Search Results (40)

This manuscript investigates the feasibility of Four-In-Wheel Electronic Differential Systems (4 IW-EDSs) within contemporary electric vehicles (EVs), emphasizing their benefits for stability regulation predicated on steering angles. Through an extensive literature review, we conduct a comparative analysis of various in-wheel-motor models in terms of power output, efficiency, and torque characteristics. Furthermore, we explore the distinctions between IW-EDSs and steer-by-wire systems, as well as conventional systems, while evaluating recent research findings to determine their implications for the evolution of electric mobility. Moreover, this paper addresses the necessity for fault-tolerant methodologies to boost reliability in practical applications. The findings yield valuable insights into the challenges and impacts associated with the implementation of differential steering control in four-wheel independent-drive electric vehicles. This study aims to explore the interaction between these systems, optimize torque distribution, and discover the most ideal control strategy that will improve maneuverability, stability, and energy efficiency, thereby opening up new frontiers in the development of next-generation electric vehicles with unparalleled performance and safety features. Full article

This paper presents a simulation framework for evaluating power flow, energy efficiency, thermal behavior, and energy consumption in electric vehicles (EVs) under standardized driving conditions. A detailed Simulink model is developed, integrating a lithium-ion battery, inverter, permanent magnet synchronous motor (PMSM), gearbox, and a field-oriented control strategy with PI-based speed and current regulation. The framework is applied to four standard driving cycles—UDDS, HWFET, WLTP, and NEDC—to assess system performance under varied load conditions. The UDDS cycle imposes the highest thermal loads, with temperature rises of 76.5 °C (motor) and 52.0 °C (inverter). The HWFET cycle yields the highest energy efficiency, with PMSM efficiency reaching 92% and minimal SOC depletion (15%) due to its steady-speed profile. The WLTP cycle shows wide power fluctuations (−30–19.3 kW), and a motor temperature rise of 73.6 °C. The NEDC results indicate a thermal increase of 75.1 °C. Model results show good agreement with published benchmarks, with deviations generally below 5%, validating the framework’s accuracy. These findings underscore the importance of cycle-sensitive analysis in optimizing energy use and thermal management in EV powertrain design. Full article

Rapid urbanization, excessive motorization, and the imperative to reduce carbon footprints are driving the search for sustainable urban space solutions. One promising approach involves the effective design of small-scale architecture, such as parking shelters, optimized for structural material consumption and resilience to vehicle impacts. This research employed a novel approach during the initial design phase. Genetic algorithms and optimization techniques were utilized to define the optimal geometries of steel structures, focusing on the height of the conoidal roof and the shape and arrangement of columns. The subsequent analysis included static and strength calculations, dimensioning, and evaluating structural responses to exceptional loading, incorporating novel impact scenarios. The analysis yielded several key insights into the structural efficiency, dynamic behavior, and design optimization of the shelters. The research revealed that both roof geometry and column shape and arrangement significantly influenced material consumption and design effectiveness. The findings indicated that shelters with four straight, vertical, non-corner columns exhibited the most favorable dynamic behavior and highest impact resistance. These shelters also facilitated easy parking for both single-module and double-module roof types. The research findings provide a foundation for the parametric design of functional and structurally resilient parking shelters that cater to urban transportation needs and ecological objectives. Full article

