Search Results (167)

The requirement of sustainable mobility and a clean environment has accelerated the development and adoption of electric vehicles (EVs) and hybrid electric vehicles (HEVs) as an alternative, practical and promising solution against conventional vehicles globally. Such alternative energy vehicles not only provide a critical solution to mitigate fossil fuel dependency and reduce greenhouse gas emissions, but also contribute to producing an energy-efficient transportation system. However, the operational performance, efficiency, and cost-effectiveness of EVs and HEVs are hugely dependent on their powertrain architectures, selection of traction motors and associated control techniques. This paper systematically compares major hybrid architectures: series, parallel, and series–parallel, plug-in, as well as battery and fuel cell electric vehicle platforms, highlighting trade-offs in component sizing, cost, and system integration complexity. The paper critically analyses traction motor technologies with respect to torque–speed characteristics, efficiency behavior, material constraints, and power density. A detailed comparative assessment of traction motor technologies is presented. Furthermore, classical and advanced motor control strategies, including field-oriented control (FOC), direct torque control (DTC), model predictive control (MPC) and AI-enhanced control frameworks, are evaluated with respect to transient performance, robustness, computational requirements, and scalability. The review identifies key technological milestones, emerging next-generation drive technologies, existing limitations, and unresolved research challenges. Finally, critical research gaps and future development pathways are articulated to support the advancement of high-efficiency, reliable, and cost-effective EV/HEV powertrain systems. Full article

Autonomous trajectory planning for electric Vertical Takeoff and Landing (eVTOL) Unmanned Aerial Vehicles (UAVs) faces the dual challenges of low-altitude environmental interference and limited onboard energy, which affects the reliability and safety of unmanned missions. To address these challenges, this paper develops the EA-TD3 autonomous trajectory planning framework for eVTOL UAV systems. First, a stochastic urban wind field model is established to simulate low-altitude interference. Then, by integrating eVTOL UAV battery discharge data from Carnegie Mellon University (CMU), a mapping relationship between maneuvers and energy consumption is identified to construct a nonlinear energy consumption model. Finally, an energy boundary penalty function is introduced into the TD3 algorithm to ensure that trajectory planning remains within battery safety margins. Experiments based on the parameters of the EH216-S platform show that EA-TD3 achieves a near 100.00% success rate under ideal conditions and outperforms benchmark algorithms while reducing average energy consumption by 11.6%. Under an energy constraint of 120 J, its success rate remains at 87.80%, which exceeds the performance of the DDPG, SAC, and standard TD3 algorithms. This study optimizes the autonomous trajectory planning of eVTOL UAV platforms in urban air mobility (UAM) to improve the energy perception and power management of the autonomous system. Full article

With the widespread adoption of electric vehicles (EVs), the deep integration of transportation and power grids has emerged as a significant trend. EV charging stations, acting as dynamic loads, present challenges to real-time power balance and economic dispatch in microgrids, while EVs serving as mobile energy storage units offer new opportunities for system flexibility. To address these issues, this paper proposes a hardware-in-the-loop (HIL) co-design method for vehicle–grid-integrated microgrid energy management systems, covering the entire workflow from simulation to embedded deployment. This method resolves the core challenges of multi-objective optimization algorithm deployment on embedded platforms (i.e., high computational complexity, strict real-time constraints, and heterogeneous communication protocol integration) via deployability analysis, hybrid code generation, real-time task restructuring, and consistency validation. A prototype microgrid system integrating photovoltaic panels, wind turbines, diesel generators, an energy storage system, and EV charging loads was built on the RK3588 embedded platform. An improved multi-objective particle swarm optimization (MOPSO) algorithm is employed to optimize operational costs. Experimental results verify the effectiveness of the proposed co-design method. Compared with traditional rule-based control strategies, the MOPSO algorithm reduces the total daily operating cost of the VGIM system by approximately 50%. After integrating vehicle-to-grid (V2G) scheduling, the operating cost is further reduced. In addition, this method ensures the consistency of algorithm functionality and performance during the migration from HIL simulation to embedded deployment, and the RK3588-based embedded system can complete a single optimization iteration within 60 s, which fully satisfies the real-time requirements of industrial applications. This work provides a feasible technical pathway for the reliable deployment of vehicle–grid-integrated microgrids in practical industrial scenarios. Full article

