Search Results (4,028)

In training vision-based autonomous system algorithms, simulation plays a pivotal role in enhancing and expediting system recognition and development. However, creating a realistic virtual underwater environment is particularly difficult. To address the need for advanced control system modeling, we developed an integrated simulation framework that combines a 3D computer graphics engine, Unreal Engine, with Simulink. This integration facilitates the design and visualization of complex control systems. The framework was evaluated using an autonomous system tasked with tracking underwater cables, employing YOLO-based object detection for visual guidance. The results demonstrate the effectiveness of the proposed simulation environment in accurately replicating the behavior of vision-based autonomous systems operating in underwater conditions. Full article

Accurate time-domain photovoltaic (PV) models are needed to evaluate performance under outdoor variability beyond STC datasheet conditions. This paper presents a traceable modeling workflow based on the standard single-diode formulation, implemented in MATLAB/Simulink (R2023a) as a modular white-box architecture that explicitly resolves photocurrent generation and loss mechanisms (diode recombination, shunt leakage, and series resistance effects) with temperature-consistent propagation through V_T(T) and saturation-current terms. The method couples optical boundary conditions to the electrical model by embedding plane-of-array (POA) excitation via the incidence angle Θ(t) and roof albedo directly into the photocurrent source term, preserving the causal chain from mounting geometry to electrical response. Calibration is separated from prediction by initializing key parameters using the standard Simulink PV block and then freezing them for time-domain evaluation. The workflow is validated on a 395 W rooftop prototype using 1 min resolved POA irradiance (ISO 9060:2018 Class A radiometric chain) and module temperature (IEC 60751 Class A Pt100), synchronized with electrical measurements. Over a multi-week campaign, the model exhibits high fidelity, with a worst-case relative current error of ~1.1% and a consistently low bias and dispersion, quantified by ME, MAE, RMSE, σ_e, and thresholded MAPE. Full article

The growing integration of renewable energy sources in grid-connected microgrids (MG) has made it increasingly challenging to attain the most cost-effective and emission-efficient power dispatch in the face of uncertainty. This study addresses the scheduling problem of MG under utility-induced demand side load participation level for residential areas. Our research overcomes the constraints of conventional techniques by utilizing quantum-inspired particle swarm optimization (QPSO) to improve the operational efficiency and resilience of MG’s. In this study, a three-stage stochastic framework is proposed to address the optimal energy scheduling of MGs while taking economic and emission aspects into account. Using real-time meteorological data, five Cases were investigated and simulated using MATLAB/Simulink. Without the involvement of load participation, MG’s producing units in first Case, had carbon emissions of 797.110 kg and an operating cost of 267.10 €. Similar to this, the impact of demand side on the MG was evaluated in the remaining Cases. According to the simulation results, the fifth Case, which has optimal DGs scheduling, is the suggested way to improve MGs efficiency and provide a dependable power supply with low operating costs, emission reduction, and convergence features. This study not only demonstrates the practicality of QPSO algorithms but also paves the way for more resilient, efficient, and sustainable energy systems. Full article

The adoption of electric vehicles (EVs) is a promising strategy for reducing emissions and promoting sustainable mobility. The increasing adoption of EVs has created a demand for efficient and sustainable charging infrastructure. The integration of rooftop solar-powered EV charging stations into distribution networks is a promising solution for reducing carbon emissions and improving grid efficiency. This integration also introduces challenges, such as power quality issues, grid instability, and the impact of environmental factors on solar generation. This study proposes a novel system that integrates a smart control algorithm for a central battery management system (CBMS) with advanced bidirectional DC-DC converters for optimised power distribution. Unlike existing systems that focus on individual components, this study combines real-time environmental monitoring with adaptive power management algorithms to handle variations in generation owing to solar irradiance, temperature, and shading, and ensure maximum power harvesting. This study also presents the role of the DC-to-DC converter integrated with a smart charging control and CBMS in smart grid-enabled EV charging station. The proposed system was validated using MATLAB 2025b Simulink simulations. This study demonstrates an improvement in overall grid stability and highlights the potential of DC-DC converter technologies for smart grid applications and decarbonisation efforts. Full article

