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    <title>Transport Research International Documentation (TRID)</title>
    <link>https://trid.trb.org/</link>
    <atom:link href="https://trid.trb.org/Record/RSS?s=PHNlYXJjaD48cGFyYW1zPjxwYXJhbSBuYW1lPSJkYXRlaW4iIHZhbHVlPSJhbGwiIC8+PHBhcmFtIG5hbWU9InN1YmplY3Rsb2dpYyIgdmFsdWU9Im9yIiAvPjxwYXJhbSBuYW1lPSJ0ZXJtc2xvZ2ljIiB2YWx1ZT0ib3IiIC8+PHBhcmFtIG5hbWU9ImxvY2F0aW9uIiB2YWx1ZT0iMCIgLz48L3BhcmFtcz48ZmlsdGVycz48ZmlsdGVyIGZpZWxkPSJpbmRleHRlcm1zIiB2YWx1ZT0iJnF1b3Q7SW5kdWN0aW9uIG1vdG9ycyZxdW90OyIgb3JpZ2luYWxfdmFsdWU9IiZxdW90O0luZHVjdGlvbiBtb3RvcnMmcXVvdDsiIC8+PC9maWx0ZXJzPjxyYW5nZXMgLz48c29ydHM+PHNvcnQgZmllbGQ9InB1Ymxpc2hlZCIgb3JkZXI9ImRlc2MiIC8+PC9zb3J0cz48cGVyc2lzdHM+PHBlcnNpc3QgbmFtZT0icmFuZ2V0eXBlIiB2YWx1ZT0icHVibGlzaGVkZGF0ZSIgLz48L3BlcnNpc3RzPjwvc2VhcmNoPg==" rel="self" type="application/rss+xml" />
    <description></description>
    <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>
    </image>
    <item>
      <title>Energy Efficiency Improvement Framework for Regenerative Braking System in Electric Vehicles</title>
      <link>https://trid.trb.org/View/2665608</link>
      <description><![CDATA[Electric vehicles (EVs) face challenges in enhancing regenerative braking (RB) efficiency, particularly at low speeds, where the traction motor’s back-electromotive force is insufficient for energy regeneration. Below a certain dynamic low-speed threshold, energy is extracted from the battery instead of being returned, exacerbating electrical losses in the drive. A model-based approach is proposed to analytically determine this dynamic low-speed threshold, alongside a loss minimization framework based on a variable flux approach to enhance energy recovery. The simulation results using a vector-controlled induction motor (IM) drive indicate a significant reduction in total system losses during braking in the high-speed, low-torque region. The loss reduction effectively lowers the low-speed threshold by 5%, depending on the specific driving conditions, for the considered target vehicle’s drive system. However, the optimal flux point varies depending on machine parameters. Hence, the effect of temperature-induced variations in stator and rotor resistances and magnetic saturation on loss minimization is also explored, showing that optimal flux point sensitivity is particularly impacted by resistance changes. The experimental validation on representative drive cycles corroborates the simulation results, showing a 13% reduction in system losses under the modified Indian driving cycle and 7% under the U.S. EPA highway cycle.]]></description>
      <pubDate>Wed, 17 Jun 2026 16:13:03 GMT</pubDate>
      <guid>https://trid.trb.org/View/2665608</guid>
    </item>
    <item>
      <title>Development and Optimization Method of Machine Considering Matching Integrated Rim-Driven Fan for Aviation Electric Propulsion</title>
      <link>https://trid.trb.org/View/2665595</link>
      <description><![CDATA[The rim-driven fan (RDF) is a distinctive aviation electric propulsion system where fan blade tips are directly driven by a rim-driven induction motor (RDIM). This configuration offers higher propulsion efficiency and a stronger correlation between fan torque demands and motor output torque. Within this study, a model of the nonlinear load characteristics of the RDF is established, and principles governing the matching of RDIM output performance across diverse flight conditions are derived. A method for determining the rational design boundaries of the RDIM is formulated, which can guide the matching design between the RDIM and different RDFs. These design constraints for the RDIM encompass torque-fan load matching, output power, and power factor requirements. Although the preliminary design satisfies basic operational requirements, significant opportunities exist to enhance system efficiency and power density. A stepwise optimization method for aviation multioperating conditions is proposed, which gradually optimizes independent objectives required for different operating conditions, ultimately targets three balanced core objectives for the Pareto frontier, enables RDIM to achieve system optimization under diverse operating conditions, and enhances RDF propulsion efficiency. A prototype is developed and tested to experimentally validate the efficacy of the proposed design approach.]]></description>
