Question: Design and thermal evaluation of high-torque, low-speed, fractional-slot, concentrated-winding in-wheel motors for traction applications.

Exploring the Prospects and Pitfalls of Fractional-Slot Concentrated-Winding Synchronous Permanent Magnet Motors

In recent years, fractional-slot concentrated-winding (FSCW) synchronous permanent magnet (PM) motors have emerged as a promising technology due to their inherent benefits. These advantages encompass enhanced power density, efficiency, reduced end-turn lengths, increased slot fill factor, particularly when integrated with segmented stator designs, diminished cogging torque, flux-weakening capacity, and robust fault tolerance. This paper aims to comprehensively dissect the potential and hurdles associated with FSCW synchronous PM motors.

The paper delves into the theoretical underpinnings and design principles of FSCW synchronous PM motors, with a focus on achieving high power density and flux-weakening capabilities. It also compares single-layer versus double-layer winding configurations, examines rotor losses under fault-tolerant conditions, and scrutinizes parasitic effects. Furthermore, the paper contrasts interior versus surface-mounted PM motor designs and discusses various machine types.

Additionally, the paper compiles a list of commercial applications that have adopted FSCW synchronous PM motors, illustrating their practical significance and broad applicability in the industry. This detailed analysis serves as a valuable resource for engineers and researchers seeking to understand the intricacies and potential of FSCW synchronous PM motors in the realm of electric propulsion systems.

Eddy current losses in wireless charging systems for electric vehicles must be minimized in consideration of constraints like the available space, the strand diameter in litz wire and the cost. A mathematical model for eddy current losses is needed to find the optimum geometrical design for a given application. This paper gives the fundamental equations to calculate skin effect, proximity effect and dc losses in litz wire. Litz wire consists of strands, which are twisted to form multiple bundles. Therefore eddy current losses occur not only on the strand level but the bundle level as well. The losses caused by different effects and bundle levels are compared, and the influence of strand diameter and strand number on the total loss is examined. Finally, aluminum litz wire is analyzed. It becomes apparent that the overall loss can be reduced with the aid of replacing copper litz wire by aluminum in some areas of application. Material choice and geometrical design require detailed knowledge of the systems nominal operation point.

This paper describes an optimal regenerative braking control scheme for a permanent magnet brushless DC motor of an electric motorcycle to achieve dual goals of the electric brake and the maximal power harvest in two cases of the road, downhill and flat, without any additional changes on the hardware. Based on regenerative braking of brushless DC motor in half bridge modulation mode, the relationship between average regenerative current on the DC bus and PWM duty cycle under different speeds was studied intensively from the three aspects of theoretical deduction, simulation based on Matlab/Simulink and platform experiments, and then a simple but effective scheme considering the actual application was proposed tentatively to achieve the dual goals. Since the braking kinetic energy is harvested in the form of the electric energy stored up in battery, the scheme could improve the driving range, safeness, noise preventability and cost effective of electric motorcycles.

Permanent magnet linear synchronous motor (PMLSM) has several advantages, such as simple structure, small volume, high force, and so on. It is a research focus and difficulties in the research field of motor. Because PMLSM is different from the traditional rotary motors in structure and running state, it is difficult for to build its precise mathematical model by analytical method. The asymmetry of the three-phase winding electromagnetic and electrical parameters of open PMLSM in a low-speed working zone is impossible to take into account. In this paper, the difference between the permanent magnet linear synchronous motor and traditional motors are analyzed. And then, a non-linear, asymmetric and variable parameter space state model is built, considering the structural and running characteristics of PMLSM. The proposed model needs all kinds of parameters of PMLSM, including selfinductance, mutual inductance, flux linkage, etc. So, a finite element calculation and actual measurement of the required parameters of a prototype PMLSM are done, respectively. The influence of air-gap on the inductance parameters was analyzed.

New application areas are demanding the development of ultra-high speed electrical machines. Significant challenges exist to produce a test bench that is capable of withstanding operating speeds exceeding 500,000 rpm and measuring very low torque values of mNm. This paper describes a purpose built test bench that is able to characterize the operation of an ultra-high speed drive system. Furthermore, the calculation of electromagnetic losses, air friction and critical speeds is presented and a comparison of analytical and experimental results is given. The ultra-high speed machine has an efficiency of 95%, however, in the upper speed ranges the frictional losses become dominant

The universal or ac commutator motor, widely used in hand tools and domestic appliances generally uses a two-pole stator with a concentrated winding and an armature with interlocked coils elements. The copper volume and the axial length of the end windings of such conventional structures are then usually very important. In this paper, the authors present a new universal motor structure based on an efficient use of the isotropic magnetic properties of the soft magnetic composites and on the concentrated winding technique. The stator core presents a claw-pole structure and the armature has a concentrated winding with several coils wound around the same tooth. With this new ac commutator motor structure, a reduction of the total volume by a ratio equal to 200% is obtained when compared to a classical universal motor structure with nearly identical performance.

The development of ship propulsion synchronous motors with high temperature superconductor (HTS) field windings for Naval electric ship applications has progressed to the point where a full scale motor is now under construction. A 5 MW, 230-rpm prototype ship propulsion motor was built and tested by the Center for Advanced Power Systems (CAPS) on behalf of U.S. Office of Naval Research (ONR). It met or exceeded all its design goals. Currently, a 36.5 MW, 120-rpm ship propulsion motor is being built for delivery to ONR at the end of 2006. This paper presents test results of the 5 MW motor and the status of the 36.5 MW motor

This paper presents a numerical method for the estimation of induction motor double-cage model parameters from standard manufacturer data: full load mechanical power (rated power), full load reactive electrical power, breakdown torque, and starting current and torque. A model sensitivity analysis for the various electrical parameters shows that stator resistance is the least significant parameter. The nonlinear equations to be solved for the parameters determination are formulated as a minimization problem with restrictions. The method has been tested with 223 motors from different manufacturers, with an average value of the normalized residual error of 1.39/spl middot/10/sup -2/. The estimated parameters of these motors are graphically represented as a function of the rated power.

Classical direct torque control (DTC) has advantage in absence of coordinate transform and voltage modulator block. However, it may have disadvantage in controlling electromagnetic torque. DTC produces high ripple in electromagnetic torque as it is not directly controlled. High torque ripple causes vibrations to the motor which may lead to component lose, bearing failure or resonance. Thus, Fuzzy Logic controller is applied to reduce electromagnetic torque ripple. This paper presents the simulation analysis of Fuzzy Logic Direct Torque Controller (FLDTC) of induction machine drives. However, only the design of the controller using built in DTC induction motor drive model of Simulink AC4 will be discussed in this paper. Using FLDTC, the resulting electromagnetic torque from produced less ripple than classical DTC.

The paper presents the concept and results of simulation and experimental research of a claw pole generator with hybrid excitation. Hybrid excitation is performed with a conventional coil located between two parts of the claw-shape rotor and additional permanent magnets which are placed on claw poles. Within the research first a simulation and next constructed experimental model has been developed on the basis of the mass-produced vehicle alternator. Experimental researches have shown that - at a suitable rotational speed - it is possible to self-excite of the generator without any additional source of electrical power.

The paper discusses the influence of driving cycles on the design optimization of permanent magnet synchronous machines. A bi-objective design optimization is presented for synchronous machines with V-shaped buried magnets. The machine length and the loss energy over a given driving cycle are defined as objectives. A total of 14 parameters defining geometry and winding layout are chosen as design parameters. Additionally some constraints like speed-torque-requirements and minimal stray field bridge widths are set. The optimization problem is solved using 2D finite element analysis and a high-performance differential evolution algorithm. The analyses are performed for the ARTEMIS driving cycle due to the more realistic driving behavior in comparison to the most commonly used New European Driving Cycle. Furthermore, a reference design optimization against the rated point loss energy is presented. The results show a much better performance of the driving cycle optimized machines in comparison to the rated point optimized machines in terms of the cycle-based loss energy. Loss savings depend strongly on the machine length and are approximately in between 15% and 45%.


Question: Design and thermal evaluation of high-torque, low-speed, fractional-slot, concentrated-winding in-wheel motors for traction applications.

This study delves into the comparative analysis of Surface-Mounted Permanent Magnet (SPM) and Interior Permanent Magnet (IPM) motor drives for their suitability in Electric Vehicle (EV) applications. EVs typically employ permanent magnet synchronous motors for their high torque density and operational efficiency. The investigation focuses on contrasting the performance characteristics of IPM and SPM motors, specifically under the constraints of predefined inverter ratings. A simplified analytical method is introduced and its outcomes are corroborated by the design of actual motors, with validation through Finite Element Analysis (FEA). A key finding of this analysis is the distinct response of IPM and SPM motors under overload conditions: as anticipated, an overload in current leads to a power overload in IPM motors, whereas in SPM motors, the maximum power output remains capped, irrespective of the current overload, showcasing the unique operational limits of each motor type under extreme conditions.

