Search Results (2,538)

Electric-vehicle gearboxes operate at high rotational speeds and under low acoustic masking, making tonal excitation and unit-to-unit variability important design concerns. Contemporary loaded tooth contact, multibody, finite-element, and vibroacoustic models can represent the nominal excitation–transfer–response–radiation chain in considerable detail, but their inputs often remain disconnected from the manufactured and assembled gearbox. This review develops a structured framework for identifying which physical parameters, numerical representations, and validation evidence are required before a model can credibly represent a nominal design, a tolerance space, an as-built unit, or a production population. Parameters are classified jointly based on the physical origin and noise, vibration, and harshness (NVH) function and are mapped to contact, system-dynamic, structural, acoustic, and hybrid data-driven models. Four simulation-readiness levels are defined: nominal, tolerance-based, measurement-based, and variability-aware. Explicit transition gates, validation quantities, and permitted claims are assigned to each level. A stage-specific validation matrix distinguishes contact-level, interface-force, structural-response, and acoustic evidence. Literature-grounded quantitative examples demonstrate validated elastic multibody modeling and manufacturing-data-based gear-whine prediction while clarifying the limits of the available evidence. The framework provides a traceable basis for model planning, measurement selection, uncertainty analysis, and readiness-aware reporting of electric-vehicle gearbox NVH simulations. Full article

The increasing demand for sustainable and intelligent energy solutions in electric vehicles (EVs) has led to a significant interest in the development of advanced hybrid energy storage systems (HESS) and efficient wireless charging architectures. In this work, a dynamic sliding mode control (DSMC) technique is optimized through a swarming heuristics framework for a battery-ultracapacitor HESS integrated with a wireless power transfer (WPT) system. Leveraging an LCC-S topology, the WPT system enables high-efficiency, contactless energy transfer to the storage modules under varying load and alignment conditions. To address the nonlinearities and parameter uncertainties inherent in such systems, a robust DSMC approach is formulated to ensure smooth system tracking and disturbance rejection. The control design is further refined using a bio-inspired moth–flame optimization algorithm hybridized with gravitational search and fractional-order PSO (MFOGSAPSO)—enhanced with adaptive entropy regulation and fractal-based memory—to dynamically tune the sliding-surface coefficients and switching gains. The proposed methodology is validated through comprehensive simulations in MATLAB/Simulink and a controller hardware-in-the-loop (C-HIL) setup on TI F28379D LaunchPads. Among the three MFO variants, MFOGSAPSO-A achieves the fastest objective function convergence, stabilizing near 685 within 10 iterations and substantially outperforming the optimized PID (715) and Optimized SMC (708). The proposed DSMC attains an overall RMSE of $0.1081$, reducing the tracking error by $60.69\%$ relative to PID and $14.84\%$ relative to SMC, while shortening the settling time to $0.102$ ms against PID ($84.84\%$) and SMC ($23.88\%$) improvements. The C-HIL results closely match the offline simulation waveforms without retuning, confirming superior energy management, improved power sharing between the battery and ultracapacitor, and enhanced overall efficiency of the wireless charging process under realistic embedded execution. Full article

Traditional location models for Park-and-Ride (P&R) facilities are constrained by the Independent and Identically Distributed (IID) assumption, failing to simultaneously capture inter-modal substitution elasticity and spatial path overlap, which leads to systematic demand forecasting biases. To address this gap, this study proposes an integrated Cross-Nested Logit (CNL) and Paired Combinatorial Logit (PCL) behavioral kernel within a bi-level programming framework, where the upper level minimizes total system generalized cost and the lower level simulates multi-modal Stochastic User Equilibrium (SUE). A hybrid GA-MSA solution strategy is developed. Experiments on the classic Sioux Falls benchmark network demonstrate that the proposed model identifies the optimal construction scale (N = 3) and the critical parking fee threshold (65 CNY) for mode shift. Compared to the un-nested MNL-PCL formulation, the integrated CNL-PCL framework provides an 11.75% downward behavioral correction in P&R market-share estimation, effectively counteracting the overestimation tendency inherent in conventional architectures. The optimal spatial layout (Nodes 4, 6, and 19) achieves a 54.40% share for “P&R + Public Transport” green modes and yields an annual net CO₂ mitigation of 957 tons. These findings confirm that synergistically characterizing mode correlation and path overlap provides a more prudent foundation for sustainable P&R planning. The proposed framework is also generalizable to other multi-modal facility location problems, such as transit-oriented hub sizing or electric vehicle charging network planning. Full article