      <pubDate>Wed, 17 Jun 2026 16:13:03 GMT</pubDate>
      <guid>https://trid.trb.org/View/2665595</guid>
    </item>
    <item>
      <title>Electromagnetic Modeling and Investigation of High-Speed LIM Including Saturation and End Effect</title>
      <link>https://trid.trb.org/View/2665548</link>
      <description><![CDATA[A detailed, modified, physics-based, time-stepped finite-element analysis (FEA) model for single-sided linear induction motors (SLIMs) is presented. This model features a new graphical processing unit (GPU)-accelerated technique that accounts for primary movement within the time-stepped framework. This advancement enables the simulation of high-speed linear induction motors (LIMs) with an effectively infinite-length reaction plate, eliminating the need for remeshing. The FEA model considers the nonlinearity of ferromagnetic materials as well as the field nonlinearity arising from the primary motion. This proposed model addresses previous challenges in the electromagnetic modeling of LIMs, including limited track length, insufficient consideration of dynamic end effects, and high computational demands. The FEA approach couples the electrical, magnetic, and kinematic systems into a single multiphysics model. Consequently, the model allows for the prediction of machine performance overextended periods while maintaining manageable simulation times. The input–output data generated from the LIM simulations is utilized for parameter identification, and the FEA model is validated using a hardware prototype. The proposed model also calculates the attraction force and its effect on primary dynamics. The proposed model is compared with the state-space model in the context of the representation of dynamic end-effects at higher operating speeds.]]></description>
      <pubDate>Fri, 05 Jun 2026 16:41:22 GMT</pubDate>
      <guid>https://trid.trb.org/View/2665548</guid>
    </item>
    <item>
      <title>Calculation of Nonsinusoidal Power Supply for Toroidal-Winding Linear Induction Motor Considering Core Saturation Under Typical Air-Gap Flux Density</title>
      <link>https://trid.trb.org/View/2665523</link>
      <description><![CDATA[The electromotive force (EMF) and thrust density of the toroidal-winding linear induction motor (TWLIM) depend on the reasonable construction of the air-gap flux density, and the core saturation makes the TWLIM nonlinear seriously leading to the difficulty of calculating the supply voltage accurately. For this reason, an analytical method (AM) of calculating the nonsinusoidal power supply of the TWLIM considering core saturation under typical air-gap flux density is proposed. First, the maximum magnetic circuit model considering core saturation is established according to Ampere’s loop theorem, and the total magnetic voltage drop (MVD) of the maximum magnetic circuit is obtained by calculating the air-gap reluctance and core reluctance. Second, the secondary current density is derived from Maxwell’s equations and integrated to obtain the sum of secondary currents. Then, the sum of primary currents is derived from the maximum magnetic circuit equation, and the amplitude and phase of the fundamental and each harmonic are obtained by fast Fourier transform (FFT). Finally, the primary circuit is established, and the primary voltage is calculated from current and EMF of the primary. The proposed AM is validated through finite element analysis (FEA) and experiment. Based on the FEA and experimental results, the accuracy of the AM is verified.]]></description>
      <pubDate>Tue, 02 Jun 2026 13:56:53 GMT</pubDate>
      <guid>https://trid.trb.org/View/2665523</guid>
    </item>
    <item>
      <title>An Empirical Modeling of Open-Phase Fault and Auto-Derating Control Strategy for PC-FPIM in Electric Vehicle Propulsion</title>
      <link>https://trid.trb.org/View/2665518</link>