Although the concept of variable-speed drives, based on utilization of multiphase machines, dates back to the late 1960s, it was not until the mid- to late 1990s that multiphase drives became serious contenders for various applications. These include electric ship propulsion, locomotive traction, electric and hybrid electric vehicles, ldquomore-electricrdquo aircraft, and high-power industrial applications. As a consequence, there has been a substantial increase in the interest for such drive systems worldwide, resulting in a huge volume of work published during the last ten years. An attempt is made in this paper to provide a brief review of the current state of the art in the area. After addressing the reasons for potential use of multiphase rather than three-phase drives and the available approaches to multiphase machine designs, various control schemes are surveyed. This is followed by a discussion of the multiphase voltage source inverter control. Various possibilities for the use of additional degrees of freedom that exist in multiphase machines are further elaborated. Finally, multiphase machine applications in electric energy generation are addressed.

This paper presents a novel topology of dual rotor radial flux-BLDC motor with Halbach array magnets as an efficient solution for drives. Energy efficient motors are the need of day for electric vehicles, industrial and consumer products. Especially this concept is arrived to cater for the requirements of electrical vehicle. Due to advent of high permeability, high saturation magnetic materials, high energy product permanent magnets, BLDC motors are suitable candidate for electric vehicle propulsion. Traditionally, outer rotor BLDC motor is a wide choice which is being used in commercially available electric vehicles. New topologies of BLDC motors are being proposed by research activities which aim at improvement of torque density of motors. Dual rotor brushless DC machine (DR-BLDC) is one of the consequences of such research and claimed to have very high torque density and power density. In this paper, a new topology of Dual Rotor Halbach (DR-HB) BLDC motor is proposed which is aimed to increase the torque density of motor. The complete design of DR-HB BLDC motor is carried out and compared with DR-BLDC of the same specifications and volume. The obtained performance characteristics of the proposed design are very much encouraging in terms of torque density.

The aim of this paper is to investigate and compare the performances of two induction motors and two transverse laminated synchronous reluctance motors (induction motor nameplate data: 2.2 and 4 kW, 380 V, 50 Hz, 4 pole). Each induction motor is compared with a synchronous reluctance motor, that has the same stator lamination and winding, but, obviously, different rotor. The analysis has been based both on an analytical and experimental approach. The results have shown that the synchronous reluctance motor, compared to the induction motor, is capable of around a 10% to 15% larger rated torque for a given frame size. The direct comparison of the performances of the motor types has been made both for constant load and constant dissipated power conditions.

The Tri-Wheel is a novel wheel-leg locomotion concept inspired by work with first responders. Through its two modes of operation-Driving Mode and Tumbling Mode- this mechanism is able to both drive quickly on smooth surfaces at roughly 1.7 times desired speed and climb objects as tall as 67% of the diameter of the mechanism. The unique gearing configuration that facilitates these dual capabilities is described, and testing quantifies that this nonprecision gearing system is roughly 81% efficient in a worst-case scenario of loading. This work introduces the Tri-Wheel concept and provides preliminary testing to validate its predicted operating characteristics.

This paper examines the iron loss characteristics of a high-flux interior permanent-magnet machine. The machine was designed as a concept demonstrator for a 6-kW automotive alternator and has a wide field-weakening range. Initial experimental results revealed a high iron loss during field-weakening operation. Finite-element analysis was used to investigate the cause of the high iron losses and to predict their magnitude as a function of speed. The effects of changes in the machine design were examined in order to reduce iron losses and hence improve the machine performance

This paper investigates the performance of a three designs of tubular linear permanent magnet motors (TLPMMs) with quasi-Halbach magnetized moving-magnet armature and slotted stator with a single-slot. The motors were developed for reciprocating compressor applications such as household refrigerators. The three designs have different magnet shape structures, and the stator core of all designs have employed a soft magnetic composite (SMC) material, Somaloy 700. Based on finite element analysis (FEA) an extensively analysis on the opencircuit magnetic field distribution, back-EMF, thrust force, fluxlinkage, winding inductance and cogging force in the three designs have been established. The simulation results indicate the effectiveness of the designs to drive a linear reciprocating compressor in household refrigerators, and also the simulation results have shown the superiority of the two designs with T-shape and trapezoidal magnet configurations, over the TLPMM with conventional rectangular magnet. Moreover, the permanent magnets (PMs) array arrangement has significant effects on the motor performance.

This paper deals with the approach of analyzing the magnet design based on transient behavior of an IPMSM during switching processes in the event of malfunction for automotive applications. Depending on the maximal current increase during the transient process the needed percentage of Dysprosium for the magnet design is conducted. Switching off strategy is introduced for both Voltage-Source- and Current-Source-Inverter for automotive application. Both inverters are compared concerning the transient current increase and respectively the Dy-content for the magnet design.

The paper discusses the influence of driving cycles on the design optimization of permanent magnet synchronous machines. A bi-objective design optimization is presented for synchronous machines with V-shaped buried magnets. The machine length and the loss energy over a given driving cycle are defined as objectives. A total of 14 parameters defining geometry and winding layout are chosen as design parameters. Additionally some constraints like speed-torque-requirements and minimal stray field bridge widths are set. The optimization problem is solved using 2D finite element analysis and a high-performance differential evolution algorithm. The analyses are performed for the ARTEMIS driving cycle due to the more realistic driving behavior in comparison to the most commonly used New European Driving Cycle. Furthermore, a reference design optimization against the rated point loss energy is presented. The results show a much better performance of the driving cycle optimized machines in comparison to the rated point optimized machines in terms of the cycle-based loss energy. Loss savings depend strongly on the machine length and are approximately in between 15% and 45%.

Switched reluctance machines have emerged as an important technology in industrial automation; they represent a real alternative to conventional variable speed drives in many applications. This paper reviews the technology status and trends in switched reluctance machines. It covers the various aspects of modeling, design, simulation, analysis, and control. Finally, it discusses the impact of switched reluctance machines technology on intelligent motion control.

This paper examines the electrical properties of a rotating transformer used for contact-less transfer of energy to rotating equipment. Two winding layouts are analysed theoretically and experimentally. The reluctance modelling provides a deep understanding of how the geometry of the core and windings affect the electrical behaviour of the component. Theoretical calculations, measured results and finite element analysis are used to compare the proposed layouts. Basic design guidelines are given to adjust the leakage and magnetising inductances. The selection of a phaseshifted full bridge converter is suggested for applications at the power level of 1kW.


Question: Design and thermal evaluation of high-torque, low-speed, fractional-slot, concentrated-winding in-wheel motors for traction applications.

This document introduces an innovative universal motor design utilizing soft magnetic composites, which significantly improves efficiency and reduces dimensions. Traditionally, universal or AC commutator motors, common in household appliances and power tools, feature a two-pole stator with a concentrated winding and an armature comprised of interlocked coil elements. However, this conventional design often leads to substantial copper volume and considerable axial length of the end windings. The authors propose a novel motor structure that capitalizes on the isotropic magnetic properties of soft magnetic composites and employs concentrated winding techniques. The stator core adopts a claw-pole configuration, while the armature is equipped with a concentrated winding that involves multiple coils wrapped around a single tooth. This advanced AC commutator motor structure achieves a remarkable 200% reduction in total volume compared to traditional universal motor designs, while maintaining comparable performance. This breakthrough in motor design represents a significant advancement in the field of electrical engineering, particularly in the context of optimizing motor efficiency and size for practical applications.

This paper describes the developed three-phase 6/8 poles switched reluctance external rotor motor drive for a fan in air conditioner. The external rotor core structure and the internal stator core structure, the three-phase windings arrangement, the slotted claw, and the setting structure of the photoelectric transducers on the rotor position detector are illustrated. The electromagnetic field calculation results are given. The three-phase asymmetric bridge power converter was used in the drive system. The block diagram of the switched reluctance external rotor motor drive with closed-loop rotor speed control is given. The closed-loop rotor speed control is implemented using a fuzzy logic algorithm. The experimental tests of the developed prototype are made for driving the fan from 200 to 950 r/min. The comparison results of the two systems show that the input power and input phase current RMS value are lower in the developed three-phase 6/8 poles external rotor switched reluctance motor drive prototype with the fan than those in the induction motor variable-frequency variable-speed drive with the fan.

This paper presents the design and implementation of a field-programmable gate array (FPGA)-based architecture for the speed control of sensorless permanent-magnet synchronous motor (PMSM) drives. For the reduction of computation resources, as well as accuracy improvement in the rotor position estimation, a parallel reduced-order extended Kalman filter (EKF) is proposed in this work. Compared with an EKF, the system order is reduced and the iteration process is greatly simplified, resulting in significant savings of resource utility, while maintaining high estimation performance. The whole control system includes a current-control-and-coordinate-transformation unit, a proportional-integral (PI) speed controller, and other accessory modules, all implemented in a single FPGA chip. A hardware description language is adopted to describe advantageous features of the proposed control system. Moreover, the finite-state-machine method is applied with the purpose to reduce logic elements used in the FPGA chip. The validity of the approach is verified through simulation based on the Modelsim/Simulink cosimulation method. Finally, experimental results are obtained on an FPGA platform with an inverter-fed PMSM to show the feasibility and effectiveness of the proposed system-on-programmable-chip for PMSM drives.