Single-wheel brake failure in electromechanical brake (EMB) systems breaks the left-right symmetry of wheel forces and yaw moments, creating a critical conflict between emergency braking effectiveness and lateral stability. To address this symmetry-breaking condition, this paper proposes a bimodal, adaptive, coordinated fault-tolerant control strategy that integrates dynamic brake torque redistribution with active front steering (AFS). A novel dynamic interaction model linking deceleration demand with tire adhesion utilization enables real-time assessment and optimization of the balance between longitudinal braking performance and yaw stability. Braking forces are allocated based on adhesion utilization through a layered two-mode strategy—balanced distribution prioritizing lateral stability and compensatory distribution engaging the healthy front wheel when rear axle capacity is exceeded. An integral sliding-mode controller computes the additional yaw moment needed to suppress yaw-rate deviation, with rigorous Lyapunov stability analysis confirming closed-loop stability. AFS is triggered only when yaw-rate deviation exceeds 0.05 rad/s or adhesion utilization reaches 90%, incorporating hysteresis to ensure smooth transitions and minimize unnecessary steering intervention. Comprehensive co-simulations using Carsim and MATLAB/Simulink under diverse failure locations (left-front and right-rear wheels), road adhesion levels ($\mu$ = 0.85 and 0.5), and braking intensities (0.2 g–0.6 g) demonstrate that the proposed strategy reduces lateral displacement by up to 85.3% compared to full-time AFS control while maintaining over 99% deceleration satisfaction. The results establish an effective dual-objective fault-tolerant framework that enhances both robustness and functional safety of EMB systems under symmetry-breaking faults, offering a physically interpretable, computationally efficient solution well-suited for real-time automotive applications. Full article

Vehicle recharge time is a key barrier to widespread adoption of battery electric trucks, where megawatt class charging could be used to achieve refueling times comparable to internal combustion vehicles. This work presents the design and validation of a megawatt-capable rechargeable energy storage system (144 kWh, 40P384S) together with a physics-based modeling framework for safe 1 MW operation. The pack architecture is reconfigurable, enabling nominal 750 V (80P192S) propulsion mode as well as 1125 V and 1500 V charging modes compatible with the Megawatt Charging System (MCS). An equivalent circuit model is developed to relate cell-level parameters to pack-level power, heat generation, and temperature rise, providing guidance on feasible charge profiles and thermal limits. A Simulink-based digital twin of the reconfigurable pack is then used to analyze sensitivity to current sensor mismatch and to verify protection logic for multiple bus voltage configurations. Finally, pack tests up to 1 MW confirm the model-predicted operating envelope and illustrate practical constraints imposed by charger voltage and pack resistance. The combined hardware and modeling approach provides a reusable platform for studying extreme fast charging of medium- and heavy-duty BEV packs-class charging -capable rechargeable energy storage system (144 kWh, 40P384S) together with a physics-based modeling framework for safe 1 MW operation. The pack architecture is reconfigurable, enabling nominal 750 V (80P192S) propulsion mode as well as 1125 V and 1500 V charging modes compatible with the Megawatt Charging System (MCS). An equivalent-circuit model is developed to relate cell-level parameters to pack-level power, heat generation, and temperature rise, providing guidance on feasible charge profiles and thermal limits. A Simulink-based digital twin of the reconfigurable pack is then used to analyze sensitivity to current–sensor mismatch and to verify protection logic for multiple bus-voltage configurations. Finally, pack tests up to 1 MW confirm the model-predicted operating envelope and illustrate practical constraints imposed by charger voltage and pack resistance. The combined hardware and modeling approach provides a reusable platform for studying extreme fast charging of medium- and heavy-duty BEV packs. Full article

