic matching positioning error based on crowdsourced magnetic sequence maps is less than 2.29 meters (95%).]]></description>
      <pubDate>Thu, 28 May 2026 17:09:33 GMT</pubDate>
      <guid>https://trid.trb.org/View/2658763</guid>
    </item>
    <item>
      <title>Vehicle Classification Based on Multi-Frequency Resistance and Reactance Magnetic Profiles</title>
      <link>https://trid.trb.org/View/2553367</link>
      <description><![CDATA[This paper presents the application of inductive loop (IL) sensor technology to classify vehicles in traffic lanes. Two wide and two slim IL sensors were installed in the traffic lane. The wide and slim IL sensors feature distinct structural designs and varying levels of sensitivity. An advanced multi-frequency impedance measurement (MFIM) system was used to operate the IL sensors. For a passing vehicle, the impedance of every IL sensor at three different operating frequencies is computed and finally recorded at a sampling frequency of 1 kHz. Each of the 12 recorded signals provides a complex-value vehicle magnetic profile (VMP). Based on the VMPs from two IL sensors positioned one after the other, an accurate measurement of vehicle speed is obtained. Furthermore, the system can capture images of vehicles. A reference database of VMPs was created for various vehicle categories. The software selects 10 statistical features from each real and imaginary VMP part. Eight machine learning algorithms were implemented using ready-made Python3 implementations. Cross-validation accuracy was tested for five feature configurations, including slim and wide IL sensors. The Random Forest (RF) algorithm, utilizing 20 features from the complex VMP, achieved an accuracy of 99.8 % for the wide IL sensor. No errors were made by the Voting Classifier and RF algorithm when they incorporated a fusion of features from complex VMPs with MFIM system, utilizing both slim and wide IL sensors.]]></description>
      <pubDate>Tue, 27 Jan 2026 09:21:21 GMT</pubDate>
      <guid>https://trid.trb.org/View/2553367</guid>
    </item>
    <item>
      <title>Improving the self-healing properties of bitumen mastic under microwave irradiation by heated oil-ground steel slag</title>
      <link>https://trid.trb.org/View/2649955</link>
      <description><![CDATA[Steel slag (SS) is a solid waste rich in iron elements with the potential for microwave heating. However, the low ferrite content and the barrier of the silicate layer limit the efficiency of microwave heating of SS. This study employs soybean oil as the grinding environment and utilizes heated oil milling technology to functionalize steel slag. This process facilitates the separation and purification of the iron phase from the SS, enabling the generation of additional Fe₃O₄ through reduction reactions. The synergistic effect of heating and grinding promotes the conversion of Fe³ + to Fe² +, which results in a 31 % increase in  Fe² + content and a 34 % increase in saturation magnetization of heated oil grinding steel slag (HGSS). Through the targeted design of particle size and oleophilic surfaces, HGSS is uniformly and compactly dispersed in the bitumen, constructing a network structure with multi-refraction properties, which reduces the reflection loss by 26 %. Under the combined effect of phase and structural changes, compared to SS bitumen mastic, HGSS bitumen mastic showed a 20 % increase in temperature rise at 60 s of microwave heating, in addition to an 11 % increase in healing index. This study aims to enhance the microwave absorption performance of SS by optimizing its microstructure and composition, thereby improving the self-healing efficiency of steel slag-bitumen composite materials under microwave heating conditions and providing new insights into the high-value utilization of SS.]]></description>
      <pubDate>Thu, 15 Jan 2026 09:22:33 GMT</pubDate>
      <guid>https://trid.trb.org/View/2649955</guid>
    </item>
    <item>
      <title>Analysis and Control of a Dual-Stator PM Motor Based on Parallel Path Magnetic Technology</title>
      <link>https://trid.trb.org/View/2604067</link>