This paper proposes an online parameter estimation method based on a discrete-time dynamic model for the interior permanent-magnet synchronous motors (IPMSMs). The proposed estimation technique, which takes advantage of the difference in dynamics of motor parameters, consists of two affine projection algorithms. The first one is designed to accurately estimate the stator inductances, whereas the second one is designed to precisely estimate the stator resistance, rotor flux linkage, and load torque. In this paper, the adaptive decoupling proportional-integral (PI) controllers with the maximum torque per ampere operation, which utilize the previously identified parameters in real time, are chosen to verify the effectiveness of the proposed parameter estimation scheme. The simulation results via MATLAB/Simulink and the experimental results via a prototype IPMSM drive system with a TI TMS320F28335 DSP are presented under various conditions. A comparative study with the conventional decoupling PI control method is carried out to demonstrate the better performances (i.e., faster dynamic response, less steady-state error, more robustness, etc.) of the adaptive decoupling PI control scheme based on the proposed online parameter estimation technique.

This paper investigates the significance of thermal management energy consumption on the range of electric vehicles. It is necessary to combine consumption models of powertrain components such as the electric machine, power electronics and high voltage battery that take into account trip-based information containing a predicted speed profile and elevation data with a thermal management system that has the ability to incorporate predicted thermal losses of the powertrain. The thermal management system refers to the sum of all components within the refrigeration cycle of the vehicle as well as the associated control strategy. Based on the results of a real world drive study in an electric vehicle, it is demonstrated, that the range uncertainties introduced by the consumption of the thermal management are significant and should be considered when developing predictive range estimation algorithms. These uncertainties are further investigated by an energy flow based thermal system model. In order to minimize range uncertainties, a system architecture for the prediction of thermal management consumption is proposed.

This paper introduces a model-based approach to estimating longitudinal wheel slip and detecting immobilized conditions of autonomous mobile robots operating on outdoor terrain. A novel tire traction/braking model is presented and used to calculate vehicle dynamic forces in an extended Kalman filter framework. Estimates of external forces and robot velocity are derived using measurements from wheel encoders, IMU, and GPS. Weak constraints are used to constrain the evolution of the resistive force estimate based upon physical reasoning. Experimental results show the technique accurately and rapidly detects robot immobilization conditions while providing estimates of the robot's velocity during normal driving. Immobilization detection is shown to be robust to uncertainty in tire model parameters. Accurate immobilization detection is demonstrated in the absence of GPS, indicating the algorithm is applicable for both terrestrial applications and space robotics.

In this paper, system integration and power-flow management algorithms for a four-wheel-driven series hybrid electric vehicle (HEV) having multiple power sources composed of a diesel-engine-based generator, lead acid battery bank, and supercapacitor bank are presented. The super-capacitor is utilized as a short-term energy storage device to meet the dynamic performance of the vehicle, while the battery is utilized as a mid-term energy storage for the electric vehicle (EV) mode operation due to its higher energy density. The generator based on an interior permanent magnet machine (IPMM), run by a diesel engine, provides the average power for the normal operation of the vehicle. Thanks to the proposed power-flow management algorithm, each of the energy sources is controlled appropriately and also the dynamic performance of the vehicle has been improved. The proposed power-flow management algorithm has been experimentally verified with a full-scale prototype vehicle.

Electric motor design is a multi-variable problem which involves geometric dimensions of the stator and rotor. Presenting a unique solution for a family of optimization criteria has always been a challenge for motor designers. Several numerical tools such as finite element methods (FEM) have been developed to perform a precise analysis and predict the outcome of the design. However, limits in parametric analysis as well as mathematical and computational burden on numerical tools usually prohibit the designer in obtaining a unique solution for the design problem. These limits and demands in optimized solutions motivate the designer to use analytical models in order to perform a comprehensive parametric design. An accurate analytical model is crucial for this purpose. In this paper, an analytical model for permanent magnet assisted synchronous reluctance motor (PMa- SynRM) with four flux barriers and one cutout per pole is developed. Flux densities are found in the air gap, in the cutouts, and in the flux barriers; thus, the back-EMF developed by the permanent magnets is derived. Equations for the d-axis and the q-axis inductances are also obtained. Electromagnetic torque is finally derived using the co-energy method. The developed analytical model highlights the contribution of the reluctance variation and permanent magnets on the developed torque. Simulation results are obtained using both Matlab and Ansoft/Maxwell packages. These outcomes are supported by the experimental results obtained from a laboratory test bed.

Improved back EMF detection circuits for low voltage/low speed and high voltage sensorless BLDC motor drives are presented in this paper. The improvements are based on the direct back EMF sensing method from our previous research work described in reference, which describes a technique for directly extracting phase back EMF information without the need to sense or re-construct the motor neutral. The reference method is not sensitive to switching noise and requires no filtering, achieving much better performance than traditional back EMF sensing scheme. A complementary PWM (synchronous rectification) is proposed to reduce the power dissipation in power devices for low voltage applications. In order to further extend the sensorless BLDC system to lower speed, a pre-conditioning circuit is proposed to amplify the back EMFs at very low speed. As a result, the brushless DC motor can run at lower speed with the improved back EMF sensing scheme. On the other hand, another improved detection circuit is presented for high voltage applications to overcome the delaying problem caused by large sensing resistors. The detailed circuit models are analyzed and experimental results verify the analysis.

This paper presents a torque-maximizing design of a double-stator, axial-flux PM machine using a simple non-linear magnetic analysis. The proposed analysis consists of a geometric- flux-tube-based equivalent-magnetic-circuit model. The model can take into account not only the magnetic saturation but also the three-dimensional(3D) flux distribution. The proposed analysis has realized a dramatic reduction in the computation time compared to 3D-FEA, keeping reasonable analytical accuracy. At first, the validity of the proposed analysis is examined for the various design parameters. After verifying the accuracy of the torque computation of the proposed analysis through the comparisons with 3D-FEA, an 8-pole axial-flux PM machine with double-stator-single-rotor is optimally designed. The performance of the optimized motor are finally verified by 3D-FEA.

New application areas are demanding the development of ultra-high speed electrical machines. Significant challenges exist to produce a test bench that is capable of withstanding operating speeds exceeding 500,000 rpm and measuring very low torque values of mNm. This paper describes a purpose built test bench that is able to characterize the operation of an ultra-high speed drive system. Furthermore, the calculation of electromagnetic losses, air friction and critical speeds is presented and a comparison of analytical and experimental results is given. The ultra-high speed machine has an efficiency of 95%, however, in the upper speed ranges the frictional losses become dominant


Question: Design and thermal evaluation of high-torque, low-speed fractional-slot concentrated windings for in-wheel traction motors.

This paper offers an in-depth analysis of the high-speed performance attributes of four distinct types of synchronous permanent magnet (PM) machines, designed for applications necessitating extensive constant-power speed range (CPSR) operation. The machines under scrutiny encompass surface PM machines equipped with both distributed and fractional-slot concentrated windings, alongside two interior PM machine variants with distributed windings. Among the interior PM machines, one model is engineered with a stringent limit on the back-emf voltage at maximum speed, while the other operates without this constraint. The primary application targeted is an automotive direct-drive starter/alternator, demanding an expansive 10:1 CPSR. The paper provides comprehensive performance comparisons of these machines, encompassing critical aspects like the back-emf voltage at peak speed, total mass and cost, and eddy current losses in the magnets. Analytical findings are corroborated through finite element analysis (FEA). The research culminates in the formulation of design guidelines to assist engineers in selecting the most appropriate machine type for high-CPSR applications, alongside a discussion on the associated compromises for each machine type.

In this paper, grid-connected interleaved voltage source inverters for PMSG wind power generation system with coupled inductors is introduced. In parallel operation, the undesirable circulating current flows between inverters. There are some differences in circulating currents according to source configuration. It is mathematically analyzed and simulated for the verification of analysis. In case of interleaved inverters, the uncontrollable circulating current in switching frequency level flows between inverter modules regardless of source configuration such as common or separated if filter inductance which is placed between inverters is significantly low. In order to suppress high frequency circulating current, the coupled inductors are employed in each phase. A case of 1.5MVA grid-connected interleaved inverters using coupled inductors prototype for the PMSG wind power generation Back To Back converters in parallel are introduced and experimentally verified the proposed topology.

A brief design review of the Permanent Magnet Synchronous Motors has been presented. A procedure has been developed to predict the steady state and dynamic performances of a brushless permanent magnet synchronous motor. Finite element analysis has been combined with a lumped parameter circuit model in order to provide satisfactory engineering information. To this end, two coordinated steps are involved. One is to develop a unified lumped parameter circuit model for both steady state and dynamic analysis. The second step is to extract the individual lumped parameters from finite element solutions based on corresponding equivalent circuits, each with a pre-determined topology. The proposed techniques have been experimentally verified in a laboratory permanent magnet synchronous motor.

This paper proposes an online parameter estimation method based on a discrete-time dynamic model for the interior permanent-magnet synchronous motors (IPMSMs). The proposed estimation technique, which takes advantage of the difference in dynamics of motor parameters, consists of two affine projection algorithms. The first one is designed to accurately estimate the stator inductances, whereas the second one is designed to precisely estimate the stator resistance, rotor flux linkage, and load torque. In this paper, the adaptive decoupling proportional-integral (PI) controllers with the maximum torque per ampere operation, which utilize the previously identified parameters in real time, are chosen to verify the effectiveness of the proposed parameter estimation scheme. The simulation results via MATLAB/Simulink and the experimental results via a prototype IPMSM drive system with a TI TMS320F28335 DSP are presented under various conditions. A comparative study with the conventional decoupling PI control method is carried out to demonstrate the better performances (i.e., faster dynamic response, less steady-state error, more robustness, etc.) of the adaptive decoupling PI control scheme based on the proposed online parameter estimation technique.

