Search Results (5,454)

The influence of standardized driving-cycle characteristics on the dynamic and energy performance of a parallel hybrid electric vehicle controlled by a fixed-gain PID speed controller was investigated. A control-oriented MATLAB/Simulink model was developed, including an electric traction subsystem, an electric battery pack, a simplified internal combustion engine subsystem, a supervisory torque-split controller and longitudinal vehicle dynamics. The same controller configuration was evaluated under the FTP75, HWFET and US06 cycles, with the shorter cycles repeated to obtain comparable durations. Control quality was assessed using RMSE, MAE, IAE and ITAE, whereas energy performance was quantified using battery state-of-charge variation, fuel consumption, engine utilization and traction motor current loading. FTP75 yielded favorable performance, with RMSE = 0.265 m/s, fuel consumption of 4.824 L/100 km and an SoC decrease of 19.698%, whereas US06 proved severe, with RMSE = 4.567 m/s, fuel consumption of 10.328 L/100 km, an SoC decrease of 41.630% and a peak motor current of 580.9 A. Sensitivity analysis showed that ±20% PID-gain variations do not materially alter the principal conclusion, while supervisory energy-management parameters exert a stronger influence on the trade-off between tracking quality, fuel expenditure and charge maintenance. The results confirm that fixed-gain PID control is cycle-dependent and becomes inadequate under aggressive driving conditions. Full article

The shells of testate amoebae are decay-resistant and well preserved in lake sediments, making them excellent biological indicators of climate change. In this study, the identification method for testate amoebae was initially optimized based on the collection of surface sediments from Dali Lake, and statistical analyses were conducted to investigate the community distribution characteristics and key environmental factors driving the testate amoeba species composition. According to the results, the testate amoeba species diversity in the surface sediments of Dali Lake was relatively low. A total of eight species belonging to five genera were identified, and the dominant species were Arcella discoides (35.64% of the total abundance), Phryganella acropodia (24.75%), and Arcella gibbosa (11.39%). All the identified testate amoeba taxa are common in global freshwater sediments, and no new species was discovered in this study. The testate amoeba community composition exhibited strong correlations with the total organic carbon, total nitrogen, dissolved inorganic phosphorus, and total phosphorus and weak correlations with the electrical conductivity and Chlorophyll-a. Full article

Directly coupled electrodynamic vibrators are widely used in vibration testing due to their ability to generate large propulsive forces. However, increasing the propulsive force typically requires higher driving currents, which leads to significant electrical heat generation and thermal management challenges in the armature coil. To address this issue, this study proposes a multi-objective parameter optimization framework for the design of armature coils in high-propulsive-force electrodynamic vibration tables. Two optimization objectives are formulated based on electromagnetic and thermal considerations: minimization of electrical heat generation in the armature coil; and improvement in cooling capability, characterized by the ratio between the cooling water channel area and the conductive cross-sectional area. The key geometric parameters of the coil, including winding configuration and cross-sectional dimensions, are treated as design variables. The resulting multi-objective optimization problem is solved using a multi-objective particle swarm optimization (MOPSO) algorithm to obtain a set of Pareto-optimal solutions that balance the two competing thermal objectives. The present work focuses on the pre-design-stage optimization of the armature coil after the rated propulsive force and geometric envelope of the vibrator have been specified. A representative high-propulsive-force electrodynamic vibrator is analyzed as a case study. Finite element thermal simulations show that the selected Pareto-optimal design reduces the peak armature-coil temperature by approximately 9.7–36.6% compared with the other investigated coil configurations under the same propulsive force condition. The proposed method provides an efficient approach for the thermally constrained parameter design of high-power electrodynamic vibrator armature coils. Full article