The rapid expansion of Unmanned Aerial Vehicle (UAV) applications in both civilian and military sectors has intensified the demand for propulsion systems capable of delivering higher speed, increased endurance, and improved payload capacity. While battery-electric propulsion remains dominant for small UAV platforms, its limited energy density restricts operational range and mission flexibility. As a result, micro gas engines have emerged as a viable alternative for applications requiring high power-to-weight ratios and sustained high-speed operation. This review examines the fundamentals, scaling effects, and classification of micro gas turbine propulsion systems used in UAV applications, with emphasis on micro turbojets and related hybrid configurations. The paper discusses the thermodynamic principles governing micro gas engines and analyzes the aerodynamic, thermal, and combustion challenges associated with miniaturization, including low Reynolds number effects, tip leakage losses, thermal management limitations, and combustion instability. Furthermore, the study reviews the operational characteristics and mission suitability of different propulsion architectures for reconnaissance UAVs, high-speed UAVs, including reconnaissance and loitering platforms, target drones, and hybrid-electric aerial platforms. Recent developments involving additive manufacturing, advanced control systems, recuperated cycles, and hybrid-electric integration are also evaluated as enabling technologies for next-generation UAV propulsion. The findings demonstrate that although micro gas turbines continue to face important efficiency and manufacturing challenges at reduced scales, they remain essential for mission profiles that exceed the capabilities of purely electric propulsion systems. Full article

The growing adoption of hybrid and plug-in electric vehicles increases heat generation in power electronic modules, driving demand for effective thermal management materials and reliable Al/Cu joining methods. However, large-area Al/Cu joints are challenging as conventional brazing requires high temperatures and flux, and fusion welding performs poorly with dissimilar metals. Contact-Reaction Brazing (CRB), which relies on eutectic-phase formation during heating, presents a promising alternative. Direct CRB of AA6063 and Cu might lead to severe aluminum dissolution above 570 °C. To mitigate this, large-area CRB of AA6063/Cu using a 4104 aluminum-foil interlayer is examined. Brazing temperature, holding time, and pressure are systematically varied to evaluate their influence on joint formation. Interfacial microstructures are characterized by SEM and XRD. Shear testing is used to assess fracture behavior and mechanical performance. A satisfactory shear strength of 48.8 MPa is achieved for the AA6063/AA4104/Cu joint under a brazing temperature of 540 °C, a holding time of 10 min, and an applied pressure of 600 Pa. Full article

Public transport corporations in Tamil Nadu face increasing operational and financial pressure due to rising diesel fuel prices, maintenance costs, and operational inefficiencies associated with the conventional bus systems. In many districts, diesel-powered public transport services also suffer from irregular vehicle dispatch and poor timetable adherence, leading to unreliable passenger service. Meanwhile, the rapid penetration of electric two-wheelers and four-wheelers indicates a broader transition towards electrified mobility. Extending electrification to public transport requires prudently designed operational planning, as electric buses operate under battery capacity constraints and charging coordination constraints. In such systems, strict adherence to the scheduling of trips and efficient energy management becomes critical for maintaining service reliability. To address these challenges, this study proposes a Tri-Level Hybrid Electric Bus Scheduling (TLH-EBS) framework integrating Particle Swarm Optimization for global search, Rule-Based Scoring Large Neighborhood Search for adaptive schedule improvement, and Mixed Integer Linear Programming for exact repair optimization. The framework simultaneously optimizes fleet size, depot charging infrastructure allocation, and daily bus assignment under timetable constraints. The proposed model has been applied in three interconnected corridors in Madurai District, which are Thirumangalam, Arapalayam, and Mattuthavani, covering 810 scheduled daily timetabled trips between 05:00 AM and 12:30 AM. Computational results show that the hybrid framework has achieved a 2.8% reduction in annual scheduling cost compared to the best conventional optimization method. Furthermore, compared to equivalent diesel-based operations, the optimized electric system has demonstrated approximately 32.3% annual cost savings, confirming the economic viability of integrated fleet–charger scheduling for district-level electric bus deployment. Full article