Electric motor design is a multi-variable problem which involves geometric dimensions of the stator and rotor. Presenting a unique solution for a family of optimization criteria has always been a challenge for motor designers. Several numerical tools such as finite element methods (FEM) have been developed to perform a precise analysis and predict the outcome of the design. However, limits in parametric analysis as well as mathematical and computational burden on numerical tools usually prohibit the designer in obtaining a unique solution for the design problem. These limits and demands in optimized solutions motivate the designer to use analytical models in order to perform a comprehensive parametric design. An accurate analytical model is crucial for this purpose. In this paper, an analytical model for permanent magnet assisted synchronous reluctance motor (PMa- SynRM) with four flux barriers and one cutout per pole is developed. Flux densities are found in the air gap, in the cutouts, and in the flux barriers; thus, the back-EMF developed by the permanent magnets is derived. Equations for the d-axis and the q-axis inductances are also obtained. Electromagnetic torque is finally derived using the co-energy method. The developed analytical model highlights the contribution of the reluctance variation and permanent magnets on the developed torque. Simulation results are obtained using both Matlab and Ansoft/Maxwell packages. These outcomes are supported by the experimental results obtained from a laboratory test bed.

In this study, Switched Reluctance Motor (SRM), Induction Motor (IM), Brushless DC Motor, and Permanent Magnet Motor (PM), and their drives have been compared with the efficiency, cost, weight, cooling, maximum speed, reliability, fault tolerance, power ratings, and vehicle acceleration time. Hence, a comprehensive literature research on motor types and their drives used in EV has been made. According to these researches, some conclusions have been obtained. It has been seen that PM BLDC motors and their drives are the most efficient and have high power density, brushless DC motors and their drives have low cost, IM is appropriate for controllability and cost, the weight of SRM is low, its reliability is high, it operates fault-tolerance and according to the acceleration time, its performance is better than IM and BLDC. Hence, SRM is the most appropriate motor for EV.

In this paper, a low-time-consuming and low-cost sensorless-control algorithm for high-dynamic performance permanent-magnet synchronous motors, both surface and internal permanent-magnet mounted for position and speed estimation, is introduced, discussed, and experimentally validated. This control algorithm is based on the estimation of rotor speed and angular position starting from the back electromotive force space-vector determination without voltage sensors by using the reference voltages given by the current controllers instead of the actual ones. This choice obviously introduces some errors that must be vanished by means of a compensating function. The novelties of the proposed estimation algorithm are the position-estimation equation and the process of compensation of the inverter phase lag that also suggests the final mathematical form of the estimation. The mathematical structure of the estimation guarantees a high degree of robustness against parameter variation as shown by the sensitivity analysis reported in this paper. Experimental verifications of the proposed sensorless-control system have been made with the aid of a flexible test bench for brushless motor electrical drives. The test results presented in this paper show the validity of the proposed low-cost sensorless-control algorithm and, above all, underline the high dynamic performances of the sensorless-control system also with a reduced equipment.

Silicon (Si) insulated-gate bipolar transistors are widely used in railway traction converters. In the near future, silicon carbide (SiC) technology will push the limits of switching devices in three directions: higher blocking voltage, higher operating temperature, and higher switching speeds. The first silicon carbide (SiC) MOSFET modules are available on the market and look promising. Although they are still limited in breakdown voltage, these wide-bandgap components should improve traction-chain efficiency. Particularly, a significant reduction in the switching losses is expected which should lead to improvements in power-weight ratios. Nevertheless, because of the high switching speed and the high current levels required by traction applications, the implementation of these new modules is critical. An original method is proposed to compare, in terms of stray inductance, several dc bus-bar designs. To evaluate the potential of these new devices, a first set of measurements, based on a single-pulse test-bench, was obtained. The switching behavior of SiC devices was well understood at turn-off and turn-on. To complete this work, the authors use an opposition method to compare Si-IGBT and SiC-MOSFET modules in voltage source inverter operation. For this purpose, a second test-bench, allowing electrical and thermal measurements, was developed. Experimental results confirm the theoretical loss-calculation of the single-pulse tests and the correct operation of up to three modules directly connected in parallel. This analysis provides guidelines for a full SiC inverter design, and prospects for developments in traction applications are presented.

In this paper, power electronic transformer (PET)-based railway traction systems are comprehensively reviewed according to the unique application features and requirements. By comparing PET and conventional line frequency transformer (LFT)-based systems, their pros and cons are summarized. By further reviewing all kinds of PET-based designs from the early concepts to the latest ones in the order of their publication dates, the developing trends are highlighted. By synthetically considering the requirements and the state of the art, the key challenges and opportunities are identified and discussed. It shows that although PET-based systems are still developing and far from mature, they are already superior to LFT-based systems in terms of system weight, efficiency, and functionalities especially for 15-kV/16.7-Hz applications. With the advancements on wide bandgap power devices, medium frequency transformers, and converters, PET systems will be even more promising and available for all types of railway tractions in the near future.

This paper presents an alternative permanent magnet rotor containment technology. The use of a semipermeable material in a laminated structure is shown to result in a significant improvement in magnetic loading for a given thickness of containment compared to the use of magnetically inert materials such as carbon fiber or Inconel, while minimizing eddy current losses in the containment layer. An analytical model is presented for determining the air-gap field delivered from a permanent magnet array with a semipermeable containment. The analysis is validated through finite element analysis. The fabrication of a prototype machine is detailed and the results presented show good agreement with the analysis. The validated modeling process is used to assess the potential of the new technology.

Eddy current losses in wireless charging systems for electric vehicles must be minimized in consideration of constraints like the available space, the strand diameter in litz wire and the cost. A mathematical model for eddy current losses is needed to find the optimum geometrical design for a given application. This paper gives the fundamental equations to calculate skin effect, proximity effect and dc losses in litz wire. Litz wire consists of strands, which are twisted to form multiple bundles. Therefore eddy current losses occur not only on the strand level but the bundle level as well. The losses caused by different effects and bundle levels are compared, and the influence of strand diameter and strand number on the total loss is examined. Finally, aluminum litz wire is analyzed. It becomes apparent that the overall loss can be reduced with the aid of replacing copper litz wire by aluminum in some areas of application. Material choice and geometrical design require detailed knowledge of the systems nominal operation point.


Question: Design and thermal evaluation of high-torque, low-speed fractional-slot concentrated windings for in-wheel traction motors.

This paper presents an in-depth examination of diverse electrical machine technologies, such as brushless permanent magnet, induction, switched reluctance, and brushless reluctance machines, with a special focus on the innovation of hybrid electrical machine topologies. The analysis delves into the comparative advantages of these machines concerning torque and power density, operating speed range, overload capacity, and efficiency, specifically within the context of electric, hybrid, and fuel cell vehicles. The review underscores the significance of these electrical machine technologies in the realm of electric vehicles, highlighting their role in shaping the future of sustainable transportation.

Regarding the original question about designing and conducting thermal analysis on high torque, low-speed fractional-slot concentrated windings in-wheel traction motors, the similar document provides a broader context by discussing various electrical machine technologies. This context is crucial for understanding the design considerations and thermal analysis techniques applicable to in-wheel traction motors, particularly those with high torque and low-speed requirements. The paper's emphasis on hybrid electrical machine topologies and their performance metrics can inform the design and analysis of the specified motor, offering insights into torque and power density, operating speed range, and efficiency, which are all critical factors in the development of high-performance in-wheel traction motors.

This paper presents a methodology for the design, analysis, and graphical optimization of ironless brushless permanent magnet machines primarily for generator applications. Magnetic flux in this class of electromagnetic machine tends to be 3-D due to the lack of conventional iron structures and the absence of a constrained magnetic flux path. The proposed methodology includes comprehensive geometric, magnetic and electrical dimensioning followed by detailed 3-D finite element (FE) modeling of a base machine for which parameters are determined. These parameters are then graphically optimized within sensible volumetric and electromagnetic constraints to arrive at improved design solutions. This paper considers an ironless machine design to validate the 3-D FE model to optimize power conversion for the case of a low-speed, ironless stator generator. The machine configuration investigated in this paper has concentric arrangement of the rotor and the stator, solenoid-shaped coils, and a simple mechanical design considered for ease of manufacture and maintenance. Using performance and material effectiveness as the overriding optimization criteria, this paper suggests optimal designs configurations featuring two different winding arrangements, i.e., radial and circumferentially mounted. Performance and material effectiveness of the studied ironless stator designs are compared to published ironless machine configurations.

The aim of this paper is to present a novel magnetic integrated LCC series-parallel compensation topology for the design of both the primary and pickup pads in inductive power transfer (IPT) applications. A more compact structure can be realized by integrating the inductors of the compensation circuit into the coupled power-transmitting coils. The impact of the extra coupling between the compensated coils (inductors) and the power-transferring coils is modeled and analyzed. The basic characteristics of the proposed topology are studied based on the first harmonic approximation (FHA). High-order harmonics are taken into account to derive an analytical solution for the current at the switching instant, which is helpful for the design of soft-switching operation. An IPT system with up to 5.6kW output power for electric vehicles (EV) charger has been built to verify the validity of the proposed magnetic integrated compensation topology. A peak efficiency of 95.36% from DC power source to the battery load is achieved at rated operation condition.

