Search Results (228)

Accurate state-of-charge (SOC) estimation is essential for battery management systems (BMSs) under dynamic operating conditions. This study proposes a lightweight data-driven SOC estimation framework based on a tensor-product bivariate polynomial surface. During offline model identification, the reference SOC and measured current are used to establish an explicit terminal-voltage surface (V = f(I, SOC)). During SOC estimation, however, the reference SOC is no longer used as an input; instead, SOC is directly recovered from the measured terminal voltage and current by inversion of the identified polynomial surface. The method used in this study is developed and evaluated using three public dynamic driving-cycle datasets, including DST, FUDS, and UDDS. To reduce temporal information leakage, the datasets are partitioned chronologically, and the polynomial orders are selected using five-fold forward-chaining cross-validation. The selected third-order current and fifth-order SOC polynomials provide a compact 24-coefficient representation. Direct comparison between the estimated and reference SOC yields an RMSE of 2.91% on the DST test set, while the corresponding RMSE values on FUDS and UDDS are 3.18% and 3.54%, respectively. The results demonstrate that the proposed approach provides a computationally lightweight and interpretable SOC estimation framework without requiring recursive filtering or online equivalent-circuit parameter identification. Full article

Electric-vehicle gearboxes remain key design elements because they determine how motor speed and torque are converted into wheel speed and tractive effort over a driving cycle. This design-oriented scoping review synthesizes EV gearbox architectures, gear ratio selection, efficiency losses, NVH, planetary and compound planetary systems, lubrication, thermal behavior, reliability, manufacturability, and cost within one evidence-informed architecture-selection perspective. A structured search of Scopus, IEEE Xplore, ScienceDirect, SpringerLink, and MDPI identified 312 records; after removal of 71 duplicates, screening of 241 titles and abstracts, and full-text assessment of 61 articles, 40 sources formed the reproducible structured-search core. A gap-directed supplementary search then added 12 sources in underrepresented areas, producing a 52-source synthesis set. The revised analysis reports publication trends, evidence-level distributions, technical-focus frequencies, and a dimension-separated evidence-count table for ratio count, gear train topology, and integration level. The evidence indicates that single-speed reduction gearboxes remain the mature baseline for many passenger EVs, whereas two-speed, multi-speed, planetary, compound planetary, and integrated e-axle solutions require application-specific justification based on system-level benefits and risks. An illustrative screening calculation demonstrates the framework logic without being presented as production-level validation. The principal gaps are experimentally validated loss and NVH maps, coupled efficiency–thermal–lubrication–durability analysis, reliability-aware mission-profile validation, standardized benchmarks, and transparent comparison of compound planetary and integrated e-axle systems. Across heterogeneous study conditions, reported energy benefits range from 2.4% for fixed-ratio optimization to 15% for selected multi-speed comparisons; these results are not pooled because the vehicles, motor maps, drive cycles, loss models, and validation methods differ. Full article

The automotive industry is evolving toward Software-Defined Vehicles (SDVs) enabled by centralized computing, cloud integration, and Over-the-Air (OTA) updates. Yet, prevailing SDV and Internet of Vehicles (IoV) simulators often treat each vehicle as a single monolithic node, obscuring the interplay between internal vehicle modules and the surrounding infrastructure in dense urban scenarios. This work proposes a modular SDV reference architecture embedded in a Software-Defined Internet of Vehicles (SD-IoV) framework together with a Software-in-the-Loop (SiL) co-simulation testbed built on Objective Modular Network Testbed in C++ (OMNeT++), Simulation of Urban MObility (SUMO), and Vehicles in Network Simulation (Veins). The architecture decouples perception, communication, decision, and actuation into typed replaceable modules and instantiates them across six co-existing agent types: an SDV; two human-driver vehicle classes with cognition modelled as a multi-stage Eye–Ear–Brain–Hand–Foot pipeline with reaction-delay sampling; a public transport bus; a Roadside Unit (RSU); and a Traffic Light (TL). Three platform-level mechanisms connect the agents to the infrastructure: a single shared world model with a three-layer line-of-sight funnel that serves visual-sensor queries and reuses the building polygons of the wireless shadowing model; a dual-CPU mobile-fog node implementing a cycles-per-frequency workload model with explicit end-to-end latency decomposition; and a three-plane intersection coordination fabric that combines 802.11p wireless with a wired RSU-to-TL star and a wired peer mesh between adjacent TLs. The initial results confirm that the implemented message paths and module interactions behave as specified, including directional Signal Phase and Timing (SPaT) reception, cross-junction handover, bus-side fog-offload latency accounting, and passive identification of Vehicle-to-Everything (V2X)-silent vehicles. Several architecture elements are specified but deliberately not exercised in the present evaluation and remain design targets for future work: the Roadside Unit (RSU) route planning and fog computing companion (and any multi-tier offloading comparison), non-line-of-sight SPaT reception, and a safety violation detection layer. Within the above scope, the testbed is positioned as a reusable foundation for module-level SDV research and as a basis for future extensions such as Joint Communication and Sensing (JCAS), energy-aware driving, and Hardware-in-the-Loop (HiL) integration. Full article