Traditional passive hydraulic dampers face the challenges of extended design cycles, inefficient parameter matching, and fixed performance, limiting their adaptability. This paper proposes an integrated solution that combines inverse parametric design with active, continuously adjustable damping. First, a high-fidelity nonlinear model is developed based on valve plate elasticity and multi-valve coupling dynamics, achieving a simulation error of ≤4%. An improved genetic algorithm is then designed to inversely optimize five key parameters. This optimization reduces the deviation between the prototype’s damping force–velocity characteristics and the target curve to ≤3% and shortens the design cycle by approximately 40%. Building on this foundation, a pilot-operated electro-hydraulic proportional relief valve is integrated to enable continuous damping adjustment. Co-simulation using AMESim2404 and MatlabSimulinkR2022 reveals the influence of solenoid valve parameters on damping characteristics and calibrates the current–damping force mapping. A co-simulation of a skyhook-controlled quarter-vehicle model demonstrates that the semi-active suspension system reduces the root mean square (RMS) of vertical body acceleration by 21.7%, indicating a significant theoretical improvement in ride comfort. This study establishes a complete technical pathway of “modeling → inverse optimization → integration → verification,” providing an efficient and viable core component solution for intelligent suspension systems. Full article

This paper investigates the influence of rolling resistance on fuel consumption in Class 8 heavy-duty vehicles, with a focus on a modeling approach through variations in the rolling resistance coefficient ($C_{rr}$) across different driving scenarios. Leveraging TruckSim’s multibody modeling approach for vehicle dynamics and MATLAB/Simulink co-simulation capability, the study provides insights into how tire rolling resistance affects energy efficiency under varying conditions while enabling controlled, repeatable comparisons across various scenarios. Results show that across the evaluated scenarios, increases in $C_{rr}$ impact the vehicle’s speed, fuel consumption, engine torque, and crankshaft spin. Specifically, increasing $C_{rr}$ from 0.004 to 0.013 was found to lead up to 68% higher fuel consumption in high demand scenarios. These findings aim to guide efforts to optimize tire design and vehicle performance that help achieve improved fuel efficiency. Full article

Ensuring the safety of Autonomous Driving Systems (ADS) at urban intersections remains challenging due to complex interactions between vehicles and traffic management infrastructure. This study validates an ADS equipped with connected perception using Infrastructure-to-Vehicle (I2V) communication within a combined virtual and hybrid testing approach. The validation follows the overall structure and methodology of the SUNRISE Safety Assurance Framework (SAF), which is applied in detail where required by the scope of the study. Five representative urban intersection scenarios, covering both nominal driving conditions and safety-critical edge cases, are evaluated using virtual simulations in MATLAB/Simulink (2014b) and hybrid experiments integrating OMNeT++ (5.7.1)/Veins (5.2)/SUMO (1.12.0) with real-world components. Key Performance Indicators (KPIs) related to safety, decision-making, longitudinal control, passenger comfort, and V2X communication performance are analyzed. The results show strong consistency between virtual and hybrid testing, with ego vehicle speed deviations below 2 km/h and trigger distance differences under 3 m. V2X communication achieves a near-perfect Cooperative Awareness Message (CAM) delivery ratio, with an average latency of approximately 142 ms. While this latency remains within the tolerance of the deployed ADS, the overall end-to-end delay highlights opportunities for further optimization. The study demonstrates how the SUNRISE SAF can effectively structure ADS validation, identifies critical scenarios such as right-of-way violations by non-priority obstacles, and provides insights into improving connectivity handling and low-speed braking behavior for Cooperative, Connected, and Automated Mobility (CCAM) systems in urban environments. Full article