There is a growing trend towards vehicles powered by alternative energy sources due to the environmental pollution caused by fossil fuel vehicles. Electric vehicles (EVs) are thought to make a significant contribution to reducing environmental pollution. This study presents a performance comparison of field-oriented control (FOC) and finite control set-based model predictive current control (FCS-MPCC) methods for controlling PMSM motors, which are commonly preferred for EV applications. A multilevel ANPC inverter topology, which has a higher-quality power flow than classical two-level inverters, was preferred to power the PMSM. While the classical FOC method has a fixed switching frequency by including cascaded PI controllers and a pulse width modulation (PWM) modulator, the FCS-MPCC method determines a variable frequency-switching signal that minimizes the cost function by predicting the future current behavior of the PMSM using the mathematical model of the system. The performance comparison of FOC and FCS-MPCC methods was carried out by conducting real-time experimental studies. Both control algorithms were analyzed under variable speed and load conditions using the same motor and drive structure. Performance analysis of FOC and FCS-MPCC control algorithms was carried out in terms of speed tracking, torque, current, and harmonics. According to the results obtained, the total harmonic distortion (THD) value of the stator current was 7.03% in the FOC method, while it was 22.19% in the FCS-MPCC method. Furthermore, a comparative analysis was conducted on the dynamic performance of the two methods in different scenarios using the mean absolute error (MAE), root mean square error (RMSE), integral absolute error (IAE), integrated time absolute error (ITAE), and integral squared error (ISE) criteria. The FCS-MPCC method was observed to be superior in different speed scenarios according to these criteria. In terms of processor load, it was calculated as 17.09% in the FOC method and 63.75% in the FCS-MPCC method. This study is important for determining the control strategy of PMSMs used in EV drives. Full article

Global demand for sustainability drives interest in bioenergy from sustainable feedstock. Agro-industrial waste such as brewer’s spent grains (BSG) is an important by-product of brewing. This study provides a comprehensive review of the current technologies of BSG for energy recovery and BSG-based materials for energy storage applications. The latest scientific progress, not only from conventional processes on anaerobic digestion, combustion, gasification, pyrolysis, torrefaction, and hydrothermal liquefaction but also from several integrated technologies, pretreatment methods, and additives/catalysts regarding the improvement of energy efficiency and process sustainability, was reviewed. In addition, the co-feedstock practices (co-combustion, anaerobic co-digestion, hydrothermal co-liquefaction, anaerobic co-fermentation) and co-production were examined. AD of BSG yields about 302 NL CH₄/kg COD, generating roughly 0.39 kWh of electricity/kg BSG and 1.71 MJ of thermal energy/kg BSG. Ultrasonic pretreatment enhances methane production up to four times (107 L CH₄/kg TVS) and reduces CO₂ emissions by 0.083 t CO₂eq/t BSG. Anaerobic co-digestion of BSG with other brewery waste increased the yield up to 88 mL CH₄/g TVS, generated approx. 0.348 kWh/kg TVS electricity, and reduced emissions by 0.114 kg CO₂eq/kg TVS. Bioethanol yields can reach 72%, while biohydrogen generation was up to 5154 mL H₂/g glucose. BSG pyrolysis provides up to 71.8% bio-oil, and its calorific value is 18–25 MJ/kg. BSG-derived activated biocarbon has a notable surface area (1792 m²/g) for lithium–sulfur batteries. The assessment showed that BSG’s transformation into bioenergy and energy storage materials aligns with waste reduction and sustainable development goals. However, future research on combined alternative wastes, integrated technologies, green nanotechnology, and artificial intelligence technology could lead to optimal performance and facilitate their industrial application. Full article

The spatial organization of microscale fillers is critical for macroscopic performance, yet precise control over their distribution and orientation remains a major challenge in direct ink writing. Here, we present an acoustofluidic capillary nozzle that integrates acoustic manipulation into direct ink writing, enabling programmable in situ assembly of functional fillers during extrusion. By coupling a piezoelectric transducer with a commercial glass capillary, stable acoustic standing waves are established within the flow channel, driving suspended filler particles toward pressure nodes via acoustic radiation forces. Simulations and experiments systematically investigate how capillary geometry and material properties influence acoustic energy distribution and particle assembly behavior. In particular, rectangular capillaries generate stable multi-node standing waves, inducing periodic alignment of nickel-coated carbon fibers into ordered conductive bundles. This acoustically programmed microstructure reduces the percolation threshold from 8 wt% to 2 wt% and enhances electrical conductivity by up to 32.1-fold at identical filler contents. Meanwhile, the composites exhibit pronounced anisotropic conductivity and maintain excellent mechanical flexibility, with stable electromechanical performance under 16% bending strain and cyclic loading. This work demonstrates a simple and scalable acoustofluidic nozzle platform for programmable microstructure engineering in direct ink writing, offering new opportunities for fabricating high-performance multifunctional composites. Full article