Rapid progress in lithium-ion batteries and AI-powered autonomous driving is poised to propel electric vehicles to a 50% share of the global automotive market by the year 2035. Today, there is a major focus on recycling electric vehicle motors, particularly on extracting rare earth elements (REEs) from NdFeB permanent magnets (PMs). This research is based on a single-furnace process concept designed to separate metal components within PM motors by exploiting the varying melting points of the constituent materials, simultaneously extracting REEs present within the PMs and transferring them into the slag phase. Thermodynamic modeling, via Factsage Equilib stream calculations, optimized the experimental process. Simulated materials substituted the PM motor, which optimized modeling-directed melting within an induction furnace. The 2FeO·SiO₂ fayalite flux can oxidize rare earth elements, resulting in slag. The neodymium oxidation reaction by fayalite exhibits a ΔG° of −427 kJ when subjected to an oxygen partial pressure ($P_{O_2}$) of 1.8 × 10⁻⁹, which is lower than that required for FeO decomposition. Concerning the FeO–SiO₂ system, neodymium, in Nd³⁺, exhibits a strong bonding with the ${\mathrm{S}\mathrm{i}\mathrm{O}}_4^{4^{-}}$ matrix, leading to its incorporation within the slag as the silicate compound, Nd₂Si₂O₇. When 30 wt.% fayalite flux was added, the resulting experiment yielded a neodymium extraction degree of 91%, showcasing the effectiveness of this fluxing agent in the extraction process. Full article

A solid propellant rocket motor is a propulsion system used in missiles and rockets that burns a propellant, typically composed of a mixture of fuel and an oxidizer, to generate the thrust necessary to propel the vehicle. During both the development and quality assurance phases, static firing tests of rocket motors are conducted to verify whether the system requirements meet the product specifications. These tests aim to produce two main types of graphs, “thrust versus burn time” and “pressure versus burn time,” both generated by the rocket motor during the burn. While thrust and pressure are important parameters in the design of a rocket motor, total impulse is the quantity that incorporates the crucial element of time, measuring how high a rocket can be launched. To ensure greater metrological reliability in static tests of rocket motors, it is important to carefully evaluate the uncertainty levels in the measurement chain of the data acquisition system. This work aims to assess the uncertainty levels expressed in the calculated total impulse values during a static firing test of a rocket motor at the Propulsion Jets Testing Laboratory of the Brazilian Army Technological Center. To estimate the measurement uncertainty of the chain in question, approaches based on combined and expanded uncertainty theories were adopted. These methodologies consider Type A and Type B uncertainties, providing a comprehensive and rigorous analysis. In addition to the uncertainties previously mentioned, the oscillation of the measured signal should also be recognized as a contributing factor to the overall uncertainty in the calculation of total impulse. By incorporating these various sources of uncertainty, we can achieve a more comprehensive and reliable understanding of the uncertainty associated with the measurements obtained from the measurement chain. This analysis yields a measurement uncertainty of 0.24% for thrust and 0.007% for impulse, both calculated at a confidence level of 95.45%. Full article

Plug-in hybrid electric vehicles (PHEVs) typically employ batteries with relatively small capacities due to constraints on chassis space and vehicle cost. Consequently, under conditions such as acceleration and hill climbing, these vehicles often experience high-current battery discharges, which can significantly compromise the battery’s lifespan. To address this issue, this paper focuses on a plug-in hybrid passenger vehicle, introducing supercapacitors to form a hybrid energy storage system (HESS) in conjunction with the PHEV battery, and it proposes a dual-layer energy management strategy for PHEVs. First, a PHEV model is developed, and a rule-based energy management strategy is designed. By conducting simulation comparisons of the CLTC under three control rules with different thresholds, the strategy yielding the lowest fuel consumption is selected, and its battery discharge characteristics are analyzed. Subsequently, the required power parameters of the supercapacitor are calculated, and, taking chassis space constraints into account, the number and specifications of the supercapacitors are determined. Subsequently, a dual-layer energy distribution strategy for PHEVs equipped with supercapacitors is proposed. In the upper layer, an equivalent fuel consumption minimization method is applied to optimize the torque distribution between the engine and the motor, while the lower layer employs a rule-based strategy for power allocation between the battery and the supercapacitor. A proportional feedback factor is introduced for the real-time adjustment of the engine and motor torque distribution, and simulations under the CLTC are conducted to evaluate the PHEV’s torque distribution and fuel consumption. The results indicate that the dual-layer energy management strategy reduces the duration of high-current battery discharge in the supercapacitor-equipped PHEV by 73.61%, decreases the peak current by 30.76%, and lowers the overall vehicle fuel consumption by 5%. Unlike other studies, this paper analyzes and calculates the specifications, dimensions, and quantity of supercapacitors based on the available chassis space of the PHEV passenger car. The results demonstrate that the designed supercapacitor array effectively mitigates the high-current discharge of the PHEV battery, and the proposed dual-layer energy management strategy is capable of reducing the overall fuel consumption of the vehicle. Full article