Accurate state-of-charge (SOC) estimation is essential for preventing battery degradation, improving energy management, and providing reliable driving-range predictions in electric vehicles (EVs). The extended Kalman filter (EKF) is a widely adopted model-based estimation technique and remains an industry-standard approach in EV battery management systems (BMS). However, its performance can be degraded by model nonlinearities, parameter uncertainties, measurement noise, and interference bursts commonly encountered in real-world operating environments. To overcome these limitations, this paper proposes an event-triggered adaptive SOC estimation framework that integrates a recursive least squares (RLS) filter with the EKF. In the proposed approach, the RLS filter recursively updates its weighting coefficients in real time to compensate for model uncertainties and measurement disturbances, thereby generating an alternative residual signal for SOC estimation. An event-triggered mechanism dynamically selects the most reliable innovation sequence for updating the EKF state estimate, enhancing estimation robustness under adverse operating conditions. A second-order RC equivalent circuit model (ECM) is employed as the nominal battery model, and a Hybrid Pulse Power Characterization (HPPC)-based current profile is used to evaluate performance over the entire SOC operating range. Extensive simulations are conducted to assess the effectiveness of the proposed event-triggered RLS-EKF algorithm under various noise levels and interference-burst scenarios. The estimation accuracy is compared with that of the conventional EKF, cubature Kalman filter (CKF), and square root cubature Kalman filter (SR-CKF) using root mean square error (RMSE) and mean absolute error (MAE) as performance metrics. Simulation results demonstrate that, under regular noise conditions and short-term interference bursts, the proposed event-triggered RLS-EKF achieves estimation performance comparable to that of the SR-CKF while consistently outperforming the EKF and CKF in both RMSE and MAE. Under long-term interference-burst conditions, the proposed method further surpasses the SR-CKF, achieving approximately 10% improvement in overall estimation accuracy as measured by RMSE and MAE. These results confirm the effectiveness and robustness of the proposed framework, highlighting its potential for practical implementation in advanced EV battery management systems. Full article

As public electric vehicle (EV) charging networks expand, infrastructure performance increasingly depends on whether occupied charging time is converted into useful energy delivery. This study develops a hybrid data-driven framework to identify, predict, and explain inefficient EV charging sessions for intelligent infrastructure management. Using charging-session data from California, inefficient sessions are defined by the joint condition of a high idle ratio and low energy delivered per occupied hour. K-means clustering and HDBSCAN are applied to examine charging-session typologies, while four machine learning methods are compared for session-level prediction. Model interpretation is conducted using permutation feature importance, partial dependence plots, and rule extraction. The results show that inefficient charging is concentrated in a long-stay, low-output profile distinct from productive long-duration charging. XGBoost provides the strongest overall predictive performance, and pricing conditions, user routines, temporal patterns, and station-utilisation context emerge as key drivers of inefficient charging risk. Full article

Synchronous Reluctance Machines (SynRMs) have emerged as a strong candidate for electric vehicle (EV) traction owing to their rare-earth-free construction, robust rotor structure, and competitive efficiency relative to permanent magnet (PM) and induction machines (IMs). Their performance, however, is governed by complex electromagnetic and thermal phenomena—saliency, magnetic saturation, flux-barrier geometry, and temperature-dependent losses—that demand accurate yet computationally tractable modelling. This paper reviews the modelling and design landscape for SynRMs in EV traction, covering analytical approaches (d–q models, magnetic equivalent circuits), numerical methods (finite element analysis), and recent hybrid techniques such as the Enhanced Hybrid Subdomain Method (EHSDM). Rotor geometry optimization, including flux-barrier shaping and saliency-ratio enhancement, is examined alongside coupled magnetic–thermal analysis, an aspect typically treated in isolation in earlier surveys. The review compares the trade-offs of competing techniques across the design workflow—from initial sizing to final verification—and identifies open challenges in reducing computational cost while preserving accuracy. The synthesis is intended to guide motor designers toward modelling choices appropriate to each design stage and to highlight directions for future research in high-performance, rare-earth-free traction motors. Full article