This paper presents a comparative modeling and experimental validation study for a modular four-wheel omnidirectional mobile robot, focusing on two locomotion architectures implemented on the same platform: four omni wheels (90° rollers) and four Mecanum wheels (45° rollers). Both configurations were evaluated under identical benchmark conditions on a 1 m × 1 m square path (4 m total path length), using the same nominal 12 V supply and the same test duration, in order to ensure a fair and reproducible cross-architecture comparison. A MATLAB/Simulink–Simscape dynamic model was developed for both architectures, while experimental validation was performed using Hall-effect current sensors integrated into the drive modules. Based on the measured and simulated motor currents, a 12 V-based electrical input-power estimate was evaluated at both motor and robot level. For the considered benchmark, the four-Mecanum configuration exhibited a lower measured input-power estimate than the four-omni configuration (17.88 W vs. 25.75 W), corresponding to an approximate reduction of 30.6% under the adopted assumptions. At robot level, the deviation between simulated and measured total input-power estimate was 3.70% for the four-omni architecture and 21.42% for the four-Mecanum architecture, indicating higher predictive agreement for the omni-wheel model in its present form. The comparative analysis also suggests that wheel–ground interaction and roller geometry influence not only the measured current demand but also the level of agreement between simulation and experiment. Although the present study is limited to a single standardized benchmark and nominal-voltage conditions, it provides a controlled basis for comparing the two locomotion solutions and for identifying directions for further model refinement. The findings should therefore be interpreted as benchmark-specific comparative results offering practical guidance for locomotion architecture selection and for future refinement of friction-aware omnidirectional robot models. Full article

Omnidirectional mobile robots are increasingly used in industrial and service applications due to their high maneuverability and ability to perform combined translational and rotational motions in confined spaces. However, these locomotion advantages are often accompanied by additional wheel–ground interaction losses, making power consumption an important design criterion in the design of efficient mobile platforms. This study presents a dynamic modeling and experimental-power benchmarking framework for a modular mobile robot equipped with three omnidirectional wheels, using a four-omni-wheel configuration as a baseline reference for comparison. A CAD-consistent multibody dynamic model of the three-wheel architecture is developed in the MATLAB/Simulink–Simscape Multibody R2024benvironment to estimate motor currents and electrical-power demand during motion. Experimental validation is carried out on the physical prototype using Hall-effect current sensors integrated into the drive modules, enabling real-time current acquisition for each motor. Both the simulation and experiments are performed on a standardized 1 m square-path benchmark at a constant 12 V supply. The results show that the proposed three-omni-wheel configuration reaches a total measured power of 14.43 W and a simulated power of 12.72 W, corresponding to a robot-level deviation of 11.85%. By comparison, the four-omni-wheel baseline exhibits a total measured power of 25.75 W and a simulated power of 24.92 W. Therefore, the proposed three-wheel architecture reduces the measured power demand by approximately 43.96% relative to the baseline, while the four-wheel configuration provides higher model fidelity. The proposed methodology supports power-oriented evaluation and informed design selection of omnidirectional locomotion architectures for modular mobile robots intended for industrial applications. Full article

Dynamic Wireless Power Transfer (DWPT) represents a promising solution to advance sustainable electric mobility by reducing vehicle downtime, extending driving range, and mitigating the need for battery oversizing. However, the lack of integrated and flexible experimental testbeds still limits the validation of emerging technologies. This paper presents DEXTER (Development of an Enhanced eXperimental proTotype of wirEless chargeR), a 1:2-scale open platform specifically designed for research on DWPT systems. The setup integrates a three-axis motion control for coil misalignments and trajectory emulation, digitally regulated TX/RX converters, a programmable battery emulator, and electromagnetic shielding coils equipped with field probes. A MATLAB-based interface enables automated testing and Hardware-in-the-Loop (HiL) integration. By combining modularity, scalability, and reproducibility, DEXTER provides a comprehensive framework for experimental optimization of power electronics and electromagnetic design while ensuring compliance with international safety standards. The case studies analyzed here demonstrate the capability of such a platform to validate and optimize the DWPT design choices, checking their impact on the overall performance of these systems. The platform constitutes a reference environment for both academia and industry, supporting the development of next-generation wireless charging systems and contributing to the sustainability and reliability of future electric mobility infrastructures. Full article