The current growing interest in lighter-than-air platforms (LTA) has been fueled by the significant development of some enabling technologies, in particular electric motors and on-board electronics. The localization of multiple thrust forces in the layout of the airship, as well as the ability to manage them through automatic control, promises to mitigate the controllability issues connatural to this type of flying craft. Employed on unmanned missions and close to the ground, LTA vehicles now appear to be a technically viable alternative to other unmanned aerial vehicles (UAVs) or low-flying manned machines and are similarly capable of effectively achieving the corresponding mission goals. A key step in establishing the credibility of LTA vehicles as industrial solutions for an end user is an assessment of the economic effort required for producing and operating them. This study presents an analytic approach for evaluating these costs, based on the data available at a preliminary design level for an airship. Three missions currently flown by other types of flying machines were considered, and for each mission the sizing and preliminary design of a LTA platform capable of providing the same mission performance was carried out. Correspondingly, a newly introduced method for the estimation of the cost of a LTA platform was applied. Also, an estimation of the costs currently sustained by operators for each mission was obtained from the available data and with the support of relevant companies, who currently do not fly LTA platforms but operate with more standard flying machines (in particular, multicopter or fixed-wing UAVs or manned helicopters). Finally, the costs corresponding to both currently flying non-LTA vehicles and suitably designed LTA solutions were compared, yielding indications of the emerging economic trade-offs. Full article

Roadway mortality increased during COVID-19, reversing a multi-decade downward trend. The Fatality Analysis Reporting System (FARS) was used to examine contributing factors pre-COVID-19 and in the COVID-19 era using the five pillars of the Safe System framework: (1) road users; (2) vehicles; (3) roadways; (4) speed; and (5) post-crash care. Two study time periods were matched to control for seasonality differences pre-COVID-19 (n = 1725, 1 April 2018–31 December 2019) and in the COVID-19 era (n = 2010, 1 April 2020–31 December 2021) with a three-month buffer period between the two time frames excluded. Four of the five pillars of the safe system had road safety indicators that worsened during the pandemic. Mortality was 19.7% higher for motor vehicle occupants and 45.1% higher for riders of motorized two-wheeled vehicles. In adjusted analyses, failure to use safety equipment (safety belts/helmets) was associated with 44% higher mortality. Two road user groups, non-motorized bicyclists and pedestrians, did not contribute significantly to higher mortality. Urban roadway crashes were higher compared to rural crashes. Additional scientific inquiry into factors associated with COVID-19-era mortality using the Safe System framework yielded important scientific insights to inform prevention efforts. Motorized two-wheeled vehicles contribute disproportionately to pandemic-era higher mortality and constitute an emerging road safety issue that deserves further attention. Full article

Mixed non-motorized traffic is largely unaffected by motor vehicle congestion, offering high accessibility and convenience, and thus serving as a primary mode of “last-mile” transportation in urban areas. To advance stochastic capacity estimation methods and provide reliable assessments of non-motorized roadway capacity, this study proposes a stochastic capacity estimation model based on power spectral analysis. The model treats discrete traffic flow data as a time-series signal and employs a stochastic signal parameter model to fit stochastic traffic flow patterns. Initially, UAVs and video cameras are used to capture videos of mixed non-motorized traffic flow. The video data were processed with an image detection algorithm based on the YOLO convolutional neural network and a video tracking algorithm using the DeepSORT multi-target tracking model, extracting data on traffic flow, density, speed, and rider characteristics. Then, the autocorrelation and partial autocorrelation functions of the signal are employed to distinguish among four classical stochastic signal parameter models. The model parameters are optimized by minimizing the AIC information criterion to identify the model with optimal fit. The fitted parametric models are analyzed by transforming them from the time domain to the frequency domain, and the power spectrum estimation model is then calculated. The experimental results show that the stochastic capacity model yields a pure EV capacity of 2060–3297 bikes/(h·m) and a pure bicycle capacity of 1538–2460 bikes/(h·m). The density–flow model calculates a pure EV capacity of 2349–2897 bikes/(h·m) and a pure bicycle capacity of 1753–2173 bikes/(h·m). The minimal difference between these estimates validates the effectiveness of the proposed model. These findings hold practical significance in addressing urban road congestion. Full article