Future traffic scenarios will involve autonomous vehicles (AVs) interacting with other road users, including cyclists. For this interaction to be safe and socially acceptable, there is a need for development and testing environments that enable scenarios between AVs and cyclists including ethical, logistical, and safety constraints. While human-in-the-loop transport simulation offers an alternative, achieving high behavioral validity requires maximizing user presence and control. This study evaluates a modular, multi-sensory cycling simulator built on the CARLA autonomous driving engine. Through a fully randomized and counterbalanced within-subjects design with 31 participants, we analyzed four simulator configurations incorporating combinations of stereoscopic visualization, speed-dependent haptic eolic feedback, first-person visual self-embodiment, and 3D spatialized audio. Non-parametric statistical evaluations revealed significant differences across the groups (${\chi }^2\left(3\right)=60.47$, $p<0.001$, $W=0.650$). Post-hoc pairwise comparisons indicated that, while head-mounted displays (HMDs) provide a level of spatial orientation, the concurrent integration of visual self-embodiment and speed-dependent airflow acts as a unifying sensory cue, significantly increasing system coherence. Conversely, the addition of 3D spatialised audio yielded no statistically significant improvement ($Z=-0.06$, $p=1.000$), suggesting a sensory saturation effect. Cross-dimensional analysis confirmed a positive correlation between perceived presence and interface control ($\rho =0.693$, $p<0.001$). These findings suggest that a combination of visual feedback, haptic airflow, and self-embodiment may be enough to provide a realistic environment for cyclist–AV interaction studies including multiple agents. Full article

Laboratory-based certification cycles systematically underestimate real-world fuel consumption and CO₂ emissions. On-board diagnostics (OBD-II) telemetry offers a low-cost alternative, yet most published approaches rely on mass air flow (MAF) sensors absent from many modern vehicles. This study validates a speed-density air-mass estimation method on a naturally aspirated RON 95 gasoline passenger car (1368 cm³, Euro 6) across seven drive cycles recorded over three measurement days in northwestern Türkiye, covering 609.6 km of highway, urban, and mixed conditions. Instantaneous air mass flow was estimated from four standard OBD-II PIDs—manifold absolute pressure, engine speed, intake air temperature, and fuel trim corrections—using the ideal gas law applied to actual engine displacement. Results were validated against pump-measured fill-up volumes. The speed-density model achieved errors of −3.6% to +4.3% across individual segments (combined error: −0.5%), outperforming the vehicle’s onboard trip computer, which exhibited errors of −10.6% to +14.6%. Derived CO₂ intensities ranged from 125.0 to 166.4 g/km, with a combined average of 147.2 g/km (pump reference: 147.9 g/km). Urban driving produced approximately 15% higher specific emissions than highway driving. These results demonstrate that a physics-based speed-density model can achieve within ±5% trip-level accuracy across diverse real-world conditions without machine learning, bespoke calibration, or a physical MAF sensor. Full article