Accurate forecasting of power production and consumption is essential for the efficient operation of smart energy grids, enabling stable energy exchange and grid reliability. However, the growing integration of photovoltaics (PVs) and electric vehicles introduces significant uncertainty. This paper evaluates multiple Machine Learning (ML) models for single- and multi-step forecasts of PV generation and household consumption, incorporating uncertainty bounds to inform operator decisions. We use data from two Swedish sites and the CityLearn benchmark dataset to compare direct, recursive, and hybrid multi-step strategies. LightGBM with gradient-boosted quantile regression achieves the best single-step performance, with Mean Absolute Error (MAE) as low as 10.19 W in Halmstad and 16.12 W in Uppsala. For multi-step forecasts, the direct method outperforms others, reaching a 48 h consumption MAE of 71.08 W in Uppsala and 52.05 W in Halmstad, and achieving prediction interval coverage probabilities above 0.95. Moreover, personalized models trained on individual households outperform generalized ones, even with smaller datasets, highlighting the value of tailored approaches for improving forecast accuracy under uncertainty. Full article

In-wheel motor (IWM) systems enable compact architectures and advanced control strategies, making them increasingly relevant in hybrid and electric vehicle applications. This work presents the design and the integration of a front-axle IWM system for a Formula SAE combustion vehicle, within a parallel hybrid configuration. The study includes vehicle dynamics analysis, battery pack sizing under strict regulatory constraints, and an initial evaluation of motor and transmission requirements. A MATLAB R2023a-based algorithm was developed to design and optimize a compact two-stage planetary gearbox. This structured and scalable approach supports future development phases and offers a valuable methodology for early-stage hybrid powertrain design. Full article

The commercialization of electric and hybrid vehicles in Colombia is a strategic component of the national energy-transition agenda and of the country’s climate-governance commitments. However, despite relevant regulatory progress, market deployment remains territorially uneven and institutionally fragmented. The article describes how regulation affects the market for electric and hybrid cars, considering regulatory consistency, inter-institutional cooperation, market obstacles, and the rollout of the energy transition. Based on doctrinal legal analysis, comparative public-policy evaluation, and contextual examination of official vehicle-registration information, the study employs a qualitative and comparative research design. The analysis reviews Colombian legal instruments, policy strategies, and governance arrangements related to electric mobility and compares them with selected experiences in Chile and Mexico. The findings show Colombia has developed tariff reductions, tax benefits, circulation privileges, charging-infrastructure obligations, interoperability rules, and strategic planning instruments. Commercialization faces structural barriers: fragmented regulation, uneven territorial implementation, insufficient charging infrastructure, weak public–private coordination, limited consumer awareness, and a lack of long-term governance for battery replacement and industrial adaptation. The results also show that hybrid electric vehicles dominate national registrations, while battery electric and plug-in hybrid vehicles remain comparatively limited. This distinction shows that Colombia’s current transition is still more strongly associated with hybridization than with full electrification. The article concludes that Colombia requires a more coherent governance architecture capable of integrating regulatory stability, territorial coordination, infrastructure deployment, market facilitation, and long-term energy-transition planning. Full article

