Search Results (90)

This study investigates the development of a tire wear model using finite element techniques. Experimental testing was conducted using the Hoosier R25B slick tire mounted onto a Mustang Dynamometer (MD-AWD-500) in the Automotive Center of Excellence, Oshawa, Ontario, Canada. A general acceleration/deceleration procedure was performed until the battery was completely exhausted. A high-fidelity finite element tire model using Virtual Performance Solution by ESI Group, a part of Keysight Technologies, was developed, incorporating highly detailed material testing and constitutive modeling to simulate the tire’s complex mechanical behavior. In conjunction with a finite element model, Archard’s wear theory is implemented algorithmically to determine the wear and volume loss rate of the tire during its acceleration and deceleration procedures. A novel application using a modified wear theory incorporates the temperature dependence of tread hardness to measure tire wear. Experimental tests show that the tire loses 3.10 g of mass within 45 min of testing. The results from the developed finite element model for tire wear suggest a high correlation to experimental values. This study demonstrates the simulated model’s capability to predict wear patterns, ability to quantify tire degradation under dynamic loading conditions and provides valuable insights for optimizing performance and wear estimation. Full article

The increasing deployment of photovoltaic and energy storage systems (ESSs) in modern power grids has highlighted the critical challenge of component degradation, which significantly impacts system efficiency, operational costs, and long-term reliability. Conventional energy dispatch and optimization approaches fail to adequately mitigate the progressive efficiency loss in PV modules and battery storage, leading to suboptimal performance and reduced system longevity. To address these challenges, this paper proposes a quantum-enhanced degradation pathway optimization framework that dynamically adjusts operational strategies to extend the lifespan of PV storage systems while maintaining high efficiency. By leveraging quantum-assisted Monte Carlo simulations and hybrid quantum–classical optimization, the proposed model evaluates degradation pathways in real time and proactively optimizes energy dispatch to minimize efficiency losses due to aging effects. The framework integrates a quantum-inspired predictive maintenance algorithm, which utilizes probabilistic modeling to forecast degradation states and dynamically adjust charge–discharge cycles in storage systems. Unlike conventional optimization methods, which struggle with the complexity and stochastic nature of degradation mechanisms, the proposed approach capitalizes on quantum parallelism to assess multiple degradation scenarios simultaneously, significantly enhancing computational efficiency. A three-layer hierarchical optimization structure is introduced, ensuring real-time degradation risk assessment, periodic dispatch optimization, and long-term predictive adjustments based on PV and battery aging trends. The framework is tested on a 5 MW PV array coupled with a 2.5 MWh lithium-ion battery system, with real-world degradation models applied to reflect light-induced PV degradation (0.7% annual efficiency loss) and battery state-of-health deterioration (1.2% per 100 cycles). A hybrid quantum–classical computing environment, utilizing D-Wave’s Advantage quantum annealer alongside a classical reinforcement learning-based optimization engine, enables large-scale scenario evaluation and real-time operational adjustments. The simulation results demonstrate that the quantum-enhanced degradation optimization framework significantly reduces efficiency losses, extending the PV module’s lifespan by approximately 2.5 years and reducing battery-degradation-induced wear by 25% compared to conventional methods. The quantum-assisted predictive maintenance model ensures optimal dispatch strategies that balance energy demand with system longevity, preventing excessive degradation while maintaining grid reliability. The findings establish a novel paradigm in degradation-aware energy optimization, showcasing the potential of quantum computing in enhancing the sustainability and resilience of PV storage systems. This research paves the way for the broader integration of quantum-based decision-making in renewable energy infrastructure, enabling scalable, high-performance optimization for future energy systems. Full article

The growing deployment of photovoltaic (PV) and energy storage systems (ESSs) in power grids has amplified concerns over component degradation, which undermines efficiency, increases costs, and shortens system lifespan. This paper proposes a quantum-enhanced optimization framework to mitigate degradation impacts in PV–storage systems through real-time adaptive energy dispatch. The framework combines quantum-assisted Monte Carlo simulation, quantum annealing, and reinforcement learning to model and optimize degradation pathways. A predictive maintenance module proactively adjusts charge–discharge cycles based on probabilistic forecasts of degradation states, improving resilience and operational efficiency. A hierarchical structure enables real-time degradation assessment, hourly dispatch optimization, and weekly long-term adjustments. The model is validated on a 5 MW PV array with a 2.5 MWh lithium-ion battery using real degradation profiles. Results demonstrate that the proposed framework reduces battery wear by 25% and extends PV module lifespan by approximately 2.5 years compared to classical methods. The hybrid quantum–classical implementation achieves scalable optimization under uncertainty, enabling faster convergence across high-dimensional solution spaces. This study introduces a novel paradigm in degradation-aware energy management, highlighting the potential of quantum computing to enhance both the sustainability and real-time control of renewable energy systems. Full article