      <description><![CDATA[This article makes a systematical and comprehensive study of a new stator-PM motor based on parallel path magnetic technology (PPMT), naming PPMT motor, which has not been studied deeply. This motor’s unique structural feature lies in that the PMs are identically embedded in the stator with the polarities opposite and the excitation windings are wounded on the stator yoke, which distinguishes it from PMSM and other stator-PM motors. To analyze this motor, first, we summarize the fundamental structure and operation principle of single-phase and poly-phase PPMT motors, and then, FE analysis is conducted to verify the motor’s electromagnetic property and establish the motor’s mathematical model. Compared with the flux-switching permanent-magnet (FSPM) motor, the PPMT motor has lower slot torque, torque ripple, and better sinusoidal back EMF, which makes it more suitable for servo application. Next, we propose a control strategy for the PPMT motor with simulation verification. Finally, a prototype of a dual-stator PPMT motor and driver circuit are constructed for experimental verification; the result shows that with sliding mode control, the speed loop has a better performance, though the control method and performance of the position loop need to be further studied and improved. This article conducts fundamental research for PPMT motors and further research needs to be done to improve the performance for specific applications.]]></description>
      <pubDate>Fri, 19 Dec 2025 10:18:52 GMT</pubDate>
      <guid>https://trid.trb.org/View/2604067</guid>
    </item>
    <item>
      <title>Robust Multiple-Fault Diagnosis of PMSMs in Dynamic Operations Under Imbalanced Datasets</title>
      <link>https://trid.trb.org/View/2603991</link>
      <description><![CDATA[Data-driven models for multiple-fault classifiers of permanent magnet synchronous machines (PMSMs) in electric power trains require historical data at faulty cases in a wide range of operations. However, data in healthy or steady-state operations are much more than the data in the faulty cases, resulting in imbalanced datasets and rendering challenges of expanding the robustness of the data-driven models in dynamic operations. To address these issues, this article proposes a robust two-stage learning scheme for detecting and classifying multiple incipient faults in PMSMs, namely, interturn short circuit (ITSC) local demagnetization (LD) and mixed faults (MFs), under dynamic operations. The first stage focuses on enhancing the effectiveness of anomaly detector in dealing with imbalanced datasets at low-severity faults by integrating prior domain knowledge into anomaly detectors and reducing false alarms using an extreme gradient boosting machine (XGBoost). In the second stage, a training algorithm based on a convolutional neural network (CNN) is introduced to classify multiple faults by studying the appearance of low-severity faults. The performance of the proposed anomaly detector is compared to conventional one-class models, including random forest, local outlier factor, and support vector machine. The accuracy and robustness of the suggested scheme are further evaluated by benchmarking pretrained classifiers such as MobileNetv2, ResNet50, and VGG16, using an imbalanced dataset at different operating conditions from an in-house test setup. A comparative study with a traditional CNN is conducted to explain how the model provides the prediction.]]></description>
      <pubDate>Fri, 05 Dec 2025 14:12:34 GMT</pubDate>
      <guid>https://trid.trb.org/View/2603991</guid>
    </item>
    <item>
      <title>Evolution and Analysis of Leakage Flux Path for Memory Machine With Variable Leakage Flux Capability</title>
      <link>https://trid.trb.org/View/2603969</link>
      <description><![CDATA[To further expand the flux regulation (FR) range, variable leakage flux (LF) technology is introduced into the hybrid magnetic circuit memory machine (HMC-MM). This technology is realized by properly constructing LF paths in the rotor and controlling their saturation degree. However, how to design the LF paths reasonably to achieve the best performance balance of the memory machine (MM) is still a challenging puzzle. In response to this puzzle, this article first evolves a conventional HMC-MM into three variable LF HMC-MMs (V+HMC-MMs) with different LF paths by configuring different q-axis barriers. The influence mechanism of different LF paths on FR range is theoretically revealed by analyzing and comparing the equivalent magnetic circuit models of the above three evolved V+HMC-MMs. Furthermore, the electromagnetic characteristics of the four investigated MMs under different magnetization states (MSs) are comprehensively computed by the finite element (FE) method. Subsequently, through a quantitative comparison and tradeoff of main electromagnetic characteristics, a preferred LF design that can achieve the best performance balance is identified. Finally, a V+HMC-MM prototype with the preferred design is manufactured and tested. Experimental results validate the correctness of the FE analysis.]]></description>