The rapid growth of electric vehicles (EVs) is reshaping transport systems and accelerating the sustainable digital transformation of smart mobility. EV battery-swapping, delivered through platform-based, data-driven service networks, offers a low-carbon alternative to conventional refueling and plug-in charging by shortening replenishment time and enabling centralized battery management. However, the behavioral mechanisms driving user adoption of this digitally enabled infrastructure remain insufficiently understood. This study develops a socio-technical system (STS) model in which social and technical drivers influence users’ intention to adopt EV battery-swapping services via the dual mediation of perceived trust and perceived risk. Using a three-stage mixed-methods design that combines a PRISMA-based literature review, expert interviews with user-journey mapping, and a large-scale user survey, the study identifies six social and technical antecedents of EV battery-swapping adoption. Based on 565 valid responses from EV users in the Beijing–Tianjin–Hebei region, partial least squares structural equation modeling and multi-group analysis are employed to test the proposed framework. The results show that all six antecedents significantly affect perceived trust and perceived risk, which in turn mediate their impacts on adoption intention, with notable heterogeneity across income and usage-frequency groups. The findings provide a mechanism-based extension of STS theory for digitally mediated battery-swapping infrastructure by showing how socio-technical conditions shape adoption via trust and risk, and they offer actionable implications for operators and policymakers to build secure, user-centered swapping services within intelligent transport systems. Full article

This paper proposes a SIM-compatible hardware coordination architecture for secure radio-frequency (RF)-triggered activation in mobile devices. The proposed concept functions as a passive coordination layer rather than as an additional wireless transceiver, enabling controlled interaction between external low-frequency RFID or high-frequency NFC fields and wireless subsystems already available in the host device. The architecture assumes a flexible nano-SIM-compatible form factor integrating passive RF detection structures, a trusted decision component, and a trigger-generation interface aligned with standard SIM/UICC electrical and logical interaction models. Upon detection of an external electromagnetic field, the coordination layer evaluates predefined authorization conditions and produces a controlled trigger event intended to propagate through existing telephony and system-service pathways. In contrast to architectures that embed active wireless transmitters, the proposed approach seeks to minimize hardware redundancy and reduce potential attack surfaces by relying on the host device’s native Bluetooth Low Energy (BLE) capabilities. Rather than directly controlling wireless modules, the interface operates as a hardware-originated coordination mechanism that may support low-power and context-aware activation scenarios in mobile and embedded environments. This paper focuses on the architectural model, system assumptions, security rationale, and implementation constraints of such a SIM-compatible interface. Particular attention is given to integration considerations related to smartphone baseband architectures, operating-system mediation, and secure-element isolation. The presented concept establishes a foundation for future prototype implementation and platform-specific validation of SIM-compatible RF-triggered coordination mechanisms. Full article

This paper presents the design of a high-efficiency apple harvesting robot based on a hybrid pneumatic-electric drive system, capable of operating around the clock. The robotic system comprises a mobile platform with two degrees of freedom (DOF) and a five-DOF PRRRP manipulator for fruit picking. To meet the harvesting requirements, a spoon-shaped end-effector with pneumatic control was developed, enabling precise manipulator control and flexible grasping. The robot’s vision system integrates machine vision and deep neural network approaches. Additionally, an industrial computer and AC servo drivers were employed to control the manipulator and end-effector. An integrated nighttime illumination system allowed for all-weather operation. Initial experiments were conducted in a controlled laboratory. Subsequently, comprehensive identification and harvesting tests were performed in both laboratory and field environments to validate system robustness. Experimental results validated the effectiveness of the proposed system, demonstrating an apple harvesting success rate of 81% and an average harvesting time of 7.81 s per apple. The system achieved a fruit damage rate of less than 5% during field experiments, demonstrating its potential for gentle handling. The primary innovation of this work lies in its hybrid drive architecture and adaptive vision strategy, which together offer a cost-effective and robust solution for all-weather automated harvesting, addressing key limitations of high cost and environmental sensitivity in existing robotic harvesters. Full article

The transition to electric mobility requires the coordinated evolution of vehicles, charging infrastructure, power systems, and intelligent digital platforms. This study examines the role of Industry 4.0 technologies in enabling large-scale electric vehicle (EV) adoption and effective EV grid integration and synthesizes the existing evidence into a coherent analytical framework to support planning and policy decision-making. A systematic review of 27 peer-reviewed studies published between 2018 and 2025 was conducted in accordance with PRISMA 2020 guidelines, capturing the acceleration of electromobility following the consolidation of Industry 4.0 technologies and the emergence of large-scale policy commitments worldwide. The analysis covers six technology families, including the Internet of Things, big data and analytics, artificial intelligence and machine learning, blockchain, digital twins, and extended reality, and examines their applications in smart charging, grid vehicle coordination, fleet optimization, and vehicle-to-grid services. The findings show that analytics and artificial intelligence consistently enhance operational reliability and efficiency, while digital twins are increasingly applied to infrastructure siting, grid impact assessment, and scenario analysis. Building on these results, the study proposes a three-layer analytical framework composed of physical, digital, and decision layers, together with a functional EV grid generation integration model that links technology readiness to system-level deployment. In addition, a transition timeline for the 2025–2040 period and a concise set of key performance indicators are introduced to support evaluation and comparison. Policy implications for Ecuador and Latin America emphasize interoperability, data governance, realistic cost assessment, and a phased approach to vehicle-to-grid deployment. Full article