Research on electric wheel loaders (EWLs) has predominantly focused on battery management, hybrid technologies, and energy recovery. However, the influence of motor types and drivetrains on the drive performance of EWLs has received little attention in previous studies. This case study addresses this gap by examining different EWL configurations and analyzing the drive theory and force requirements by integrating classic vehicle theory with EWL-specific characteristics. The study compares an original EWL, equipped with Permanent Magnet Synchronous Motors (PMSMs) on both the front and rear axles with identical transmission ratios of 22.85, to a modified EWL, which features a Switched Reluctance Motor (SRM) on the front axle and a transmission ratio of 44.05. Walking and shoveling tests were conducted to evaluate performance. The walking test results reveal that, at motor speeds of 200 rpm, 400 rpm, and 600 rpm, energy consumption in R-drive mode is 68.56%, 71.88%, and 74.87% of that in F-drive mode when two PMSMs are used. When an SRM is applied with a transmission ratio of 44.05, these values shift to 73.90%, 70.35%, and 67.72%, respectively. This demonstrates that using the rear motor alone for driving under walking conditions can yield greater energy savings. The shoveling test results indicate that distributing torque according to wheel load reduces rear wheel slippage, and the SRM with a transmission ratio of 44.05 delivers sufficient drive force while operating within a high-efficiency speed range for the EWL. Full article

The global transportation sector is rapidly shifting towards electrification, aiming to create more sustainable environments. As a result, there is a significant focus on optimizing performance and increasing the lifespan of batteries in electric vehicles (EVs). To achieve this, the battery pack must operate with constant current charging and discharging modes of operation. Further, in an EV powertrain, maintaining a constant DC link voltage at the input stage of the inverter is crucial for driving the motor load. To satisfy these two conditions simultaneously during the energy transfer, a hybrid energy storage system (HESS) consisting of a lithium–ion battery and a supercapacitor (SC) connected to the semi-active topology of the bidirectional DC–DC converter (SAT-BDC) in this research work. However, generating the duty cycle for the switches to regulate the operation of SAT-BDC is complex due to the simultaneous interaction of the two mentioned constraints: regulating the DC link voltage by tracking the reference and maintaining the battery current at a constant value. Therefore, this research aims to efficiently resolve the issue by incorporating a highly flexible nonlinear model predictive control (NMPC) to control the switches of SAT-BDC. Furthermore, the converter system design is tested for operational performance using MATLAB 2022B with the battery current and the DC link voltage with different priorities. In the NMPC approach, these constraints are carefully evaluated with varying prioritizations, representing a crucial trade-off in optimizing EV powertrain operation. The results demonstrate that battery current prioritization yields better performance than DC link voltage prioritization, extending the lifespan and efficiency of batteries. Thus, this research work further aligns with the conceptual realization of the sustainability goals by minimizing the environmental impact associated with battery production and disposal. Full article