      <pubDate>Wed, 26 Nov 2025 14:14:25 GMT</pubDate>
      <guid>https://trid.trb.org/View/2603969</guid>
    </item>
    <item>
      <title>PMSLM Demagnetization Fault Detection Based on Multisensor Signal Fusion and Enhanced Graph Neural Network</title>
      <link>https://trid.trb.org/View/2603967</link>
      <description><![CDATA[An innovative diagnosis method for detecting demagnetization faults (DFs) in permanent magnet synchronous linear motors (PMSLMs) is proposed in this study. The developed approach leverages the combined strengths of the external three-line measurement (ETM) method and the adaptive edge coefficient graph attention network (AECGAT). The approach begins with the deployment of a tunnel magnetoresistance signal acquisition array to collect external stray magnetic flux signals (ESMFSs) for fault diagnosis. Compared with other signal acquisition strategies, the multisensor array offers advantages, including enhanced spatial resolution and the ability to capture comprehensive magnetic field distribution characteristics. Subsequently, the demagnetization three-line graph (DTG) is utilized to map the three-line signals and effectively capture the spatial relationships among sensors. The adaptive edge coefficient (AEC) prior mechanism is integrated into graph attention network (GAT) to further enhance the diagnostic capabilities. The AEC prior mechanism can reduce redundant connections and increase sparsity in the graph structure, thus improving the model’s efficiency and performance. Experimental validation on simulation reaches 98.75%, and prototype PMSLMs achieves 97.50% classification accuracy. The proposed method offers a highly accurate and reduces dependence on internal sensors, lowering maintenance costs and operational downtime.]]></description>
      <pubDate>Wed, 26 Nov 2025 14:14:25 GMT</pubDate>
      <guid>https://trid.trb.org/View/2603967</guid>
    </item>
    <item>
      <title>Study on the magnetic-temperature characteristics of the induction asynchronous mechanical-electric-hydraulic power coupler</title>
      <link>https://trid.trb.org/View/2625570</link>
      <description><![CDATA[The promotion and application of new energy vehicles have become an important trend. However, traditional electric vehicles face challenges such as a single energy structure and high costs. Hydraulic power, on the other hand, features high power density, efficient regenerative braking, and reliable operation. To address issues with conventional multi-source power coupling devices, which are typically bulky, loosely structured, and exhibit low energy conversion efficiency, an Induction Asynchronous Mechanical-Electric-Hydraulic Power Coupler (IA-MEHPC) has been proposed. This coupler boasts a compact structure and facilitates the mutual conversion of mechanical energy, electrical energy, and hydraulic energy. The study explored the structural design and analysis methods for the electrodynamic and hydraulic power aspects of the IA-MEHPC. It analyzed the impact of the hydraulic power structure on magnetic field distribution. It also discussed the effects of temperature rise on the IA-MEHPC. Specifically, the study examined how temperature affects the inductance of the motor stator windings. Compared to existing research, this study is the first to thoroughly explore the impact of temperature on the system, particularly its thermal magnetic characteristics. The findings indicate that the introduction of liquid can effectively lower the motor’s temperature, ensuring stability and high efficiency under high load and high-speed working conditions. With increasing temperature, the average value of inductance shows a downward trend. In the Electro-Hydraulic Coupling Mode, the magnetic induction intensity of the motor increases by approximately 3% compared to the Pure Electric Motor Mode. This research provides crucial references for the structural design optimization and performance enhancement of the IA-MEHPC.]]></description>
      <pubDate>Tue, 25 Nov 2025 09:19:23 GMT</pubDate>
      <guid>https://trid.trb.org/View/2625570</guid>
    </item>
    <item>