This study addresses key challenges in high-precision industrial motion control, including dynamic load disturbances, nonlinear parameter coupling, and degradation in synchronization accuracy. A dual-motor cross-coupled synchronous control strategy is proposed, integrating Hertzian contact theory with an adaptive fuzzy PI control algorithm. First, a precise pressure measurement model for the printing contact zone is established based on Hertzian contact theory. The model quantitatively characterizes the relationship between structural parameters and pressure distribution. Key parameters include cylinder radius and plate thickness. This provides a theoretical foundation for precise regulation. Subsequently, a fuzzy PI controller with parameter self-tuning capability is incorporated into the motor speed loop, enabling real-time adjustment of control parameters to effectively compensate for system nonlinearities and time-varying disturbances. Furthermore, a cross-coupled synchronization architecture is designed to enable bidirectional compensation between the two motors, significantly improving synchronization accuracy under complex operating conditions. Simulations were performed in MATLAB/Simulink. The tests covered typical operational scenarios, including load start-up, single-motor disturbance, and multi-disturbance conditions. The results demonstrate that the proposed system achieves high performance: dual-motor speed synchronization accuracy reaches 99.5%; the response time for disturbance compensation is within 0.3 s; and printing-pressure fluctuation is confined to ±0.8%. This performance represents a 62.5% improvement in stability over conventional single-motor control systems. This research not only resolves the long-standing issue of pressure non-uniformity in flexographic printing but also provides a generalizable framework for multi-motor synchronous control in precision manufacturing. The findings offer substantial academic insight and practical value for advancing intelligent industrial measurement and control technologies. Full article

The recent advent of charging infrastructure on an Electric Vehicles (EVs) poses a severe problem with effect on the power grid in terms of harmonic distortion, mostly caused by the nonlinear loads on the electric power produced by charging stations, diode bridge rectifiers, and switching converters. These harmonics continuously negatively influence power quality by increasing system and grid current, voltage total harmonic distortion (THD), power factor, and voltage regulation, and lowering the overall efficiency of the system at high rates that exceed IEEE 519 harmonic standards. This paper develops a thorough design and critical analysis of four topologies of harmonic passive filter, including single-tuned filter (STF), double-tuned filter (DTF), high-pass filter (HPF), and C-type high-pass filter (CHPF), to alleviate harmonics and enhance power quality on grid-tied charging stations of electric vehicles. A generalized structure is modeled and simulated in MATLAB/Simulink R2021a at a charging load of an EV charging load for all the filters under the same conditions and evaluated based on the current THD (ITHD), voltage THD (VTHD), input power factor (PF), voltage regulation (VR), and efficiency (η). The findings show that STF has an ITHD of 8.3%, VTHD of 4.6%, PF of 0.92, VR of 6.2%, and efficiency of 91.3%; DTF has an ITHD of 6.1%, VTHD of 3.9%, PF of 0.95, VR of 5.4%, and 93.5%; HPF has an ITHD of 5.6%, VTHD of 3.5%, 0.96 PF, 5.0% of VR, and 94.2% efficiency. The effectiveness of the proposed CHPH is superior to all other traditional approaches and has the lowest ITHD and VTHD, 3.7% and 2.1%, respectively, the highest PF of 0.987, a better VR of 3.8%, and a higher efficiency of 96.2%. The proposed CHPF shows the high-performance characteristics as reflected in the harmonic reduction, improved voltage stability, power factor, and efficiency. The suggested CHPF complies with IEEE 519 standards and provides better grid compatibility with modern EV charging applications. Full article

This study proposes a systematic approach for implementing discrete-time Linear Active Disturbance Rejection Control in the closed-loop regulation of power converters. The continuous-time Linear Extended State Observer was discretized using the zero-order hold method to obtain a current estimator-based Linear Extended State Observer that is suitable for real-time implementation. The design considerations for discrete-time Linear Active Disturbance Rejection Control, including the selection of observer and controller parameters and the sampling period, are addressed. For performance comparison, a PI controller was designed and implemented in discrete time. The control schemes were evaluated via MATLAB/Simulink (2025b) simulations and real-time closed-loop experiments on a microcontroller to assess the transient response, disturbance rejection capability, and steady-state accuracy of the buck converter. The simulation and experimental results demonstrate that the discrete-time Linear Active Disturbance Rejection Control incorporating a current-estimator-based Linear Extended State Observer significantly outperforms the PI controller in terms of transient response and disturbance rejection capability. From this perspective, this study provides a meaningful contribution to the limited literature on linear extended state observer-based discrete-time Active Disturbance Rejection Control methods. Full article