The well-known Gaussian plume model has proven to be very useful in simulating the atmospheric dispersion of air pollutants (both gaseous and particulates). Nevertheless, the nature of the model presents problems in the actual computation of concentrations when the plume is confined between two parallel boundaries due to the occurrence of multiple reflections. The ground and temperature inversion lid (especially, when the inversion layer is at low levels in the atmosphere) with a chimney stack releasing the effluent below the latter, is one contextual example of horizontal parallel reflecting boundaries. A second example is buildings confining a roadway on either side, with motor vehicles emitting pollution within the street canyon (or urban notch). In such cases, multiple reflections should be accounted for, otherwise the model underpredicts the resulting concentration. This paper presents a mathematical rewriting of the Gaussian plume model equation corrected for multiple reflections when the pollution source is confined between parallel boundaries. The obtained result is most appropriate when the parallel boundaries are rigid, and near-complete reflection is achieved, e.g., street canyon environment (second quoted example). It is worth noting that the relevant mathematical derivations and definitions are all included in the paper to facilitate reading and to ensure comprehensiveness in the presentation. Additionally, the outcome of some preliminary numerical testing is presented. The latter indicates that the new formulation is mathematically stable and yields interesting results. Further numerical investigation and experimental evaluation are merited. Full article

Induction motors (IMs) must meet high reliability and safety standards in mission-critical applications, such as electric vehicles (EVs), where sensorless control strategies are fundamental. However, sensorless rotor speed estimation demands improvements to overcome filtering distortions, tuning complexities, and sensitivity to IM model mismatch. Algebraic methods offer inherent filtering capabilities and design flexibility to address these challenges without introducing additional dynamics into the control system. The objective of this paper is to provide an algebraic estimation strategy that yields an accurate rotor speed estimate for sensorless IM control. The strategy includes an algebraic estimator with single-parameter tuning and inherent filtering action. We propose an EV case study to experimentally evaluate and compare its performance with a typical drive cycle and a dynamic torque load that emulates a small-scale EV power train. The algebraic estimator exhibited a signal-to-noise ratio (SNR) of 43 dB. The closed-loop experiment for the EV case study showed average tracking errors below 1 rad/s and similar performance compared to a well-known sensorless strategy. Our results show that the proposed algebraic estimation strategy works effectively in a nominal speed range for a practical IM sensorless application. The algebraic estimator only requires single-parameter tuning and potentially facilitates IM model updates using a resetting scheme. Full article

Traumatic Brain Injury (TBI) is a debilitating condition that poses a significant public health concern. Historically linked to motor vehicle accidents, the epidemiology of TBI has evolved. Falls now emerge as the predominant cause, particularly among older adults. Sport-related TBIs have also garnered increased attention due to concerns regarding long-term neurological sequelae. To date, therapeutic interventions remain limited and have yet to yield substantial clinical benefits. Cell-based therapies offer promising avenues for neural repair and regeneration: endogenous stem cell therapies capitalize on endogenous pools that can be triggered by the injury and further enhanced by therapeutic approaches. In contrast, exogenous cell therapies provide an exogenous source of cells. However, challenges such as age-related decline in neurogenesis, age-related inflammation, and the heterogeneity of TBI present significant hurdles to overcome. Moreover, translating stem cell research from the laboratory to clinical applications necessitates the adherence to good manufacturing practice standards, which presents distinct obstacles. Addressing these challenges requires a multifaceted approach, including careful patient selection in clinical trials, appropriate experimental models, and the optimization of therapeutic techniques. Ultimately, a combination of strategies is likely to yield the most promising outcomes in the pursuit of effective TBI therapies. Full article

Considering the high-speed and high power density technical specifications of new energy vehicle motors, there is a growing demand for rotor strength as motor peak speeds reach 20,000 r/min and beyond. The utilization of non-oriented silicon steel with a high yield strength in rotors has emerged as a promising approach to increase motor speed. However, the magnetic and mechanical properties of high-strength silicon steel under variable temperature conditions have not been fully explored, particularly in regards to their impact on motor torque, efficiency, and speed. This manuscript investigates the behavior of high-strength silicon steel before and after annealing and at different temperatures, analyzing its influence on high-speed motor performance. The validity and feasibility of this study are confirmed through prototype testing, providing a comprehensive reference for engineering design. Full article