      <title>Time-domain characterisation of magnetorheological fluid engine mount with double-throttle disk</title>
      <link>https://trid.trb.org/View/2598719</link>
      <description><![CDATA[This paper analyses the temporal characteristics of the double-throttle disk magnetorheological fluid engine mount. Initially, we introduce and perform a magnetic circuit analysis of the double-throttle disk magnetorheological fluid engine mount. Subsequently, we derive the mathematical model of the magnetorheological fluid engine mount and conduct a temporal characteristics analysis. Finally, we construct a double-throttle disk MRF engine mount system based on a 1/4 car model. Then we analyse the impact of applying different electrical currents to various inertia channels on road excitation and engine excitation. The research findings indicate that applying a current of I = 2.0 A to all four magnetorheological fluid inertial channels enables the magnetorheological fluid engine mount to achieve low-frequency vibration isolation effects. Conversely, when no electrical current is applied to the four magnetorheological fluid inertial channels, the magnetorheological fluid engine mount demonstrates high-frequency vibration isolation effects.]]></description>
      <pubDate>Tue, 25 Nov 2025 09:18:59 GMT</pubDate>
      <guid>https://trid.trb.org/View/2598719</guid>
    </item>
    <item>
      <title>Analysis of the cooling performance based on the three-dimensional magnetic-thermal coupling in high-speed permanent magnet synchronous motor</title>
      <link>https://trid.trb.org/View/2606805</link>
      <description><![CDATA[The high-speed permanent magnet synchronous motors (HSPMSM) serve as the primary driving mechanism for the air compressor within the hydrogen fuel cell system; however, the heating problem is serious in HSPMSM and the heat is difficult to dissipate, which leads to severe challenges to the life and safety of the hydrogen fuel cell system. To solve this problem, an accurate indirect magnetothermal coupling analysis model was established to obtain the temperature characteristics of the HSPMSM. The loss of HSPMSM was analyzed initially, followed by an investigation into the interplay between the temperature and electromagnetic fields. Utilizing this model, the cooling efficiency of the axial channel and circumferential parallel channel were compared, and the temperature distribution considering the influence of the load and the high-temperature gas on the load side of the air compressor was analyzed. The findings suggest that, for the centrifugal compressor motors utilized in hydrogen fuel cell systems, the axial cooling waterway is recommended.]]></description>
      <pubDate>Mon, 13 Oct 2025 08:49:07 GMT</pubDate>
      <guid>https://trid.trb.org/View/2606805</guid>
    </item>
    <item>
      <title>An Energy-Efficient Multi-Source Fusion Mechanism for Vehicle Detection Scenarios Based on Seismic and Magnetic Signatures</title>
      <link>https://trid.trb.org/View/2593860</link>
      <description><![CDATA[Currently, the magnetometer-based Wireless Parking Detector (WPD) powered by a battery has the advantages of low cost and convenient installation, which leads to its widespread use in collecting parking status information for Intelligent Parking System (IPS). However, existing magnetometer-based parking detection (MPD) schemes waste a lot of energy to process the redundant magnetic data when the parking space is free or occupied. Although energy consumed by WPD devices can be optimized by reducing the magnetic sampling rates, it may reduce the detection accuracy. For this reason, a geophone-magnetometer based parking detection method (GMPD) combing the signatures extracted from a self-powered geophone and magnetic sensor, is proposed. In the work, MPD algorithm can only be awakened when a vehicle-induced seismic feature is detected. Therefore, the magnetometer and microcontroller can sleep as long as possible when the parking space is free or occupied. Finally, the experimental results show that the overall accuracy of GMPD is 99.33%, and the energy consumption is only 18.07% of the existing algorithms, which provides a valuable reference for the WPD industry and reduces the pollution of waste batteries to the environment.]]></description>
      <pubDate>Wed, 24 Sep 2025 15:31:09 GMT</pubDate>
      <guid>https://trid.trb.org/View/2593860</guid>
    </item>
    <item>
      <title>Research on time delay compensation control of Taylor series backstepping for magnetorheological semi-active suspension</title>