      <description><![CDATA[In this research article, an extensive investigation of fault-tolerant operation of pentagon-connected five-phase induction machine (PC-FPIM) with electric vehicle (EV) is propounded. To begin with, an empirical model of open-phase fault is proposed for PC-FPIM. Modeling is crucial in testing the heavy machineries in a nondestructive manner. Further, a hybrid fault-tolerant control strategy (HFTCS) by re-evaluating the reference currents is applied to achieve stable operation of the system by maintaining constant magneto-motive force (MMF). Despite HFTCS, the currents might exceed the rated value. Hence, an auto-derating control scheme (ADCS) which redefines the drive cycle is proposed. The pentagon connection is chosen as it is superior to that of star in terms of fault-tolerant capability. The operation of PC-FPIM is probed in the entire speed range of the vehicle, in both healthy as well as open-phase fault conditions with EV as load. The FPIM drive is tested under all the road-driving conditions with open-phase fault and it endures stable operation manifesting the efficacy of the FTCS applied. The simulations are carried in MATLAB/Simulink environment and the developed model is validated using an experimental prototype of 1.5 HP FPIM. The dSPACE DS1104 controller is used to realize the control algorithm. The experimental results are extensively discussed which are in compliance with the simulation results.]]></description>
      <pubDate>Mon, 01 Jun 2026 09:13:31 GMT</pubDate>
      <guid>https://trid.trb.org/View/2665518</guid>
    </item>
    <item>
      <title>Architectural Search-Driven Hybrid Pipelines for Diagnosing Incipient Stator Inter-Turn Faults in Induction Motor Under Power Quality Disturbances</title>
      <link>https://trid.trb.org/View/2665501</link>
      <description><![CDATA[Line-fed induction motors (IMs) are essential in transportation systems, powering traction systems, hoists, elevators, escalators, and conveyor belts. Stator inter-turn faults (SITFs) pose a significant threat, potentially causing severe motor damage and operational failures. Diagnosing SITFs is further complicated under practical conditions where IMs are subjected to power quality (PQ) disturbances, deviating from ideal balanced sine wave operation. This article presents two hybrid diagnostic machine learning (ML) pipelines, developed through architectural searches, for fault diagnosis in IMs operating under PQ disturbances. The first pipeline predicts the severity of SITFs using stator voltage and current signals, while the second estimates the type of PQ event-based solely on stator voltages. A dedicated experimental test-bed was developed to emulate various fault severities under various PQ disturbances and load conditions. The diagnostics pipelines achieved an R² score of 0.9699 on training and 0.9533 on testing for severity estimation. The threshold-based fault classification achieved accuracies of 96.92% (training) and 93.42% (testing). PQ event classification accuracies reached 98.87% and 96.71%, respectively. In addition, comparisons with models trained on raw time-series data were conducted to demonstrate the effectiveness of the proposed diagnostic approach. This research uniquely integrates domain knowledge with ML techniques to diagnose incipient SITFs in IMs under PQ disturbances—an area largely unexplored in existing literature.]]></description>
      <pubDate>Fri, 29 May 2026 14:09:16 GMT</pubDate>
      <guid>https://trid.trb.org/View/2665501</guid>
    </item>
    <item>
      <title>Multiscalar Control-Based Flux Optimization for Efficiency Improvement of Induction Motor</title>
      <link>https://trid.trb.org/View/2659262</link>
      <description><![CDATA[Constant flux control is typically used for induction motor (IM) drives to offer the same torque capabilities across all operating ranges. Unfortunately, the motor efficiency is low because this control system produces high stator current at light loads. The model-based techniques are superior to the traditional efficiency optimization algorithms in accuracy and fast response. In addition, an IM sensitivity to parameter variations still presents a challenge in the high-speed range to operate at maximum torque across the whole flux-weakening area. The energy efficiency of IM drives depends strongly on the choice of flux. This work proposes a novel online model-based maximum reactive power per flux controlling variable (MRPPFCV) control method to determine an optimum rotor flux level for efficiency optimization based on reactive power, resulting in improved system stability at low speeds and light loads. The new online model-based strategy, utilizing the multiscalar control principle of IM drives, is