In order to improve the speed regulation accuracy, dynamic response and operation robustness of an automatic guided vehicle (AGV) in a complex road disturbance environment, this paper studies an adaptive AGV speed regulation system based on EKF state estimation on the basis of AGV dynamics modeling and adaptive control. Firstly, through the electrical-mechanical coupling modeling of the AGV drive system, state space construction and external unknown disturbance equivalent design, a unified electromechanical coupling simulation and physical verification environment is built, which lays a model foundation for the research of the speed control algorithm. Secondly, based on the optimal control model of PID and LQR with first-order lead compensation, an EKF adaptive speed regulation model is constructed by combining the extended Kalman filter and adaptive control to realize the online estimation and dynamic compensation of unknown disturbances. Finally, based on MATLAB/Simulink simulation platform and the STM32 embedded experimental platform, the rationality and robustness of the proposed speed control strategy are verified by speed-mutation conditions, load-disturbance condition and a physical verification experiment. The results show that the overshoot of the EKF adaptive control strategy is only 1.8%, which is 84.1% lower than that of PID control and 61.7% lower than that of LQR control. The rise time is 42% shorter than PID and 23% shorter than LQR. The recovery time under load disturbance is 58% shorter than that of PID and 31% shorter than that of LQR. EKF adaptive control is significantly better than PID and LQR in overshoot, rise time and control stability. The disturbance rejection ability and dynamic recovery speed are greatly improved, which can ensure the high robustness and smooth operation of the AGV speed control system under complex working conditions, effectively enhance the response and compensation ability of the system to sudden disturbances, and better meet the actual needs of AGV speed control in complex engineering scenarios. Full article

Reliable battery health assessment is essential to accelerate battery electric vehicle (BEV) adoption, yet most existing in-vehicle methods do not capture the complex processes driving ageing. Electrochemical impedance spectroscopy (EIS) offers deeper diagnostic insight but faces significant architectural and integration constraints. This study establishes a rigorous system-level framework for practicable onboard EIS implementation, focusing on the integration within Battery Management System (BMS) and powertrain architectures. Various integration topologies for cell-, module- and pack-level EIS are evaluated, highlighting their key trade-offs. The viability of the presented architectures is assessed through an application-specific Multi-Criteria Decision Analysis (MCDA) for mass-market, high-performance and circular economy use-cases. This study confirms the feasibility of onboard EIS while providing industry and scientific stakeholders with practical guidance to advance battery diagnostics for next-generation BEVs. Full article

This study presents an energy-based assessment of a battery electric sport utility vehicle (SUV) tested under high-altitude and high-gradient real-world conditions in Ambato, Ecuador, at approximately 2500 m above sea level. A low-cost instrumentation setup composed of a Global Navigation Satellite System (GNSS) device, a Fluke 393 FC clamp meter, and an On-Board Diagnostics II (OBD-II) interface was used to evaluate zero, positive, and negative road-gradient conditions in Normal and Sport driving modes. The results show that positive gradients increased the acceleration energy from 0.0454 to 0.0658 kWh in Normal mode and from 0.0351 to 0.0535 kWh in Sport mode. In contrast, negative gradients favored regenerative braking, with Normal mode reaching a net energy balance of $-0.0249$ kWh and a segment-level recovery ratio of 194.38%. This value reflects the contribution of gravitational potential energy. Sport mode showed lower regenerative performance, particularly during uphill operation, where the recovery ratio decreased to 8.96%. These findings demonstrate that low-cost instrumentation can capture representative route-level energy trends and support real-world electric vehicle (EV) energy assessment in topographically complex high-altitude environments. Full article