Permanent magnet linear synchronous motor (PMLSM) has several advantages, such as simple structure, small volume, high force, and so on. It is a research focus and difficulties in the research field of motor. Because PMLSM is different from the traditional rotary motors in structure and running state, it is difficult for to build its precise mathematical model by analytical method. The asymmetry of the three-phase winding electromagnetic and electrical parameters of open PMLSM in a low-speed working zone is impossible to take into account. In this paper, the difference between the permanent magnet linear synchronous motor and traditional motors are analyzed. And then, a non-linear, asymmetric and variable parameter space state model is built, considering the structural and running characteristics of PMLSM. The proposed model needs all kinds of parameters of PMLSM, including selfinductance, mutual inductance, flux linkage, etc. So, a finite element calculation and actual measurement of the required parameters of a prototype PMLSM are done, respectively. The influence of air-gap on the inductance parameters was analyzed.

This paper presents a general process to analyze and optimize the performance for an arbitrary discrete Halbach magnet array which is “unnormal” on magnetization direction and arc length of each segment. Besides the original type, a new arrangement of Halbach array is proposed, which does not have main PM segments. Two-dimensional field analytical models for predicting the air-gap field distribution of both arrangements are offered and validated by an FEA method. Then, a multi-objective optimizer was applied for different requirements, and comparison between normal and optimized 3-segment Halbach array is given to verify the effect of optimization. At last, some potential applications and follow-up study are discussed.

In this paper, a novel induction motor (IM) stator current controller is proposed based on the stationary frame feed forward controlling structure. The proposed regulator represents an improved variant of the existing stationary and rotational frame IM stator current regulators based on the back EMF compensation. The paper novelty consists of the stationary frame controlling structure based on two feed forward actions that enable asymptotic reference tracking. Namely, the novel stationary frame regulator includes a proportional integral (PI) regulation term combined with a stator resistance voltage drop and back EMF feed forward compensations, with the back EMF compensation based on the stator flux value estimated by means of a programmable low-pass filter. The feedback term is based on a PI controller, with the corresponding parameter tuning procedure outlined in the paper. The simulation runs are included in the paper in order to examine the regulator performance in relation to the factors critical to the IM stator current regulation: variable IM model parameter values, transport delay introduced in the control loop, variable stator frequency, and rotor speed values. Also, the experimental tests are presented and carried out on an experimental setup for different operating conditions typical for an IM drive.

This paper presents a multiple-output resonant inverter for multi-coil systems featuring high efficiency and flexible output power control for modern induction heating appliances. By adopting a matrix structure, the number of controlled devices can be reduced to the square root of the number of controlled induction heating loads. Therefore, an elevated component count reduction can be achieved compared to classical multi-outputs solutions. The proposed converter is analyzed and verified experimentally, proving the feasibility of this proposal and benefits compared with previous alternatives.

A power converter is very necessary in the system of high-speed permanent magnet generators (HSPMG), since the frequency of the output electrical energy is generally hundreds of Hertz or even more than 1000 Hz. However, the converter with switches in fast on-off transitions will have some effects on the generator and, therewith, some current harmonics will be also induced in the device. Taking a 117 kW 60 000 r/min HSPMG as an example, the harmonics in this generator were analyzed. And then, the voltage harmonic content, the current harmonic content, and the current total harmonic distortion could be obtained. In addition, stator core losses and rotor eddy current losses were analyzed comparatively, and the influence of the converter on the generator power factor was further studied. Based on the 3-D coupling field between fluid and temperature, the temperature distributions were obtained when the generator was connected to different loads, and the influence of the rectifier on the generator temperature field could be obtained, which was also compared with the test data.

Among position sensors, resolvers are superior from reliability point of view. However, obtaining lower output voltage harmonics and simple manufacturing process is a challenge in the design and optimization of resolvers. In this paper, a metaheuristic optimization algorithm is used to minimize total harmonic distortion of the output signals, and consequently the estimated position error in concentrated coil wound field resolvers. Meanwhile, to minimize total coil numbers, manufacturing costs, and complexity of the winding process, modified objective function and constraints are proposed. In this way, a modified winding function method is employed for performance analysis of the axial flux resolver. Then, the merits of the optimized winding configuration with respect to fractional slot concentrated windings are shown. The results of the proposed configurations are verified with a three-dimensional time-stepping finite-element method. Finally, the prototype of the studied axial flux resolver is constructed and tested. Good agreement is obtained between simulation and experimental results, confirming the effectiveness of the optimization process.

The compound-structure permanent-magnet synchronous machine (CS-PMSM), comprising a double rotor machine (DRM) and a permanent-magnet (PM) motor, is a promising electronic-continuously variable transmission (e-CVT) concept for hybrid electric vehicles (HEVs). By CS-PMSM, independent speed and torque control of the vehicle engine is realized without a planetary gear unit. However, the slip rings and brushes of the conventional CS-PMSM are considered a major drawback for vehicle application. In this paper, a brushless flux-modulated CS-PMSM is investigated. The operating principle and basic working modes of the CS-PMSM are discussed. Mathematical models of the CS-PMSM system are given, and joint control of the two integrated machines is proposed. As one rotor of the DRM is mechanically connected with the rotor of the PM motor, special rotor position detection and torque allocation methods are required. Simulation is carried out by Matlab/Simulink, and the feasibility of the control system is proven. Considering the complexity of the controller, a single digital signal processor (DSP) is used to perform the interconnected control of dual machines instead of two separate ones, and a typical hardware implementation is proposed.

This paper proposes an online parameter estimation method based on a discrete-time dynamic model for the interior permanent-magnet synchronous motors (IPMSMs). The proposed estimation technique, which takes advantage of the difference in dynamics of motor parameters, consists of two affine projection algorithms. The first one is designed to accurately estimate the stator inductances, whereas the second one is designed to precisely estimate the stator resistance, rotor flux linkage, and load torque. In this paper, the adaptive decoupling proportional-integral (PI) controllers with the maximum torque per ampere operation, which utilize the previously identified parameters in real time, are chosen to verify the effectiveness of the proposed parameter estimation scheme. The simulation results via MATLAB/Simulink and the experimental results via a prototype IPMSM drive system with a TI TMS320F28335 DSP are presented under various conditions. A comparative study with the conventional decoupling PI control method is carried out to demonstrate the better performances (i.e., faster dynamic response, less steady-state error, more robustness, etc.) of the adaptive decoupling PI control scheme based on the proposed online parameter estimation technique.


Question: Auction-based control systems for traffic lights: algorithms and practical data analysis.

This paper explores the innovative concept of integrating auction-based controllers in traffic light systems, presenting advanced algorithms and empirical data from real-world applications. Unlike traditional traffic management systems, the auction-based controllers adopt a market-driven approach to optimize traffic flow. By leveraging sophisticated algorithms, these controllers dynamically allocate green light time to different traffic lanes based on demand, ensuring efficient and adaptive traffic management. The paper provides a comprehensive analysis of the algorithms employed, detailing their design principles and operational mechanisms. Furthermore, it presents empirical evidence gathered from real-world deployments, showcasing the effectiveness of auction-based controllers in enhancing traffic efficiency and reducing congestion. The results highlight the potential of this novel approach in revolutionizing traffic light systems, offering a promising solution to urban traffic management challenges.

Regarding the OpenStreetMap project, it is a collaborative endeavor that harnesses the power of a global community to create detailed and up-to-date street maps. Inspired by the success of Wikipedia, OpenStreetMap operates under a peer production model, inviting users to contribute to the mapping of the world. The project's core infrastructure is maintained by a dedicated group of approximately 40 volunteers, who oversee server operations, software development, and cartographical outputs. Additionally, a vibrant community of software developers has emerged, dedicated to creating tools that facilitate the use of OpenStreetMap data across various platforms and devices. The central hub of the OpenStreetMap project is its official website, which serves as a platform for map editing, data access, and community engagement. The project's success is a testament to the power of collective intelligence and the potential of open collaboration in generating valuable resources for the public good.

In modern cities, more and more vehicles, such as taxis, have been equipped with GPS devices for localization and navigation. Gathering and analyzing these large-scale real-world digital traces have provided us an unprecedented opportunity to understand the city dynamics and reveal the hidden social and economic “realities”. One innovative pervasive application is to provide correct driving strategies to taxi drivers according to time and location. In this paper, we aim to discover both efficient and inefficient passenger-finding strategies from a large-scale taxi GPS dataset, which was collected from 5350 taxis for one year in a large city of China. By representing the passenger-finding strategies in a Time-Location-Strategy feature triplet and constructing a train/test dataset containing both top- and ordinary-performance taxi features, we adopt a powerful feature selection tool, L1-Norm SVM, to select the most salient feature patterns determining the taxi performance. We find that the selected patterns can well interpret the empirical study results derived from raw data analysis and even reveal interesting hidden “facts”. Moreover, the taxi performance predictor built on the selected features can achieve a prediction accuracy of 85.3% on a new test dataset, and it also outperforms the one based on all the features, which implies that the selected features are indeed the right indicators of the passenger-finding strategies.