introduced to optimize rotor flux, ensuring maximum torque operation while maintaining robustness to parameter variations in the flux-weakening region. This method calculates the reference flux controlling variable online and does not use a lookup table. The primary contribution is designing a straightforward and simple yet effective online flux optimization method to enhance performance, allowing the flux to be modified based on motor requirements for energy efficiency. Consequently, the proposed approach is resilient to parameter variations. The real-time implementation of the proposed MRPPFCV technique is tested on a 5.5 kW IM drive in the laboratory. The simulations and experimental results demonstrate the effectiveness and validity of the proposed technique.]]></description>
      <pubDate>Tue, 26 May 2026 11:56:25 GMT</pubDate>
      <guid>https://trid.trb.org/View/2659262</guid>
    </item>
    <item>
      <title>Real-Time Rotor Temperature Estimation of Induction Machines Using a Differentiated Input Thermal Neural Network</title>
      <link>https://trid.trb.org/View/2659259</link>
      <description><![CDATA[Monitoring the rotor temperature of the driving machine is crucial for the safety and performance of electric vehicles (EVs). The thermal neural network (TNN) integrating deep learning with the thermal resistance model is a state-of-the-art (SOTA) method for rotor temperature estimation. However, TNN does not actively select the influencing factors in the thermal parameter identification process, which limits both the accuracy and generalization performance of the model. Meanwhile, the multi-dimensional network input makes it difficult to be directly deployed in the embedded control system with limited computing power. Therefore, this article proposes a differentiated input TNN (DI-TNN) for real-time rotor temperature estimation of induction machines. First, a simplified yet concise lumped-parameter thermal network (LPTN) is established according to the structure of the oil-cooled induction machine, and the actual influencing factors of each thermal network parameter are obtained based on physical mechanisms. Subsequently, a DI-TNN model consisting of multiple differentiated input networks (DI-Networks) is proposed, and a feasible loss function is constructed for model training. Furthermore, the thermal parameters corresponding to various operating conditions are traversed using the trained model, and a lookup table-based real-time temperature estimation method is deployed into an embedded system. Finally, a rotor temperature dataset covering a wide range of operating conditions is collected through bench tests, and experiments are conducted to compare and validate the DI-TNN against the TNN. The results demonstrate that the DI-TNN offers substantial improvements in the interpretability of thermal parameter identification, the accuracy of rotor temperature estimation, and its applicability in embedded deployment.]]></description>
      <pubDate>Tue, 26 May 2026 11:56:24 GMT</pubDate>
      <guid>https://trid.trb.org/View/2659259</guid>
    </item>
    <item>
      <title>Efficiency Optimization Strategy of Induction Motor Based on Electromagnetic and Reactive Torque Control</title>
      <link>https://trid.trb.org/View/2659243</link>
      <description><![CDATA[This article proposes an efficiency optimization strategy for the induction motor (IM) based on electromagnetic and reactive torque control (ERTC). The application of ERTC better decouples flux and torque, directly controls torque for rapid and stable dynamic regulation, and cuts dynamic losses under various conditions. On this basis, an optimal rotor flux based on the loss model controller (LMC) is computed to achieve real-time efficiency optimization of the motor. Meanwhile, to alleviate the deceleration of system response speed caused by the dynamic flux variations during the process of optimizing efficiency, the model-free predictive ERTC (MFPERTC) is presented. MFPERTC further improves the comprehensive performance between high efficiency and rapid, stable response, demonstrating stronger robustness than traditional model predictive control. It makes certain contributions to the development of electric vehicles (EVs) and the industrial standardization of predictive control.]]></description>