This article presents the simulation results of synchronization of a permanent magnet synchronous generator (PMSG) dedicated for a hydroelectric plant without power converter devices. The proposed machine design allows to connect a generator to the grid in two different ways. With the first method, the machine is connected to the grid in a similar way as in the case of an electrically excited synchronous generator. The second method is a direct line-start process based on asynchronous torque—similar to asynchronous motor start. Both methods can be used alternately. The advantages of the presented design are elimination of converter devices for starting the PMSG, possibility of use in small and medium hydroelectric power plants, operation with a high efficiency and high power factor in a wide range of generated power, and smaller dimensions in comparison to the generators currently used. The described rotor design allows for the elimination of capacitor batteries for compensation of reactive power drawn by induction generators commonly used in small hydroelectric plants. In addition, due to the high efficiency of the PMSG, high power factor, and appropriately selected design, the starting current during synchronization is smaller than in the case of an induction generator, which means that the structural elements wear out more slowly, and thus, the generator’s service life is increased. In this work, it is shown that PMSG with a rotor cage should have permanent magnets with an increased temperature class in order to avoid demagnetization of the magnets during asynchronous start-up. In addition, manufacturers of such generators should provide the number of start-up cycles from cold and warm states in order to avoid shortening the service life of the machine. The main objective of the article is to present the methods of synchronizing a generator of such a design (a rotor with permanent magnets and a starting cage) and their consequences on the behavior of the machine. The presented design allows synchronization of the generator with the network in two ways. The first method enables synchronization of the generator with the power system by asynchronous start-up, i.e., obtaining a starting torque exceeding the braking torque from the magnets. The second method of synchronization is similar to the method used in electromagnetically excited generators, i.e., before connecting, the rotor is accelerated to synchronous speed by means of a water turbine, and then, the machine is connected to the grid by switching on the circuit breaker. This paper presents electromagnetic phenomena occurring in both cases of synchronization and describes the influence of magnet temperature on physical quantities. Full article

The automated guided vehicle (AGV) is widely used in industrial environments for goods transportation. However, issues such as mechanical wear, reduced battery life, navigation error accumulation, and decreased operational efficiency caused by frequent starts and stops need to be addressed. This paper proposes an improved Autonomous Emergency Braking (AEB) algorithm to tackle these problems. The algorithm employs a stepwise deceleration strategy, effectively reducing the frequency of sudden stops and enhancing the system’s operational smoothness. The AEB algorithm not only considers straight-line driving scenarios but also optimizes deceleration strategies for turning scenarios, adjusting the deceleration detection range according to the turning trajectory. Additionally, a velocity smoothing algorithm is designed to ensure that speed changes during deceleration are gradual, avoiding abrupt speed variations that could impact the system. The feasibility of the AEB algorithm is validated through testing on actual equipment, and its performance is compared to that of a conventional emergency stop strategy. Experimental results show that the AEB algorithm significantly reduces the number of sudden stops, improves the AGV’s operational smoothness and safety, and demonstrates excellent adaptability and robustness across different operational conditions. Full article

Current environmental challenges demand immediate action, especially in the transport sector, which is one of the largest CO₂ emitters. Vehicle electrification is considered an essential strategy for emission mitigation and combating global warming. This study presents methodologies for the modeling and energy management of microgrids (MGs) designed as charging stations for electric vehicles (EVs). Algorithms were developed to estimate daily energy generation and charging events in the MG. These data feed an energy management algorithm aimed at minimizing the costs associated with energy trading operations, as well as the charging and discharging cycles of the battery energy storage system (BESS). The problem constraints ensure the safe operation of the system, availability of backup energy for off-grid conditions, preference for reduced tariffs, and optimized management of the BESS charge and discharge rates, considering battery wear. The grid-connected MG used in our case study consists of a wind turbine (WT), photovoltaic system (PVS), BESS, and an electric vehicle fast charging station (EVFCS). Located on a highway, the MG was designed to provide fast charging, extending the range of EVs and reducing drivers’ range anxiety. The results of this study demonstrated the effectiveness of the proposed energy management approach, with the optimization algorithm efficiently managing energy flows within the MG while prioritizing lower operational costs. The inclusion of the battery wear model makes the optimizer more selective in terms of battery usage, operating it in cycles that minimize BESS wear and effectively prolong its lifespan. Full article