Battery management systems (BMSs) in electric vehicles (EVs) are instrumented with an increasing number of heterogeneous sensors, many of which contribute redundant or noisy measurements that increase computational cost without improving diagnostic accuracy. This paper proposes a Binary Hybrid Particle Whale Optimization Algorithm (BHPWOA) for multi-objective feature selection targeting three-class BMS fault diagnosis: OK, Warning, and Critical. The method is evaluated using an 18-feature EV charging dataset with $n=500$ samples. BHPWOA encodes candidate feature subsets as binary masks in a continuous $\left[\mathrm{0,1}\right]$ position space. It executes a Binary Particle Swarm Optimization (BPSO) phase during the first 50 iterations to rapidly identify a promising subset region, then transfers the global-best mask as the Whale Optimization Algorithm (WOA) leader for the remaining 50 iterations of bubble-net exploitation. A multi-objective fitness function simultaneously penalises classifier error and subset size, directly optimising the accuracy–cost trade-off. BHPWOA selects four features out of 18, corresponding to a $77.8\mathrm{\%}$ reduction, and achieves accuracy $=0.710$ and macro-F1 $=0.4455$ on the held-out test set. It outperforms all-feature KNN $\left(F_1=0.2997\right)$, standalone BPSO with six selected features $\left(F_1=0.4603\right)$, BWOA with two selected features $\left(F_1=0.4026\right)$, and BSFSA with five selected features $\left(F_1=0.4216\right)$ on the Pareto-dominant combined fitness objective. The selected subset $\left\{{\mathrm{C}\mathrm{e}\mathrm{l}\mathrm{l}\mathrm{V}\mathrm{o}\mathrm{l}\mathrm{t}\mathrm{a}\mathrm{g}\mathrm{e}}_V,{\mathrm{C}\mathrm{h}\mathrm{a}\mathrm{r}\mathrm{g}\mathrm{e}\mathrm{C}\mathrm{u}\mathrm{r}\mathrm{r}\mathrm{e}\mathrm{n}\mathrm{t}}_A,{\mathrm{S}\mathrm{O}\mathrm{C}}_{\mathrm{\%}},{\mathrm{C}\mathrm{h}\mathrm{a}\mathrm{r}\mathrm{g}\mathrm{e}\mathrm{P}\mathrm{o}\mathrm{w}\mathrm{e}\mathrm{r}}_{kW}\right\}$ achieves the best fitness score of $-0.5555$, enabling a $77.8\mathrm{\%}$ sensor-cost reduction while improving fault detection. Stability analysis across five independent random seeds confirms a mean feature count of $4.0\pm 0.7$ and a mean macro-F1 of $0.441\pm 0.021$, demonstrating algorithmic robustness. Full article

Three-port DC-to-DC converters based on the SEPIC-boost circuit have gained remarkable attraction in standalone applications such as DC micro grids having PV panels as roof-tops in electric boats, electric and hybrid vehicles, LED driving circuits, telecommunication systems, and medical and industrial electronics devices. Such a combination of SEPIC-boost in a single converter eliminates the use of three separate DC-to-DC converters to charge the batteries and to supply power from the PV module or batteries to the load. All such modes of operation in a single package make the converter compact by reducing the number of solid-state devices and passive components. It also enables the reduction in conversion losses and hence improves the system’s overall conversion efficiency. The control of a single circuit becomes simple and effective in terms of power management by detecting the solar irradiation and state of charge (SOC) of the battery. It enables the continuous flow of power to the load from PV modules or batteries, which is determined by the SOC of the battery and the available level of solar irradiation. This article develops the dynamic or state-space modeling of the combined operation of the SEPIC-boost-based DC-to-DC converter, which has not yet been developed in the literature. The development of systems based on separate dynamic SEPIC or boost modeling cannot meet the requirements of all operating modes. A state-space model of the combined operation of the SEPIC-boost converter enables evaluating the performance of such an energy management system during its various operating modes effectively. The validity of the developed model is recognized with results gained from MATLAB/Simulink and electronics-based Multisim computer software. Full article

The increasing penetration of distributed generation (DG) units and electric vehicles (EVs) has created significant challenges in minimizing real power loss in modern distribution systems. In this paper, a hybrid Genetic Algorithm–Monte Carlo Simulation (GA–MCS) optimization framework is implemented for the optimal sizing and placement of DG units in a 38-bus radial distribution system under stochastic operating conditions. The analysis is carried out for different voltage-dependent load models considering single-, double-, and triple-DG configurations with different EV categories. The proposed framework is implemented using 50 Monte Carlo scenarios and 50 GA iterations under varying operating conditions. The obtained results indicate that DG2 provides the best performance among single-DG cases in terms of real power loss minimization. In coordinated multi-DG cases, the DG1–DG2 combination reduces real power loss by approximately 6.50–10.07% compared with the best-performing single-DG configuration. The DG1–DG2–DG4 configuration under extended-range electric vehicle (EREV) penetration achieves the minimum real power loss with an additional reduction of approximately 11.89–11.96% with respect to the best-performing single-DG configuration. Voltage profile analysis further confirms the improvement in voltage magnitude after coordinated DG integration. Comparative analysis also indicates the improved convergence characteristics of the proposed GA–MCS framework compared with conventional GA and PSO approaches. Full article