      <pubDate>Tue, 26 May 2026 11:56:24 GMT</pubDate>
      <guid>https://trid.trb.org/View/2659243</guid>
    </item>
    <item>
      <title>Synchronous Optimal Hybrid PWM Technique With Smooth Pulse Pattern Transition Strategy for IM-Based High-Power Railway Traction Drive</title>
      <link>https://trid.trb.org/View/2659210</link>
      <description><![CDATA[Two-level voltage source inverter (2L-VSI) fed high-power induction motor (IM) drives are popularly used for railway traction applications because of high reliability and robustness. The switching frequency (fsw) of the 2L-VSI in such high-power drives is maintained below 500 Hz to curb switching energy losses. Therefore, synchronized pulsewidth modulation (PWM) techniques are utilized. Here, the ratio between fsw and the fundamental frequency (fₘ) [termed pulse number, i.e., P = (fsw/fₘ)] is maintained as an odd integer. A synchronous optimal hybrid (SOH) PWM technique, combining synchronous sine triangle (SST) and synchronous optimal (SO) PWMs, is proposed in this article for high-power IM drives while restricting fsw  below 350 Hz. SST PWM with P = 21, 15, 9 is utilized for low and medium ranges of fₘ, whereas SO PWM having P = 9, 7, 5 is deployed for medium and high ranges of fₘ. Implementation of the proposed PWM poses a challenge due to two major factors: dependency on lookup table-based offline solution for optimal switching angles and sudden change in pulse pattern (PP) at different values of P. This article proposes a linear equation-based method to compute optimal switching angles on the basis of modulation index (M). Thus, lookup tables are no longer required to be stored in a microcontroller. Furthermore, a change in PP, if done in an unconstrained manner, leads to severe transients in the line currents and electromagnetic torque developed by the motor. Hence, a stator flux vector-based generalized pulse transition strategy is proposed for smooth transition. Effectiveness of these propositions is validated through simulations on a 2.2-kV, 850-kW, and 65-Hz IM drive and experiments on a scaled-down laboratory prototype of IM drive rated at 415 V, 3.7 kW, and 65 Hz.]]></description>
      <pubDate>Tue, 26 May 2026 11:56:24 GMT</pubDate>
      <guid>https://trid.trb.org/View/2659210</guid>
    </item>
    <item>
      <title>Harmonic Current Injection-Based Online Rotor Time Constant Estimation Method for IM</title>
      <link>https://trid.trb.org/View/2665472</link>
      <description><![CDATA[Accurate rotor time constant (RTC) is the key to achieving high-performance induction motor (IM) drives. To obtain an accurate RTC, a new robust online RTC estimation method based on harmonic current injection is presented. In the proposed method, the RTC is estimated from the speed response corresponding to the injected harmonic current. Compared with the existing schemes, the parameter mismatches will not influence the steady-state identification results of RTC. Thus, it has stronger robustness to parametric uncertainties. Finally, the effectiveness and correction of the proposed method are verified by simulations and experiments.]]></description>
      <pubDate>Tue, 26 May 2026 11:56:23 GMT</pubDate>
      <guid>https://trid.trb.org/View/2665472</guid>
    </item>
    <item>
      <title>Modified Multivector Predictive Current Control for High-Performance EV Induction Motor Drives</title>
      <link>https://trid.trb.org/View/2659190</link>
      <description><![CDATA[Predictive current control (PCC) is an effective technique for managing constrained nonlinear systems requiring high dynamic performance, especially in industrial and transportation applications. This article presents a robust multivector (MV) PCC scheme for electric vehicle (EV) induction motor (IM) drives, designed to maintain a fixed switching frequency with improved performance. The scheme combines symmetric three-vector PCC with slip-speed based dwelling time to introduce a simplified voltage vector (VV) preselection method, covering all operating modes and ensuring accurate reference voltage tracking, enhanced sector identification, and reduced computational complexity. Additionally, a Maximum Torque Per Ampere (MTPA) strategy is integrated to extend the speed range, ensuring efficient performance both below and above the rated speed while maintaining voltage constraints. Experimental results demonstrate the proposed scheme’s superior dynamic response, computational efficiency, and wide-speed adaptability for EV using extra-urban driving cycle (EUDC) driving scheme.]]></description>