Driven by China’s “dual carbon” (carbon peak and carbon neutrality) goals and the national strategy of new-type urbanization, prefabricated construction has emerged as a pivotal pathway toward industrialized and sustainable development in the construction sector—leveraging its distinctive advantages in construction efficiency, cost optimization, environmental performance, and design adaptability. Nevertheless, the inherently sequential and interdependent nature of the full construction process—encompassing off-site component manufacturing, logistics transportation, and on-site assembly—introduces pronounced cross-stage risk transmission mechanisms, with prefabricated components serving as critical risk carriers. Such transmission dynamics significantly impede the scalable and safe deployment of prefabricated construction. To date, scholarly efforts on construction safety in prefabricated buildings have predominantly addressed isolated, stage-specific risks, falling short in quantitatively modeling the coupled propagation of risks across stages, accommodating epistemic uncertainties and latent (i.e., unknown or unobserved) risks, and informing targeted, evidence-based mitigation strategies. To bridge this gap, this study develops a rigorous quantitative framework for assessing cross-stage risk transmission in prefabricated construction safety. Specifically, it aims to (i) uncover the structural patterns and driving mechanisms underlying inter-stage risk propagation; (ii) reduce the likelihood of safety incidents throughout the construction life cycle; and (iii) deliver actionable theoretical insights and methodological guidance for practitioners and policymakers. Methodologically, we first conduct a systematic identification of safety-critical risk factors and establish a hierarchical risk indicator system comprising three first-level dimensions and twenty second-level indicators. Second, using the Decision-Making Trial and Evaluation Laboratory (DEMATEL) method, causal relationships among risk factors are clarified, while incorporating the Leaky Noisy-or Gate (LNOG) extended model to account for unknown risks. Risk data are processed using triangular fuzzy functions, and a Bayesian network (BN) topology diagram is constructed via the GeNIe 5.0 platform, forming a DEMATEL-LNOG-BN-based model for assessing cross-phase risk transmission. Finally, applying the model to an actual project—”a prefabricated construction project in Shanghai”—the study conducts a cross-phase risk transmission analysis. Through forward probability inference, backward causality tracing, sensitivity analysis, and pathway decomposition, sensitivity comparisons are performed under different LNOG unknown risk parameters. Results are compared with those from the traditional DEMATEL-BN model to validate the stability and consistency of high-sensitivity risk factor identification, comprehensively verifying the applicability and predictive reliability of the proposed DEMATEL-LNOG-BN model. The study quantitatively reveals the progressive diffusion and amplification mechanisms of risks across the production–transportation–assembly process, providing scientific support and practical reference for precise safety risk prevention, critical node control, and the optimization of management systems in prefabricated construction sites. Full article

Background: Escherichia coli remains a critical multidrug-resistant nosocomial pathogen, driving interest in bacteriophage-based biocontrol. The genus Kayfunavirus (family Autotranscriptaviridae) exhibits obligately lytic replication cycles and favorable biosafety profiles, yet each new phage requires comprehensive genomic characterization to expand therapeutic candidate pools. This study aimed to isolate and genomically characterize a novel Kayfunavirus from an environmental reservoir in Vietnam. Methods: Escherichia phage vB_EcoA-Sparklingdew was isolated from Can Tho River water using host E. coli AgE9. The genome was assembled using SPAdes. The termini were resolved with PhageTerm. The annotation was done via the Pharokka pipeline and HHpred. Taxonomic classification was performed using taxMyPhage, VIRIDIC intergenomic comparisons, and maximum likelihood phylogeny of concatenated structural proteins. Results: The complete genome comprises a 37,944 bp linear dsDNA molecule (49.9% GC), encoding 51 open reading frames in a predominantly unidirectional arrangement. Key features include a virion-encoded T7-like RNA polymerase, a 723-residue T7-like DNA polymerase, a canonical lysis triad, and two putative tailspike proteins. A 212 bp direct terminal repeat and coverage profiles support a headful (pac) packaging mechanism. Comprehensive screening confirmed the absence of lysogeny, virulence, and antibiotic resistance determinants. A single synonymous SNP indicated high clonal purity. Intergenomic identity peaked at 87.7% against ICTV references, confirming placement in a novel species. Conclusions: Phage Sparklingdew represents a strictly lytic Kayfunavirus with a compact genomic architecture. Its favorable safety profile and absence of temperate markers support further evaluation for targeted therapeutic applications against pathogenic E. coli. Full article