The development of high-efficiency energy dissipation devices is crucial for mitigating the significant threat posed by seismic loads to modern buildings. Therefore, the purpose of this work is to design a novel fluid viscous inerter damper (FVID) and systematically investigate its mechanical performance through theoretical derivations, experiments, and finite element simulations. Furthermore, the impact of FVIDs on the seismic performance of structures is comprehensively evaluated. The advantage of FVID is that under external excitation, the fluid can flow through multiple channels, thereby generating inertial and damping forces to dissipate energy. The theoretical model of FVID’s output force is determined based on FVID’s construction and fluid flow characteristics. The hysteresis performance of the FVID is evaluated through cyclic loading tests, and the influence of the cross-sectional radius and number of turns of the helical tube on its output force is analyzed. By performing finite element simulations of the internal flow field of FVID, the distributions of fluid pressure and velocity at different positions within FVID are analyzed. Based on Simulink, the focus is on investigating the control effect of FVID on structural responses under non-pulse near-field ground motions, pulse-type near-field ground motions, and far-field ground motions. The results indicate that the FVID has a strong energy-dissipation capacity and can effectively reduce structural responses under different types of earthquakes. The cross-sectional radius of the helical tube is a key design parameter that determines the damper’s output force. For highly destructive pulse-type near-field ground motions, FVIDs still exhibit excellent comprehensive performance in the structure. Full article

Decarbonizing ocean-going shipping requires decision-grade environmental evidence for propulsion transitions, yet conventional LCA relies on static inventories that inadequately represent dynamic operations and route-dependent renewable generation. This study evaluates well-to-wake (WtW) Global Warming Potential (GWP) for two large container ships operated by a Korean company under four scenarios: conventional diesel main engine, diesel–electric with onboard generator, full battery-electric supplied by shore electricity from the Republic of Korea grid, and battery-electric with a route-resolved solar PV system. A Live-LCA (LLCA) framework couples LCI data with MATLAB/Simulink power and propulsion modeling driven by actual operating profiles and route environmental conditions to generate operational inventories for impact calculation. Diesel–electric operation increases annual WtW GWP by over 26% for both ships versus the baseline of a conventional diesel main engine, whereas shore-electric battery operation is able to reduce WtW GWP by around 40% versus diesel–electric. With limited PV installation, additional reductions are marginal. Depending on electricity profile, it can increase battery-electric GHG emissions by approximately 27%, highlighting sensitivity to electricity evolution. Overall, electric propulsion delivers climate benefits only when paired with low-carbon electricity, and LLCA enables operationally and route-grounded LCA for large container ships. Full article

The growing need to safeguard sensitive data in various fields, including in relation to education, banking over the phone, private voice conferences, and the military, has grown as dependence on technology in daily life has increased. Encryption schemes based on chaotic systems are among the most commonly utilized approaches in the security field due to their high levels of safety and reliability. This study proposes a secure audio encryption framework based on the Chameleon chaotic algorithm implemented on a Xilinx ZedBoard Zynq-7000 FPGA. The system was designed using a fixed-point arithmetic format with 32-bit precision (eight integers; 24 fractional bits) with the Xilinx System Generator in MATLAB Simulink R2021b and verified using Vivado. The Chameleon Chaotic System, characterized by its transition from self-excited to hidden attractors through parameter variation, adds complexity to the system dynamics and strengthens the encryption algorithm. The Adaptive Feedback Control technique was applied to synchronize the signals. These methods enhance the security of audio data by ensuring robust and fast synchronization during transmission. The performance of the proposed system was assessed using correlation analysis, the mean squared error, histogram analysis, and audio spectrogram analysis. The system demonstrated strong encryption capabilities with low correlation values (−0.0033). In decryption, they achieved high fidelity with a correlation exceeding 0.999 in noise-free conditions and above 0.9933 under 20 dB AWGN. Adaptive Feedback Control showed superior decryption precision with lower MSEU and higher PSNR, confirming its effectiveness under noisy environments. Full article