Flight line personnel are constantly exposed to noise and jet fuel while working on flight lines. Studies suggest that jet fuel in combination with noise affects hearing loss more than noise exposure alone. This study examined the combined effects of jet fuel and noise exposure on the hearing of flight line personnel stationed at Japan Air Self-Defense Force Air Bases (Hamamatsu, Matsushima, Hyakuri, Yokota, and Iruma) and US Air Force Air Bases (Kadena and Misawa) in Japan. Samples were collected from all participants, 97 flightline-exposed and 71 control volunteers, to measure their individual noise levels with a personal sound level meter and volatile organic chemicals (VOCs) with a chemical sampling pump during a single shift. Blood samples were collected post shift. Urine samples (entire void) were collected prior to the shift (morning first void) and post shift. VOCs were measured in air, blood, and urine. An audiometric test battery, consisting of immittance measurements, audiograms, distortion product otoacoustic emissions, and the auditory brain response, was conducted after the shift to examine the hearing of participants. Total VOCs in personal air samples were in the ppb range for each group. Tinnitus and temporary hearing loss were reported in audiological histories but were also present in some controls. Noise levels on the flight line were greater than the action level for requiring hearing protection and exceeded exposure limits, but all exposed subjects reported wearing hearing protection. Audiometric tests identified significant differences and trends between flight line and control personnel, indicating the potential for hearing disorders. In spite of very low levels of VOC exposure and wearing hearing protection for noise, there is still the potential for hearing issues in flight line personnel. Full article

Chronic ankle instability (CAI), due to its chronic nature and biomechanical complexity, is well-suited for continuous monitoring and tele-rehabilitation using wearable sensor technology. This study assessed whether a smart insole system, equipped with 4 force-sensing resistor sensors and an inertial measurement unit, combined with functional tests and biomechanical indices, could distinguish CAI patients from healthy controls. A total of 21 CAI patients (23.8 ± 5.1 years) and 16 controls (22.62 ± 2.60 years) completed a battery of functional performance tests while wearing the smart insole system. The results showed an increased medial-lateral pressure ratio in the CAI during heel raise (p = 0.031, effect size = 0.82) and hop tests, suggesting an everted foot position. Significant deviations in center-of-pressure trajectory during double-leg heel raises (p = 0.005, effect size = 1.10) suggested asymmetric motion coordination, while compensatory fluctuations of the lifted limb during single-leg balance tests (p = 0.011, effect size = 1.03) were greater in CAI patients. These findings facilitated the development of features to characterize CAI-specific movement patterns. Together, this system shows promise as a quantitative assessment tool for CAI, supporting improved treatment outcomes through tele-rehabilitation. Full article

A novel self-heating technique is proposed to clear snow from photovoltaic panels as a solution to the issue of winter snow accumulation in photovoltaic (PV) power plants. This approach aims to address the shortcomings of existing methods. It reduces PV cell wear, resource loss, and safety risks, without the need for additional devices. A self-heating current is applied to the solar panel to melt the snow covering its surface, which is then allowed to slide off the panel due to gravity. The proposed system consists of a bidirectional DC-DC converter, which removes the snow cover by heating the solar PV modules using electricity from the grid or electric vehicle (EV) batteries. It also charges the EV battery pack and/or supplies the DC bus when no EV is plugged into the charging station. For each mode of operation, a current-controlled system was implemented using a PI controller and a model predictive controller (MPC). The MPC approach achieved a faster rise time, shorter settling time, very low current ripples, and high stability for the proposed system. Specifically, the settling time decreased from 9 ms and 155 ms when using the PI controller at 20 µs and 35 µs with the MPC controller for both the buck and boost modes, respectively. Full article

A respiration rate (RR) monitoring system was created by integrating a Fibre Bragg Grating (FBG) optical fibre sensor into a respirator mask. The system exploits the sensitivity of an FBG to temperature to identify an individual’s RR by measuring airflow temperature variation near the nostrils and mouth. To monitor the FBG response, a portable, battery-powered, wireless miniature interrogator system was developed to replace a relatively bulky benchtop interrogator used in previous studies. A healthy volunteer study was conducted to evaluate the performance of the developed system (10 healthy volunteers). Volunteers were asked to perform normal breathing whilst simultaneously wearing the system and a reference spirometer for 120 s. Individual breaths are then identified using a peak detection algorithm. The result showed that the number of breaths detected by both devices matched exactly (100%) across all volunteer trials. Full article

Plastics and composites for consumer goods often require flame retardants (FRs) to mitigate flammability risks. Finding FRs that are effective in new sustainable materials is important for bringing them to the market. This study evaluated various FRs in SunBG₉₀ (a composite made from triglycerides and sulfur)—a high sulfur-content material (HSM) promising for use in Li–S batteries, where flame resistance is critical. SunBG₉₀ was blended with FRs from several classes (inorganic, phosphorus-based, brominated, and nitrogen-containing) to assess compliance with UL94 Burning Test standards. Inorganic FRs showed poor flame retardancy and lower mechanical strength, while organic additives significantly improved fire resistance. The addition of 20 wt. % tetrabromobisphenol A enabled SunBG₉₀ to achieve the highest flame retardancy rating (94V-0), while also enhancing wear resistance (52 I_W, ASTM C1353) and bonding strength (26 psi, ASTM C482). Selected organic FRs also enhance compressive strength compared to the FR-free SunBG₉₀. This research highlights the potential of HSMs with traditional FRs to meet stringent fire safety standards while preserving or enhancing the mechanical integrity of HSM composites. Full article