The transition toward sustainable power systems requires load forecasting methods that can support renewable integration under increasing uncertainty. However, many deep learning models mix historical load, temporal priors, and external drivers in black-box structures, and often assume that true future driver values are available. To address these issues, this study proposes ADDF (Automatic Driver Discovery and Fusion), a semi-explicit self-driven framework for multi-step load interval forecasting. ADDF organizes historical load, calendar priors, and external drivers into three functional branches to distinguish load inertia, temporal regularity, and external forcing. The Driver Branch estimates future driver states under practical information constraints and uses dynamic gating to screen useful driving information. The three branch representations are adaptively integrated through Three-Way Fusion, followed by bounded residual correction to generate multi-step quantile forecasts. Experiments on the Panama electricity load dataset and ETTh1 dataset under one-step and 24-step settings show that ADDF achieves competitive point accuracy and interval prediction performance. Mechanism analyses indicate that the proposed branch-level structure provides clearer interpretability than post-hoc black-box explanations. The framework offers uncertainty-aware forecasting support for sustainable power system operation, including day-ahead scheduling, reserve planning, and energy management. Full article

Fault detection and diagnosis of three-phase inverter-fed motor drives is essential for ensuring system reliability, safety, and continuous operation in applications such as electric vehicles and industrial automation. This paper proposes a data-driven fault detection framework based on normalized current features and a lightweight bidirectional long short-term memory (BiLSTM) network which can be generalized to different motor power rating in the same controller system. A compact set of six time-domain features, consisting of the mean and root-mean-square (RMS) values of the phase currents, is extracted and normalized with respect to the average RMS value. This normalization effectively removes dependency on operating conditions, enabling the model to generalize across different load levels and motor power ratings without retraining. A lightweight BiLSTM architecture is employed, reducing computational complexity while maintaining high diagnostic performance. The proposed method is validated under various operating conditions, including different speeds, load variations, motor power ratings, and noisy conditions. The results demonstrate an overall classification accuracy of 99.65%, with reliable fault detection achieved within less than half of a fundamental cycle. The proposed approach provides an efficient, robust, and scalable solution for inverter fault detection and diagnosis, offering strong potential for practical deployment in modern motor drive systems. Full article

High-entropy MAX phases (TiVNbTa)₂AlC have attracted increasing attention due to their potential advantages in structural stability, damage tolerance, and mechanical reliability under complex service environments. This work studied the crystal and electrical structures with the elastic properties, the synthesis reactions and further wear resistance of HE-MAX (TiVNbTa)₂AlC theoretically and experimentally. The charge transfer between both M-C atoms and M-Al atoms turned more intense, which correspondingly strengthened the M-C and M-Al bonds, respectively. Because of the dope on M-sites, (TiVNbTa)₂AlC exhibited larger fracture toughness K_IC and a lower brittle index M, which suggested lower brittleness, better crack extension resistance, and higher damage tolerance than Ti₂AlC. In this work, high-entropy (TiVNbTa)₂AlC MAX phase ceramics were successfully synthesized by a powder metallurgy route combined with pressureless sintering and spark plasma sintering (SPS). The effects of raw material composition and sintering temperature on phase evolution, microstructure formation, mechanical properties, and tribological behavior were systematically investigated. The results show that a highly pure (TiVNbTa)₂AlC phase with a phase fraction of 96.8% could be obtained at a molar ratio of M:Al:C = 2:1.2:0.8 and a sintering temperature of 1550 °C. Phase evolution analysis indicated that the reaction process followed the sequence of intermetallic compound (IMC) formation → carbide formation → MAX phase formation. Severe lattice distortion induced by the multi-principal-element solid solution significantly enhanced the hardness of the material, which was markedly higher than that of conventional ternary MAX phases. Owing to its higher hardness and more homogeneous solid-solution structure, HE-MAX (TiVNbTa)₂AlC could inhibit the formation of surface microcracks and reduce the driving force for crack propagation to some extent. Therefore, the lower wear rate not only reflected improved tribological performance but also demonstrated the beneficial role of high-entropy design in enhancing resistance to surface damage. Full article