      <pubDate>Thu, 23 Apr 2026 13:54:25 GMT</pubDate>
      <guid>https://trid.trb.org/View/2659190</guid>
    </item>
    <item>
      <title>FCS-MPC Based IM-PMSM Dual-Motor System with Torque Optimal Control</title>
      <link>https://trid.trb.org/View/2113887</link>
      <description><![CDATA[This paper proposes a finite control set model predictive control (FCS-MPC) based dual-motor control strategy for induction motor (IM) and permanent magnet synchronous motor (PMSM). By establishing a dual-motor current prediction model, the unified cost function and optimization strategy are constructed, and the torque synchronization constraint and total torque ripple suppression constraint are designed. Compared with the traditional control strategy, the proposed control algorithm verifies the feasibility of FCS-MPC in the IM-PMSM dual-motor system. Moreover, the proposed strategy has good transient performance, and realizes the torque synchronous control and total torque ripple suppression of the dual-motor system.]]></description>
      <pubDate>Tue, 21 Apr 2026 08:28:11 GMT</pubDate>
      <guid>https://trid.trb.org/View/2113887</guid>
    </item>
    <item>
      <title>Adaptive Fast Super-Twisting Sliding Mode Direct Thrust Control for Linear Induction Machine Adapted to Linear Metro</title>
      <link>https://trid.trb.org/View/2659175</link>
      <description><![CDATA[This article proposes an adaptive fast super-twisting (AFST) sliding mode control (SMC) strategy based on direct thrust control (DTC), referred to as AFST-DTC, for linear induction machine (LIM) drive systems used in linear metro applications. First, the AFST technique is designed and implemented on LIM to achieve rapid response, precise speed tracking, and robustness against load disturbances. The proposed AFST-DTC method demonstrates fast convergence when the sliding mode state is near or far from the designed sliding mode surface, with reduced chattering phenomenon compared to the conventional super-twisting under DTC (ST-DTC) method. Second, the convergence time is calculated, and the stability of the control system is verified using the Lyapunov function. Finally, comprehensive simulation and experimental results on a prototyped 3 kW arc induction machine (AIM) have fully demonstrated the effectiveness and advantages of the AFST control method, including faster transient response, better steady-state and dynamic performance, and lower thrust and flux ripple, compared to those of ST-DTC and conventional DTC (CDTC), respectively.]]></description>
      <pubDate>Mon, 20 Apr 2026 09:24:40 GMT</pubDate>
      <guid>https://trid.trb.org/View/2659175</guid>
    </item>
    <item>
      <title>Oscillation Analysis and Mitigation of Induction Motors Controlled by Slip Frequency Control</title>
      <link>https://trid.trb.org/View/2511564</link>
      <description><![CDATA[In slip frequency control (SFC) mode, induction motor is prone to oscillation under low-speed and high-load conditions. To address this issue, the small-signal model of the induction motor system controlled by SFC is built first. Based on the eigenvalue analysis, the speed oscillation mechanism of the induction motor is revealed theoretically. Then, a motor speed-based flux compensation strategy is proposed, which can improve system stability and suppress speed oscillation. Meanwhile, an inertia frequency controller with anti-interference capability is proposed, which can smooth motor speed and damp speed overshoot. By combining the proposed flux compensation strategy and inertia frequency controller, the dynamic and steady-state performance of the induction motor system can be improved significantly. Compared with the typical SFC, the low-speed carrying capacity of the induction motor system under the proposed control strategy is significantly better. Finally, the theoretical analysis and proposed control are verified through simulation and experimental studies.]]></description>
      <pubDate>Sat, 28 Feb 2026 17:16:53 GMT</pubDate>
      <guid>https://trid.trb.org/View/2511564</guid>
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