Teaching practices are moving from decontextualised to more representative curricula. Although this is argued to be a positive step, low motor competence is a continual issue in primary-aged school children. One methodological approach to investigate ways to improve motor competence, eye tracking, is moving to more representative tasks. So far, eye-tracking research using static activities has demonstrated a positive association between motor competence and earlier fixation and longer duration. However, this research has been constrained to laboratory settings and tasks, or discrete activities (e.g., throw and catch). This study seeks to understand how to conduct more representative eye-tracking research in primary school-aged children. To this end, thirteen 10–11-year-old children were fitted with an eye-tracker during a typical football coaching session. Children were asked acceptability-based questions, and eye-gaze data were captured to illustrate what children attended to under a representative dynamic football-based activity. Based on the voices of children and captured eye-gaze data, six practical implications for research in this population are proposed: (1) conduct eye-tracking research indoors (where possible); (2) ensure long hair or fringes are secured so as not to obscure line of sight; (3) run the same activity to increase comparability across children wearing the eye-tracker; (4) use a properly fitted backpack (if a backpack is to be used); (5) assure children about the capability and hardiness of the eye-tracker, as they do not need to change the way they move; (6) explain there may be some discomfort with the nose clip, head strap, and battery weight and ensure that children wish to continue. Full article

Eco-driving aims to enhance vehicle efficiency by optimizing speed profiles and driving patterns. However, ensuring safe following distances during eco-driving can lead to excessive use of lithium-ion batteries (LIBs), causing accelerated battery wear and potential safety concerns. This study addresses this issue by proposing a novel, multi-physics-constrained cruise control strategy for intelligently connected electric vehicles (EVs) using deep reinforcement learning (DRL). Integrating a DRL framework with an electrothermal model to estimate unmeasurable states, this strategy simultaneously manages battery degradation and thermal safety while maintaining safe following distances. Results from hardware-in-the-loop simulation testing demonstrated that this approach reduced overall driving costs by 18.72%, decreased battery temperatures by 4 °C to 8 °C in high-temperature environments, and reduced state-of-health (SOH) degradation by up to 46.43%. These findings highlight the strategy’s superiority in convergence efficiency, battery thermal safety, and cost reduction compared to existing methods. This research contributes to the advancement of eco-driving practices, ensuring both vehicle efficiency and battery longevity. Full article

Automated charging technologies are becoming increasingly important in the electrification of heavy road freight transport, especially in combination with autonomous driving. This study provides a comprehensive analysis of automated charging technologies for electric heavy-duty vehicles (HDVs). It encompasses the entire spectrum of feasible technologies, including static and dynamic approaches, with each charging technology evaluated for its advantages, potentials, challenges and technology readiness level (TRL). Static conductive charging methods such as charging robots, underbody couplers, or pantographs show good potential, with pantographs being the most mature option. These technologies are progressing towards higher TRLs, with a focus on standardization and adaptability. While static wireless charging is operational for some prototype solutions, it encounters challenges related to implementation and efficiency. Dynamic conductive charging through an overhead contact line or contact rails holds promise for high-traffic HDV routes with the overhead contact line being the most developed option. Dynamic wireless charging, although facing efficiency challenges, offers the potential for seamless integration into roads and minimal wear and tear. Battery swapping is emerging as a practical solution to reduce downtime for charging, with varying levels of readiness across different implementations. To facilitate large-scale deployment, further standardization efforts are required. This study emphasizes the necessity for continued research and development to enhance efficiency, decrease costs and ensure seamless integration into existing infrastructures. Technologies that achieve this best will have the highest potential to significantly contribute to the creation of an efficiently automated and environmentally friendly transport sector. Full article

Modern concepts in irreversible thermodynamics are applied to system transformation and degradation analyses. Phenomenological entropy generation (PEG) theorem is combined with the Degradation-Entropy Generation (DEG) theorem for instantaneous multi-disciplinary, multi-scale, multi-component system characterization. A transformation-PEG theorem and space materialize with system and process defining elements and dimensions. The near-100% accurate, consistent results and features in recent publications demonstrating and applying the new TPEG methods to frictional wear, grease aging, electrochemical power system cycling—including lithium-ion battery thermal runaway—metal fatigue loading and pump flow are collated herein, demonstrating the practicality of the new and universal PEG theorem and the predictive power of models that combine and utilize both theorems. The methodology is useful for design, analysis, prognostics, diagnostics, maintenance and optimization. Full article