Groundwater contamination by redox-sensitive elements (RSEs) such as arsenic (As), chromium (Cr), vanadium (V), and selenium (Se) pose a critical challenge in alluvial aquifers, where seasonal hydrological forcing drives dynamic hydrogeochemical and redox conditions. This study investigates the seasonal evolution of groundwater hydrogeochemistry and multi-metal behavior in shallow aquifers of the Mid-Gangetic Plain, India, with particular emphasis on the role of seasonal redox decoupling. Monsoon conditions were dominated by strongly reducing environments (ORP: −150 to −70 mV), predominantly Ca–Mg–SO₄ and Na–Cl type facies. Under these conditions, significant correlations among RSEs in particular (As–V, As–Se) indicated coupled mobilization governed by the reductive dissolution of Fe–Mn (oxyhydr)oxides. Monsoon groundwater also exhibited strong associations between RSEs and agronomic indicators (NO₃⁻, SO₄²⁻), suggesting the influence of recharge-mediated agricultural inputs on redox-sensitive geochemical processes. In contrast, post-monsoon conditions showed a clear transition to sub-oxic states (ORP up to +121 mV) and were dominated by Ca–Mg–HCO₃ facies, accompanied by substantial increases in bicarbonate (~372%), electrical conductivity (~62%), and total dissolved solids (~21%). Despite the partial oxidation of the aquifer system, redox-sensitive metals did not respond uniformly. Instead, inter-element correlations weakened or disappeared, indicating a transition from coupled to decoupled contaminant behavior. Arsenic concentrations increased up to 20.8 µgL⁻¹, whereas Cr and V displayed variable enrichment controlled by alkali-induced desorption and carbonate-mediated surface interactions. This transition reflects seasonal redox decoupling, whereby seasonal redox shifts lead to metal-specific rather than coordinated multi-metal behavior. We propose a Seasonal Redox Decoupling Framework (SRDF) to explain the shift from coupled reductive release during monsoon conditions to selective mobilization pathways in the post-monsoon period. These findings demonstrate that seasonal redox shifts control not only metal concentrations but also inter-element relationships, leading to metal-specific risk profiles. This underscores the need for seasonally adaptive monitoring and management strategies in hydrologically dynamic alluvial aquifers. Full article

This article describes the study of electric drive performance with a basic scalar control algorithm for an induction motor affected by wear and tear and specific inverter characteristics. The deterioration or defect pattern of the electric motor is represented as a resulting change in the magnetizing inductance. We cover methods of mathematical and simulation modeling, along with an analysis of the equivalent circuit parameters of an induction motor according to its technical specifications. The mathematical and simulation models of three inverter configurations are shown, both with and without distortion and voltage drop. The influence of each factor on output signal waveforms is evaluated. Laboratory bench tests were conducted, proving the adequacy and reliability of the models. The simulation and experimental results support the hypothesis that the energy characteristics of an electric drive can be preserved during degradation, taking into account the specifics of the control system and the inverter. We outlined the main conclusions and provided practical recommendations for applying each of the considered inverter models in electric drive systems. Full article

The end-to-end design and successful integration of a low-voltage educational electric vehicle (EV) built around a Controller Area Network (CAN) backbone is presented in this study. Its reproducible system architecture was built on a unified message specification database, and a set of bring-up and diagnostic procedures enables students to assemble, validate, and extend an EV using commodity controllers. The vehicle was manufactured and commissioned with classic-CAN operating at 250–500 kbps, integrating traction, battery management system, dashboard, lighting, and safety nodes. Initial tests confirmed reliable messaging and error-free operation under typical campus driving conditions. In addition, an upgrade path to CAN with flexible data-rate and 100BASE-T1 Ethernet is provided for future curricula. The platform reduces integration complexity, shortens fault-finding, and supports multidisciplinary teaching across mechanical engineering, electrical and computer engineering, and computer science. Full article