This paper presents the design and implementation of a field-programmable gate array (FPGA)-based System-on-Programmable-Chip (SoPC) architecture for sensorless speed control of permanent magnet synchronous motor (PMSM) drives. To enable real-time execution of the computationally intensive estimation stage, a parallelized Unscented Kalman Filter (UKF) is proposed for the joint estimation of rotor speed, position, and load torque. Unlike traditional sequential processor-based UKF implementations, the proposed parallel architecture simplifies the iterative process and significantly reduces computational latency and hardware resource utilization while preserving high estimation fidelity. This transformation reduces the number of sequential dependency stages within one estimation cycle and enables simultaneous execution of matrix operations using dedicated FPGA resources, thereby decreasing effective iteration latency. The complete control system comprises current regulators, a coordinate transformation module, a proportional–integral (PI) speed controller, and auxiliary functional blocks—all fully integrated within a single SoPC. The UKF estimator and control components are described using a hardware description language (HDL), enabling efficient hardware-level parallelism and real-time execution. The proposed system is validated through co-simulation and experimental verification on a Xilinx ZCU102 platform driving an inverter-fed PMSM. The results confirm correct real-time operation of the proposed architecture and demonstrate its feasibility for FPGA-based sensorless motor drive implementation. A detailed quantitative comparison with a fully sequential FPGA-based UKF implementation is identified as future work to further substantiate the reported latency reduction. Full article

Non-exhaust emissions represent a growing share of traffic-related particulate matter and are increasingly addressed by regulatory frameworks. This study presents a comparison of two brake emission test cycles, the California Brake Dynamometer Cycle (CBDC) and Worldwide Harmonized Light Vehicles Test Procedure Braking Cycle (WLTP-BC), the latter being formally embedded in current regulations. Firstly, we present a detailed comparison of WLTP-BC and CBDC in terms of parameters that are shown to affect or may affect braking control and particle emissions (braking torque, vehicle speed, acceleration, friction work, disc temperature, etc.). Secondly, we present a way to evaluate test system torque control quality, supplementing the friction work-based method present in United Nations Regulation (UNR) No. 179, and quantitatively assess the control quality between the cycles. CBDC was found to be the more challenging cycle to execute. However, the testbench control architecture was found to be sufficient to execute it with high fidelity. Thirdly, we present the emission results obtained from the cycles, both per kilometre driven, as well as per friction work done. We argue that the latter is better for comparing the emission results obtained from cycles with different braking profiles. Driving the CBDC resulted in higher particle mass (PM) emissions but similar particle number (PN) emissions. In light of this dataset, friction work seems to be a better predictor for PN than for PM. While this study encompasses only a single friction pair, and more tests with different brakes would be required to generalize the findings, the results highlight the importance of cycle selection in emission research, both in terms of quantifying the emissions and demands for the test system. Full article

Sweeping jet (SWJ) actuators are widely used in active flow control, but explicitly resolving actuator-scale unsteadiness in full-configuration computational fluid dynamics (CFD) remains prohibitively expensive because of the small geometric scales and high-frequency oscillations involved. Existing reduced-order boundary-condition models constructed from exit-plane data alone can reproduce the observed switching waveform, but they treat the actuator as an input–output black box and provide limited insight into the internal dynamics that generate the response. This work develops a causal state-space reduced-order modeling framework that links internal mixing-chamber dynamics to time-resolved exit-plane boundary conditions. Proper orthogonal decomposition (POD) is used to obtain a low-dimensional representation of the internal flow, and a data-driven linear evolution operator is identified in the reduced space by least-squares regression of successive snapshot pairs. A POD truncation rank of $r=60$ is selected from cumulative-energy and validation-error sensitivity analyses, capturing well above 99% of the fluctuation energy while lying within the converged performance regime. A corresponding reduced operator is identified for the exit plane, and spectral comparison reveals near-neutrally stable oscillatory modes in both regions. Using a $\pm 1\%$ relative frequency-matching tolerance, the dominant reduced-operator modes exhibit a 28.3% frequency overlap, providing operator-level evidence that exit-plane oscillations are dynamically linked to internal coherent structures. This correspondence is further supported by cross-spectral coherence analysis between representative internal and exit-plane probe signals, which shows strong coherence at dynamically relevant frequencies. A delayed causal output mapping is then formulated in which the internal reduced state drives the exit-plane response after an identified lag of 149 time steps, corresponding to $2.98\times {10}^{-3}$ s. This delay provides a physically interpretable convective transport timescale from the mixing chamber to the actuator exit. Over the validation interval, the model maintains a mean relative $L_2$ error below 0.02, with maximum normalized errors below 0.04 for most of the prediction horizon, and localized increases are confined to rapid jet-switching events. Field-level reconstructions of streamwise velocity and total pressure show that the model captures both phases of the jet-switching cycle, with errors concentrated primarily in high-gradient shear-layer regions. Compared with exit-only reduced-order models, the proposed internal-driven formulation improves amplitude and phase fidelity over extended prediction horizons. The resulting framework provides a compact, interpretable, operator-based representation of SWJ actuator dynamics suitable for use as a CFD-embeddable dynamic boundary condition. Full article