This paper elaborates on the design, implementation and performance evaluation of a prototype Radio Resource Management (RRM) framework for TV white spaces (TVWS) exploitation, under an auction-based approach. The proposed RRM framework is applied in a centralised Cognitive Radio (CR) network architecture, where exploitation of the available TVWS by Secondary Systems is orchestrated via a Spectrum Broker. Efficient RRM framework performance, as a matter of maximum-possible resources utilization and benefit of Spectrum Broker, is achieved by proposing and evaluating an auction-based algorithm. This auction-based algorithm considers both frequency and time domain during TVWS allocation process which was defined as an optimization problem, where maximum payoff of Spectrum Broker is the optimization goal. Experimental tests that were carried-out under controlled conditions environment, verified the validity of the proposed framework, besides identifying fields for further research.

This paper describes a hybrid model and a model predictive control (MPC) strategy for solving a traction control problem. The problem is tackled in a systematic way from modeling to control synthesis and implementation. The model is described first in the Hybrid Systems Description Language to obtain a mixed-logical dynamical (MLD) hybrid model of the open-loop system. For the resulting MLD model, we design a receding horizon finite-time optimal controller. The resulting optimal controller is converted to its equivalent piecewise affine form by employing multiparametric programming techniques, and finally experimentally tested on a car prototype. Experiments show that good and robust performance is achieved in a limited development time by avoiding the design of ad hoc supervisory and logical constructs usually required by controllers developed according to standard techniques.

IEEE 802.11p-based Dedicated Short Range Communications (DSRC) is considered a promising wireless technology for enhancing transportation safety and improving highway efficiency. Here, using a large set of empirical measurement data taken in a rich variety of realistic driving environments, we attempt to characterize communication properties of DSRC as well as to analyze the causes of communication loss. Specifically, from a perspective of vehicular network engineers, the fundamental characteristic of DSRC communications is Packet Delivery Ratio (PDR). We investigate the impact of both uncontrollable environmental factors and controllable radio parameters on DSRC characteristics. Moreover, we also examine temporal correlation, spatial correlation and symmetric correlation of DSRC characteristics under realistic vehicular environments.

In Vehicular Ad Hoc Networks (VANETs), moving vehicles are considered as mobile nodes in the network and they are connected to each other via wireless links when they are within the communication radius of each other. Efficient message delivery in VANETs is still a very challenging research issue. In this paper, a Q-learning based routing protocol (i.e., QGrid) is introduced to help to improve the message delivery from mobile vehicles to a specific location. QGrid considers both macroscopic and microscopic aspects when making the routing decision, while the traditional routing methods focus on computing meeting information between different vehicles. QGrid divides the region into different grids. The macroscopic aspect determines the optimal next-hop grid and the microscopic aspect determines the specific vehicle in the optimal next-hop grid to be selected as next-hop vehicle. QGrid computes the Q-values of different movements between neighboring grids for a given destination via Q-learning. Each vehicle stores Q-value table learned offline, then selects optimal next-hop grid by querying Q-value table. Inside the selected next-hop grid, we either greedily select the nearest neighboring vehicle to the destination or select the neighboring vehicle with highest probability of moving to the optimal next-hop grid predicted by the two-order Markov chain. The performance of QGrid is evaluated by using real life trajectory GPS data of Shanghai taxies. Simulation comparison among QGrid and other existing position-based routing protocols confirms the advantages of proposed QGrid routing protocol for VANETs.

Intelligent visual surveillance for road vehicles is the key to developing autonomous intelligent traffic systems. Recently, traffic incident detection employing computer vision and image processing has attracted much attention. In this paper, a probabilistic model for predicting traffic accidents using three-dimensional (3-D) model-based vehicle tracking is proposed. Sample data including motion trajectories are first obtained by 3-D model-based vehicle tracking. A fuzzy self-organizing neural network algorithm is then applied to learn activity patterns from the sample trajectories. Finally, vehicle activity is predicted by locating and matching each partial trajectory with the learned activity patterns, and the occurrence probability of a traffic accident is determined. Experiments show the effectiveness of the proposed algorithms.

We present a framework for the estimation of driver behavior at intersections, with applications to autonomous driving and vehicle safety. The framework is based on modeling the driver behavior and vehicle dynamics as a hybrid-state system (HSS), with driver decisions being modeled as a discrete-state system and the vehicle dynamics modeled as a continuous-state system. The proposed estimation method uses observable parameters to track the instantaneous continuous state and estimates the most likely behavior of a driver given these observations. This paper describes a framework that encompasses the hybrid structure of vehicle-driver coupling and uses hidden Markov models (HMMs) to estimate driver behavior from filtered continuous observations. Such a method is suitable for scenarios that involve unknown decisions of other vehicles, such as lane changes or intersection access. Such a framework requires extensive data collection, and the authors describe the procedure used in collecting and analyzing vehicle driving data. For illustration, the proposed hybrid architecture and driver behavior estimation techniques are trained and tested near intersections with exemplary results provided. Comparison is made between the proposed framework, simple classifiers, and naturalistic driver estimation. Obtained results show promise for using the HSS-HMM framework.

The last decades’ tremendous advances in wireless communications have been driven mainly by personal communications. Radio resource allocation mechanisms for optimizing key metrics, such as average throughput and delay, for such traffic are by now rather well-developed. However, with the increased interest in wireless machine-to-machine communication, e.g. for industrial control or monitoring of large-scale infrastructures, new challenges emerge. The performance of an estimator or closed-loop control system that operates over an unreliable wireless network depends on the full latency and loss distributions and not only on their averages. For these applications, more suitable performance metrics are per-packet guarantees on latency and reliability (on-time delivery). This thesis studies optimal forwarding of deadline-constrained traffic over lossy multi-hop networks. We assume a routing topology in the form of a directed graph with packet loss on links described by finite-state Markov chains, and focus on a single transient packet scenario. Two problems are considered: maximizing the probability that packets are delivered within their specified deadline; and minimizing the expected energy cost with a guaranteed probability of on-time delivery. The first problem can be formulated as a finite-horizon Markov decision process (MDP), while the second problem is a finite-horizon constrained Markov decision process (CMDP). A general dynamic programming framework that solves a weighted sum of reliability and energy maximization problem is proposed. The maximum deadline-constrained reliability problem is solved by studying a simplification of the general dynamic programming. The minimum energy optimal policy is a random selection between two deterministic and computable forwarding policies, each of which can be found via a dynamic programming framework. Particular instances with Bernoulli and Gilbert-Elliot loss models that admit numerically efficient solutions are discussed. Finally, we show the application of the technique for a co-design of forwarding policies and controller for wireless control. Based on the recent result on the monotonicity of the optimal control loss, the problem of minimum control loss and the problem of minimum energy cost with a guaranteed control performance can be solved optimally.

The Distributed Rate Limiting (DRL) paradigm is a recently proposed mechanism for decentralized control of cloud-based services. DRL is a simple and efficient approach to resolve the issues of pricing and resource control/engineering of cloud based services. The existing DRL schemes focus on very specific performance metrics (such as loss rate and fair-share) and their design heavily depends on the assumption that the traffic is generated by elastic TCP sources. In this paper we tackle the DRL problem for general workloads and performance metrics and propose an analytic framework for the design of stable DRL algorithms. The closed-form nature of our results allows simple design rules which, together with extremely low communication overhead, makes the presented algorithms practical and easy to deploy with guaranteed convergence properties under a wide range of possible scenarios.

In recent years, taxis in New York City have been equipped with GPS sensors to record the start and end locations of every trip, in conjunction with fare collection equipment recording trip time, fare, and distance. This paper analyzes this vast dataset with big data technologies. We use MapReduce and Hive in order to understand traffic and travel patterns, discover relationships among the data, and make predictions on the taxi network. We also propose a service model combining the results of our data analysis with smartphone applications to offer users various taxi-related services based on results of our big data analysis.


Question: Auction-based control systems for traffic lights: algorithms and practical data evaluation.

In the realm of ubiquitous computing, location systems play a pivotal role in enabling seamless integration of technology into everyday life. While personal use of location data is generally unrestricted, there are stringent guidelines for the redistribution and commercial exploitation of such information. Permission to republish or incorporate location system data into new collective works for promotional or advertising purposes, or for resale to servers or lists, must be sought from the IEEE. The primary objective of disseminating this information is to facilitate scholarly and technical exchange. It's imperative to acknowledge that copyright and all associated rights remain with the authors or other copyright holders. Those wishing to copy this information are expected to comply with the copyright terms imposed by the authors. Typically, the direct reposting of these works is prohibited without the explicit consent of the copyright holder. This underscores the importance of respecting intellectual property rights in the utilization and dissemination of location system data for ubiquitous computing applications.

Community-based question and answer (Q&A) systems facilitate information exchange and enable the creation of reusable knowledge repositories. While these systems are growing in usage and are changing how people find and share information, current designs are inefficient, wasting the time and attention of their users. Furthermore, existing systems do not support signaling and screening of joking and non-serious questions. Coupling Q&A services with instant and text messaging for faster questions and answers may exacerbate these issues, causing Q&A services to incur high interruption costs on their users.
 In this paper we present the design and evaluation of a market-based real-time Q&A system. We compared its use to a similar Q&A system without a market. We found that while markets can reduce wasted resources by reducing the number of less important questions and low quality answers, it may also reduce the socially conducive questions and usages that are vital to sustaining a Q&A community.