      <link>https://trid.trb.org/View/2596685</link>
      <description><![CDATA[The investigation of magnetorheological (MR) semi-active suspension is crucial for automotive applications. This paper introduces a novel approach by considering time delay effect when load changes and considering vehicle’s spring-loaded mass as an uncertain parameter. To address these challenges, a new magnetorheological semi-active quarter-vehicle suspension controller is proposed. Controller combines inverse model with Taylor series backstepping control strategy to determine the necessary damping force provided by magnetorheological damper based on system’s dynamic error. Additionally, damping force is compensated using Taylor series expansion method. Drive current of magnetorheological damper is obtained by solving hyperbolic positive model parameters and inverse model through nonlinear least squares genetic algorithm. Simulation experiments are conducted to compare the performance of Taylor series backstepping control (TBS) suspension, backstepping control suspension affected by time delay, MSH control suspension affected by time delay and passive suspension. Various metrics such as spring-loaded mass displacement, velocity, acceleration, suspension dynamic displacement, and wheel dynamic load under random road excitation are evaluated. Simulation results demonstrate that proposed system effectively mitigates vehicle vibration caused by time delay and load variations, while enhancing vehicle’s handling and smoothness.]]></description>
      <pubDate>Wed, 24 Sep 2025 15:31:08 GMT</pubDate>
      <guid>https://trid.trb.org/View/2596685</guid>
    </item>
    <item>
      <title>Design and optimization of spoke type permanent magnet synchronous machines a rare-earth element free solution for electromobility</title>
      <link>https://trid.trb.org/View/2598615</link>
      <description><![CDATA[Electromobility solutions are central to the current decarbonization of transportation. So far, no other solution has contributed significantly to reducing carbon emissions other than adopting or using electric vehicles. However, a significant percentage of these new vehicles have been designed to use Rare-Earth Elements (REEs), mainly in Permanent Magnets (PMs) of their electric motor, but also in other components of their powertrain. Currently, REEs are classified by the European Union and other governing agencies as "critical raw materials" due to their fragile supply chain and their importance in strategic industries. Alternatives to REE-based PMs have been studied for some time now, but no alternative is widely used today in electromobility applications. This thesis explores the use of ferrites PMs in electric machines; in particular, it studies the design and optimization of Spoke Type Permanent Magnets Synchronous Machines (Spoke) using this type of PMs.]]></description>
      <pubDate>Fri, 12 Sep 2025 10:19:06 GMT</pubDate>
      <guid>https://trid.trb.org/View/2598615</guid>
    </item>
    <item>
      <title>Uniform Demagnetization Fault Diagnosis of PMSM Based on Radial Air-Gap Flux Density Under Nonstationary Conditions</title>
      <link>https://trid.trb.org/View/2553622</link>
      <description><![CDATA[Permanent magnet synchronous motor (PMSM) often operates in harsh environments, such as high temperatures and intense vibrations, which could result in partial demagnetization fault or uniform demagnetization fault (UDF). Although the probability of UDF is relatively low, once it occurs, it will cause a decrease in motor efficiency and even system paralysis. In addition, the PMSM usually operates under nonstationary conditions, which could increase the difficulty in UDF diagnosis. Hence, this article proposes a UDF diagnosis method of PMSM based on radial air-gap flux density under nonstationary conditions. In this method, the d-axis magnetic network model of PMSM is established and analyzed. The fault feature for UDF is extracted from radial air-gap flux density based on the d-axis magnetic network model. By the extracted fault feature, not only UDF can be detected, but also UDF degree can be calculated. Both the simulation and experimental results validate the effectiveness of the proposed UDF diagnosis method.]]></description>
      <pubDate>Fri, 18 Jul 2025 15:10:23 GMT</pubDate>
      <guid>https://trid.trb.org/View/2553622</guid>
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