The escalating financial burden of deteriorating civil infrastructure worldwide necessitates a shift from conventional repair methods towards more durable and economically efficient long-term solutions. This paper presents a comprehensive techno-economic review of using carbon fibre-reinforced polymer (CFRP) fabrics for structural strengthening. Moving beyond a simple first-cost comparison, this review utilizes a life-cycle cost analysis (LCCA) framework to evaluate the total cost of ownership. The analysis deconstructs the complete cost profile, demonstrating that while CFRP systems have a high initial material cost, this is frequently offset by substantial savings in labour, equipment, and, critically, the indirect costs associated with reduced construction time and operational disruption. Furthermore, the inherent corrosion immunity of CFRP virtually eliminates future maintenance and repair expenditures, leading to a lower total life-cycle cost compared to traditional steel or concrete-based methods in a wide range of applications. Specifically, the conducted LCCA case study demonstrates that the CFRP alternative can reduce total life-cycle costs by nearly 25% relative to conventional steel sheet bonding, overwhelmingly driven by minimized operational downtime and related indirect costs. The value proposition is shown to be context-dependent, driven by minimizing user delay costs in bridges, mitigating catastrophic risk in seismic retrofitting, preserving cultural value in heritage structures, and maximizing revenue uptime in industrial facilities. The review also examines market dynamics, including the roles of standardization and government policy in driving adoption, and explores future trends such as inorganic matrix composites (TRM/FRCM), integrated structural health monitoring (SHM), and the push towards a circular economy. The findings conclude that a holistic, life-cycle-based economic assessment establishes CFRP strengthening as a cornerstone technology for the sustainable and resilient management of modern civil infrastructure. Full article

Silicon carbide (SiC) power devices are emerging as an alternative for electrical transportation systems to improve energy efficiency, reduce carbon emissions, increase power density, and enable long-term cost savings throughout the product life cycle. Thus, a fair comparison with state-of-the-art Silicon (Si) technology is required to justify the productization of SiC devices. This work performs a systematic investigation of both technologies at the device and system levels for distinct power module voltage classes (3.3 and 6.5 kV) and circuit topologies. Initially, experimental characterization of state-of-the-art power modules is performed, followed by energy efficiency characterizations at the power converter level. Then, an electrothermal simulation model was built and validated based on experimental results. Accurate system simulations of commercial two- and three-level traction topologies were developed, focusing on efficiency over the entire load range, loadability, potential energy savings under realistic train drive cycles, and a financial comparison of inverter prices per kW. SiC exhibits lower loadability degradation at high switching frequencies (>500 Hz) than Si technology. Energy-saving potentials of 40–70% in the traction inverter with a guaranteed return on investment during the converter’s lifetime are achieved by substituting Si with SiC inverters. In addition, massive energy savings of up to 200 MWh per inverter lifetime can effectively reduce the carbon footprint of railway systems (up to ~76 t CO₂-eq saved during the inverter lifetime). This paper provides essential information for distinct stakeholders to support the decision-making process and design considerations for future railway power conversion technologies. Full article