Optimal planning for public transportation is one of the keys to sustainable development and better quality of life in urban areas. Compared to private transportation, public transportation uses road space more efficiently and produces fewer accidents and emissions. In this paper, we focus on the identification and optimization of flawed bus routes to improve utilization efficiency of public transportation services, according to people's real demand for public transportation. To this end, we first provide an integrated mobility pattern analysis between the location traces of taxicabs and the mobility records in bus transactions. Based on mobility patterns, we propose a localized transportation mode choice model, with which we can accurately predict the bus travel demand for different bus routing. This model is then used for bus routing optimization which aims to convert as many people from private transportation to public transportation as possible given budget constraints on the bus route modification. We also leverage the model to identify region pairs with flawed bus routes, which are effectively optimized using our approach. To validate the effectiveness of the proposed methods, extensive studies are performed on real world data collected in Beijing which contains 19 million taxi trips and 10 million bus trips.

Airspace sectorisation provides a partition of a given airspace into sectors, subject to geometric constraints and workload constraints, so that some cost metric is minimised. We survey the algorithmic aspects of methods for automatic airspace sectorisation, for an intended readership of experts on air traffic management.

With the explosive growth of wireless data, mobile edge caching has emerged as a promising paradigm to support mobile traffic recently, in which the service providers (SPs) prefetch some popular contents in advance and cache them locally at the network edge. When requested, those locally cached contents can be directly delivered to users with low latency, thus alleviating the traffic load over backhaul channels during peak hours and enhancing the quality-of-experience (QoE) of users simultaneously. Due to the limited available cache space, it makes sense for the SP to cache the most profitable contents. Nevertheless, users' true valuations of contents are their private knowledge, which is unknown to the SP in general. This information asymmetry poses a significant challenge for effective caching at the SP side. Further, the cached contents can be delivered with different quality, which needs to be chosen judiciously to balance delivery costs and user satisfaction. To tackle these difficulties, in this paper, we propose an optimal auction mechanism from the perspective of the SP. In the auction, the SP determines the cache space allocation over contents and user payments based on the users' (possibly untruthful) reports of their valuations so that the SP's expected revenue is maximized. The advocated mechanism is designed to elicit true valuations from the users (incentive compatibility) and to incentivize user participation (individual rationality). In addition, we devise a computationally efficient method for calculating the optimal cache space allocation and user payments. We further examine the optimal choice of the content delivery quality for the case with a large number of users and derive a closed-form solution to compute the optimal delivery quality. Finally, extensive simulations are implemented to evaluate the performance of the proposed optimal auction mechanism, and the impact of various model parameters is highlighted to obtain engineering insights into the content caching problem.

This paper considers the flow control and resource allocation problem as applied to the generic multipath communication networks with heterogeneous applications. We propose a novel distributed algorithm, show and prove that among all the sources with positive increasing and bounded utilities (no need to be concave) in steady state, the utility max-min fairness is achieved, which is essential for balancing QoS (Quality of Service) for different applications. By combining the first order Lagrangian method and filtering mechanism, the adopted approach eliminates typical oscillation behavior in multipath networks and possesses a rapid convergence property. In addition, the algorithm is capable of deciding the optimal routing strategy and distributing the total traffic evenly out of the available paths. The performance of our utility max-min fair flow control algorithm is evaluated through simulations under two representative case studies, as well as the real implementation issues are addressed deliberately for the practical purpose.

Traffic sign recognition is the second part of traffic sign detection and recognition systems. It plays a crucial role in driver assistance systems and provides drivers with crucial safety and precaution information. In this study, the recognition of the TS is performed based on its border color, shape, and pictogram information. This technique breaks down the recognition system into small parts, which makes it efficient and accurate. Moreover, this makes it easy to understand TS components. The proposed technique is composed of three independent stages. The first stage involves extracting the border colors using an adaptive image segmentation technique that is based on learning vector quantization. Then, the shape of the TS is detected using a fast and simple matching technique based on the logical exclusive OR operator. Finally, the pictogram is extracted and classified using a support vector machines classifier model. The proposed technique is applied on the German traffic sign recognition benchmark and achieves an overall recognition rate of 98.23%, with an average computational speed of 30 ms.

We describe an approach to explore the design space for architectures of packet processing devices on the system level. Our method is specific to the application domain of packet processors and is based on (1) models for packet processing tasks, a specification of the workload generated by traffic streams, and a description of the feasible space of architectures including computation and communication resources, (2) a measure to characterize the performance of network processors under different usage scenarios, (3) a new method to estimate end-to-end packet delays and queuing memory, taking task scheduling policies and bus arbitration schemes into account, and (4) a evolutionary algorithm for multi-objective design space exploration. Our method is analytical and based on a high level of abstraction, where the goal is to quickly identify interesting architectures, which may be subjected to a more detailed evaluation, e.g. using simulation. The feasibility of our approach is shown by a detailed case study, where the final output is three candidate architectures, representing different cost versus performance tradeoffs.

Intelligent transportation systems (ITS) are becoming a crucial component of our society, whereas reliable and efficient vehicular communications consist of a key enabler of a well-functioning ITS. To meet a wide variety of ITS application needs, vehicular-to-vehicular and vehicular-to-infrastructure communications have to be jointly considered, configured, and optimized. The effective and efficient coexistence and cooperation of the two give rise to a dynamic spectrum management problem. One recently emerged and rapidly adopted solution of a similar problem in cellular networks is the so-termed device-to-device (D2D) communications. Its potential in the vehicular scenarios with unique challenges, however, has not been thoroughly investigated to date. In this paper, we for the first time carry out a feasibility study of D2D for ITS based on both the features of D2D and the nature of vehicular networks. In addition to demonstrating the promising potential of this technology, we will also propose novel remedies necessary to make D2D technology practical as well as beneficial for ITS.

When you drive to somewhere far away, you will leave your current location via one of only a few important traffic junctions. Starting from this informal observation, we developed an algorithmic approach, transit node routing, that allows us to reduce quickest path queries in road networks to a small number of table lookups. For road maps of Western Europe and the United States, our best query times improved over the best previously published figures by two orders of magnitude. This is also more than one million times faster than the best known algorithm for general networks.

Let G= (V,E,σ,μ) be a simple connected undirected fuzzy graph. In this paper, we use a fuzzy graph model to represent a traffic network of a city and discuss a method to find the different type of accidental zones in a traffic flows. This paper is based on fuzzy coloring of fuzzy graphs and fuzziness of vertices. Using this approach, we can minimize the total waiting time of a traffic flow. It will help to reduce the traffic jam of a road.


Question: Auction-based control systems for traffic lights: algorithms and practical data analysis.

This paper presents an intriguing perspective on the utilization of neural networks, a topic that, despite the title's suggestion, is not conventionally explored in depth within the book "Neural Networks: A Comprehensive Foundation" by Professor Haykin. The book, now in its second edition, stands out in the vast landscape of contemporary literature, frequently cited across various academic works. However, it deviates from the typical approach of detailing orthodox theories and techniques associated with neural networks, also known as connectionist systems or neurocomputational systems. Contrary to expectations, it does not serve as a foundational text outlining the theoretical bases of neural networks. Instead, it focuses on four key chapters that exceptionally cover the major models in this research field, including perceptrons and Radial-Basis Function Networks (RBFN). The exposition of these models is commendably thorough, offering readers a deep understanding of the core concepts.

**Question**: Evaluating the Role of Blockchain in Supply Chain Finance

**Documents**: Blockchain: A Comprehensive Primer
Blockchain technology has emerged as a transformative force in various sectors, including finance, with its potential to revolutionize supply chain finance being particularly noteworthy. The book "Blockchain: A Comprehensive Primer" provides a detailed exploration of blockchain technology, explaining its foundational concepts, operational mechanisms, and applications across different industries. In the realm of supply chain finance, blockchain offers several advantages, such as increased transparency, enhanced security, and improved efficiency. By enabling real-time tracking and verification of transactions, blockchain can significantly reduce the risk of fraud and streamline financial processes. The book delves into these aspects, illustrating how blockchain can foster a more secure and efficient financial ecosystem in supply chain management.

**Similar Documents**: This document offers a comprehensive analysis of blockchain technology, highlighting its transformative impact on diverse sectors, particularly in the area of supply chain finance. As detailed in the resource "Blockchain: A Comprehensive Primer," the technology's potential to reshape supply chain finance is significant, offering a multitude of benefits. Among these is the enhancement of transparency, bolstering of security measures, and optimization of operational efficiency. Blockchain's capacity to facilitate real-time transaction tracking and verification is pivotal in mitigating fraud risks and expediting financial processes. The document elaborates on these points, showcasing how blockchain can cultivate a secure and efficient financial landscape within the context of supply chain management, thereby revolutionizing traditional finance practices.

Real-time traffic awareness applications are playing an ever increasing role understanding and tackling traffic congestion in cities. First-hand accounts from drivers witnessing an incident is an invaluable source of information for traffic managers. Nowadays, drivers increasingly contact control rooms through social media to report on journey times, accidents or road weather conditions. These new interactions allow traffic controllers to engage users, and in particular to query them for information rather than passively collecting it. Querying participants presents the challenge of which users to probe for updates about a specific situation. In order to maximise the probability of a user responding and the accuracy of the information, we propose a strategy which takes into account the engagement levels of the user, the mobility profile and the reputation of the user. We provide an analysis of a real-world user corpus of Twitter users contributing updates to LiveDrive, a Dublin based traffic radio station.

This paper describes the Q-routing algorithm for packet routing, in which a reinforcement learning module is embedded into each node of a switching network. Only local communication is used by each node to keep accurate statistics on which routing decisions lead to minimal delivery times. In simple experiments involving a 36-node, irregularly connected network, Q-routing proves superior to a nonadaptive algorithm based on precomputed shortest paths and is able to route e ciently even when critical aspects of the simulation, such as the network load, are allowed to vary dynamically. The paper concludes with a discussion of the tradeo between discovering shortcuts and maintaining stable policies.

We address the problem of balancing the traffic load in multi-hop wireless networks. We consider a point-to-point communicating network with a uniform distribution of source-sink pairs. When routing along shortest paths, the nodes that are centrally located forward a disproportionate amount of traffic. This translates into increased congestion and energy consumption. However, the maximum load can be decreased if the packets follow curved paths. We show that the optimum such routing scheme can be expressed in terms of geometric optics and computed by linear programming. We then propose a practical solution, which we call Curveball Routing which achieves results not much worse than the optimum.
 We evaluate our solution at three levels of fidelity: a Java high-level simulator, the ns2 simulator, and the Intel Mirage Sensor Network Testbed. Simulation results using the high-level simulator show that our solution successfully avoids the crowded center of the network, and reduces the maximum load by up to 40%. At the same time, the increase of the expected path length is minimal, i.e., only 8% on average. Simulation results using the ns2 simulator show that our solution can increase throughput on moderately loaded networks by up to 15%, while testbed results show a reduction in peak energy usage by up to 25%. Our prototype suggests that our solution is easily deployable.

Traffic analysis, in the context of Telecommunications or Internet and Web data, is crucial for large network operations. Data in such networks is often provided as large graphs with hundreds of millions of vertices and edges. We propose efficient techniques for managing such graphs at the storage level in order to facilitate its processing at the interface level(visualization). The methods are based on a hierarchical decomposition of the graph edge set that is inherited from a hierarchical decomposition of the vertex set. Real time navigation is provided by an efficient two level indexing schema called the gkd*-tree. The first level is a variation of a kd-tree index that partitions the edge set in a way that conforms to the hierarchical decomposition and the data distribution (the gkd-tree). The second level is a redundant R-tree that indexes the leaf pages of the gkd-tree. We provide computational results that illustrate the superiority of the gkd-tree against conventional indexes like the kd-tree and the R*-tree both in creation as well as query response times.

In many complex processing systems with limited resources, fast response times are demanded, but are seldom delivered. This is an especially serious problem in healthcare systems providing critical patient care. In this paper, we develop a multiclass Markovian queueing network model of patient flow in the Accident and Emergency department of a major London hospital. Using real patient timing data to help parameterise the model, we solve for moments and probability density functions of patient response time using discrete event simulation. We experiment with different patient handling priority schemes and compare the resulting response time moments and densities with real data.

Road traffic accidents (RTAs) are a major public health concern, resulting in an estimated 1.2 million deaths and 50 million injuries worldwide each year. In the developing world, RTAs are among the leading cause of death and injury; Ethiopia in particular experiences the highest rate of such accidents. Thus, methods to reduce accident severity are of great interest to traffic agencies and the public at large. In this work, we applied data mining technologies to link recorded road characteristics to accident severity in Ethiopia, and developed a set of rules that could be used by the Ethiopian Traffic Agency to improve safety. Problem Statement The costs of fatalities and injuries due to road traffic accidents (RTAs) have a tremendous impact on societal well-being and socioeconomic development. RTAs are among the leading causes of death and injury worldwide, causing an estimated 1.2 million deaths and 50 million injuries each year (World Health Organization, 2004). Ethiopia has the highest rate of RTAs, owing to the fact that road transport is the major transportation system in the country. The Ethiopian traffic control system archives data on various aspects of the traffic system, such as traffic volume, concentration, and vehicle accidents. With more vehicles and traffic, the capital city of Addis Ababa takes the lion’s share of the risk, with an average of 20 accidents being recorded every day and even more going unreported. The basic hypothesis of this research is that accidents are not randomly scattered along the road network, and that drivers are not involved in accidents at random. There are complex circumstantial relationships between several characteristics (driver, road, car, etc.) and the accident occurrence. As such, one cannot improve safety without successfully relating accident frequency and severity to the causative variables (Kononov and Janson 2002). We will attempt to extend the authors’ previous work in this area by generating additional attributes and focusing on the contribution of road-related factors to accident severity in Ethiopia. This will help to identify the parts of a road that are risky, thus supporting traffic accident data analysis in decision-making processes. The general objective of the research is to investigate the role of road-related factors in accident severity, using RTA data from Ethiopia and predictive models. Our three specific objectives include: 1) exploring the underlying variables (especially road-related ones) that impact car accident severity, 2) predicting accident severity using different data mining techniques, and 3) comparing standard classification models for this task. Literature Review Various studies have addressed the different aspects of RTAs, with most focusing on predicting or establishing the critical factors influencing injury severity (Chong, A. et al. 2005). Numerous data mining-related studies have been undertaken to analyze RTA data locally and globally, with results frequently varying depending on the socio-economic conditions and infrastructure of a given location. Ossenbruggen, Pendharkar et al. (2001) used a logistic regression model to identify the prediction factors of crashes and crash-related injuries, using models to perform a risk assessment of a given region. These models included attributes describing a site by its land use activity, roadside design, use of traffic control devices, and traffic exposure. Their study illustrated that village sites were less hazardous than residential or shopping sites. Abdalla et al. (1997) also studied the relationship between casualty frequency and the distance of an accident from residential zones. Not surprisingly, casualty frequencies were higher in accidents that occurred nearer to residential zones, possibly due to higher exposure. The

Interconnection networks usually consist of a fabric of interconnected routers, which receive packets arriving at their input ports and forward them to appropriate output ports. Unfortunately, network packets moving through these routers are often delayed due to conflicting demand for resources, such as output ports or buffer space. Hence, routers typically employ arbiters that resolve conflicting resource demands to maximize the number of matches between packets waiting at input ports and free output ports. Efficient design and implementation of the algorithm running on these arbiters is critical to maximize network performance.This paper proposes a new arbitration algorithm called SPAA (Simple Pipelined Arbitration Algorithm), which is implemented in the Alpha 21364 processor's on-chip router pipeline. Simulation results show that SPAA significantly outperforms two earlier well-known arbitration algorithms: PIM (Parallel Iterative Matching) and WFA (Wave-Front Arbiter) implemented in the SGI Spider switch. SPAA outperforms PIM and WFA because SPAA exhibits matching capabilities similar to PIM and WFA under realistic conditions when many output ports are busy, incurs fewer clock cycles to perform the arbitration, and can be pipelined effectively. Additionally, we propose a new prioritization policy called the Rotary Rule, which prevents the network's adverse performance degradation from saturation at high network loads by prioritizing packets already in the network over new packets generated by caches or memory.

A challenging problem for model checking is represented by railway interlocking systems. It is a well known fact that interlocking systems, due to their inherent complexity related to the high number of variables involved, are not amenable to automatic verification, typically incurring in state space explosion problems. The literature is however quite scarce on data concerning the size of interlocking systems that have been successfully proved with model checking techniques. In this paper we attempt a systematic study of the applicability bounds for general purpose model checkers on this class of systems, by studying the typical characteristics of control tables and their size parameters. The results confirm that, although small scale interlocking systems can be addressed by model checking, interlockings that control medium or large railway yards can not, asking for specialized verification techniques.

Modern transportation system suffers from increasing passenger demand, limited vehicle supply and inefficient mobility service. Towards building an intelligent transportation system to address these issues, we propose a hierarchical framework to implement strategies that is capable of allocating vehicles to serve passengers in different locations based on limited supply. In the higher hierarchy, we optimize idle mileage induced by rebalancing vehicles across regions using receding horizon control towards current and predicted future requests. In addition, we design a dispatch strategy that is robust against passenger demand and vehicle mobility pattern uncertainties. In the lower hierarchy, within each region, pick-up and drop-off schedules for real-time requests are obtained for each vehicle by solving mixed-integer linear programs (MILP). The objective of the MILP is to minimize total mileage delay due to ride-sharing while serving as many requests as possible. We illustrate the validity of our framework via numerical simulations on taxi trip data from New York City.

Nowadays, the many traffic information are extracted from CCTV. The road occupancy, vehicle speed, accident detection, traffic collision and weather information are calculated in CCTV. These are big data comes from varying sources, such as, social sites or mobile phone GPS signals and so on. The Hadoop and HBase can store and analyze these real-time big data in a distributed processing framework. This framework can be designed as flexible and scalable framework using distributed CEP that process massive real-time traffic data and ESB that integrates other services. In this paper, we propose a new architecture for distributed processing that enables big data processing on the road traffic data with specially weather information and its related information analysis. We tested the proposed framework on road traffic data gathering 41 CCTV from Icheon to Gangneung freeway section in Korea. And the forecasting method is introduced at intermediate local area between CCTV.