AI data-center (DC) growth is increasingly constrained by limited deliverable electricity, interconnection capacity, and cooling demand. This study develops a boundary-consistent screening framework for waste-to-energy (WtE)-coupled AI DC cooling, treating cooling as an energy service that can be supplied through grade matching rather than solely through electricity-driven mechanical chilling. The framework translates plant-side exportable heat into corridor-level planning objects by explicitly accounting for thermal attenuation, absorption-based conversion, and parasitic electricity associated with delivery and auxiliaries. Three results structure the analysis. First, a reference-case energy-service ledger shows how a representative regulated WtE plant with municipal solid-waste throughput of 1500 t/day and lower heating value of 10 MJ/kg yields ~78.1 MWth of exportable driving heat and, at a 20 km corridor, ~53.0 MWcool of delivered cooling and ~8.0 MWe of net avoided cooling electricity after parasitic debiting. Second, the coupled system is governed by operating regimes, not a single efficiency score. Under the baseline package, full thermal coverage is maintained up to ~20.9 km, the stricter quality-adjusted criterion remains positive to ~22.9 km, and the electricity–relief criterion remains positive to ~44.7 km. Third, deployment-scale translation for a 1 GW IT campus ($u=0.70$, $L=5 \mathrm{k}$m) implies a net grid relief of ~116.9–264.4 MW across scenario packages, while the required WtE footprint ranges from roughly three to 148 equivalent representative plants, or about 0.6–40 full-load-equivalent plants at a 25% displacement target. The contribution is a siting-ready planning framework that identifies when WtE-coupled cooling remains corridor-feasible, when it becomes hybrid and marginal, and when infrastructure scale rather than thermodynamic benefit becomes the binding constraint. It is intended as a screening tool for planning and comparison, not as a project-specific hydraulic or plant-cycle design. Full article

Superconducting-qubit control is fundamentally constrained by decoherence, finite bandwidth, and hardware-limited drive amplitudes, making high-fidelity state preparation sensitive to optimizer initialization under non-convex open-system dynamics. We propose a hybrid reinforcement learning (RL)–quantum optimal control (QOC) pipeline in which a lightweight, tabular, model-free RL agent is trained offline in simulation to generate feasible, bounded seed pulses, which are subsequently refined via GRAPE under Lindblad dynamics. Hard amplitude constraints are enforced consistently across both stages, ensuring strict feasibility throughout optimization. Performance is evaluated using a budget-matched protocol based on fidelity evaluations (F-evals), enabling controlled comparison with random-start multi-start GRAPE. On a transmon-like qubit benchmark with relaxation and dephasing, RL warm-starting reduces the median online refinement effort in the adopted finite-difference GRAPE implementation from 7568 to 3543 F-evals (2.14× reduction) while achieving terminal state fidelity ≥0.995 under identical constraints and evaluation budgets. We provide a theoretical interpretation of the improvement in terms of basin-of-attraction probability shaping in constrained control landscapes and an amortized cost analysis showing that the offline RL cost is recovered after a small number of reuse cycles. The results support the view that learning-based initialization can improve warm-start quality relative to uninformed feasible multi-start in constrained open-system quantum-control benchmarks, while broader practical comparison against stronger physics-guided seeds remains for future work. Full article

To address the core challenges of low energy utilization efficiency and limited range in front-wheel-drive electric vehicles (FWD EVs), this study proposes a dynamic series braking energy recovery strategy featuring adaptive braking force distribution and multi-factor correction. A comprehensive simulation model integrating five core modules—Cycle, Driver, Controller, Vehicle, and Display—was developed using Matlab/Simulink, combining the dynamic series recovery strategy with traditional parallel recovery strategies. Model reliability was validated through chassis dynamometer test data (maximum error ≤ 3.2%), followed by simulation comparisons under CLTC conditions. Results demonstrate that compared to parallel strategies, the dynamic series approach increases range by 25.8% (from 318 km to 400 km). Key innovations include real-time adaptive front axle braking coefficients based on braking intensity and a correction mechanism integrating vehicle speed and state of charge (SOC), achieving a balance between recovery efficiency, braking stability, and battery protection. This study provides actionable design guidance for FWD EV powertrain optimization while establishing a validated regenerative braking simulation framework. Full article