This study addresses the problem of actively stabilizing the longitudinal body inclination of a tracked mobile platform operating over uneven terrain. A novel drive system architecture is proposed that combines conventional track traction electric drives with an inertial body-stabilization drive based on a flywheel mounted on the pitch axis between the chassis and the body module. The main contribution of the proposed approach is the coordinated control of the traction drives and the inertial actuator based on a unified dynamic model of the platform. A quadratic performance criterion is formulated, and a coordinated optimal control law is synthesized to limit body angular oscillations while accounting for actuator energy consumption. Simulation results for motion over step-like and random terrain irregularities, as well as under external moment disturbances, demonstrate a significant reduction in both peak and root-mean-square pitch-angle deviations relative to configurations without an inertial actuator and with local body stabilization. The results obtained confirm the potential and effectiveness of inertial stabilization drives as part of coordinated drive control systems for tracked mobile platforms intended for special-purpose applications, and indicate prospects for their use in advanced terrestrial robotic platforms and future space robotic systems operating in challenging environments. Full article

The sensitive detection of microRNA-21 (miR-21), a key biomarker for various cancers, is crucial for early diagnosis, yet conventional methods often face limitations in sensitivity and operational complexity. Here, we report a label-free biosensor based on a suspended graphene field-effect transistor (GFET) for the direct electrical detection of miR-21. The suspended architecture isolates the graphene channel from substrate-induced interference, resulting in enhanced carrier mobility and reduced electrical noise. After surface functionalization with a specific probe, the GFET demonstrated a clear concentration-dependent response to target miR-21. The binding events were transduced into a monotonic increase in relative resistance (ΔR/R₀) and a positive shift of the Dirac point (V_Dirac), achieving a detection limit in the femtomolar (fM) range. These results establish the suspended GFET as a highly sensitive and robust platform for quantifying nucleic acid biomarkers, holding significant potential for biomedical research and point-of-care diagnostics. Full article

In response to the problems of poor operating stability and easy tipping of small agricultural machinery under the complex terrain of hilly and mountainous areas, this study designed a tracked mobile platform suitable for hilly and mountainous areas and equipped with an omnidirectional leveling function. The omnidirectional leveling system adopted an innovative coordinated leveling scheme with four servo-electric cylinders of “dual lateral and dual longitudinal” structure. Integrated with dual-axis tilt sensors and a PLC control system, the system enabled decoupled leveling in both the lateral and longitudinal directions. Dynamic simulations of the platform’s leveling process under typical working conditions were performed using ADAMS. The simulation results verified the feasibility of the omnidirectional leveling system. Field tests on slopes in hilly and mountainous areas demonstrated that the omnidirectional leveling system achieves rapid leveling on steep slopes within 5–6 s. After leveling, the average fuselage inclination angle was stabilized within 2°, with a standard deviation of less than 3.4°. This study provided a reliable technical solution and design reference for agricultural machinery manufacturers, while offering users a safer and more efficient platform for operations in complex mountainous areas, significantly reducing the risk of overturning. Full article

This work presents an innovative concept of a mobile micro-water turbine for energy recovery from flood-threatened rivers, combining environmental protection with renewable energy production. In response to the increasing frequency and intensity of floods caused by climate change, the authors propose active utilisation of the kinetic energy of water masses during these events through the installation of mobile water turbines along rivers. Rather than merely mitigating the consequences of floods, the energy from flowing water can be converted into electrical current, and the water can be purified and used for other purposes. The article analyses various solutions for water turbines, including the Kaplan turbine, Banki–Michell turbine, and screw turbine, taking into account their efficiency and ability to adapt to changing flow conditions. For the Biała Lądecka river, it was demonstrated that a mobile micro turbine operating for three days can generate a significant amount of energy for on-site consumption or storage. The key challenge is the development of effective water filtration and treatment systems to remove pollutants brought by floods, as well as mobile platforms enabling rapid assembly and disassembly of turbines at threatened sites. The comparative analysis of turbines conducted makes it possible to determine the optimal choice for mobile systems due to operation at low heads, simple construction facilitating installation, and tolerance for contaminants. Full article

Monolayer ${\mathrm{MoS}}_2$ memtransistors offer gate-tunable hysteresis for neuromorphic reservoir computing, yet the role of operating window and fading-memory dynamics in CVD devices remains underexplored. We grow CVD monolayer ${\mathrm{MoS}}_2$, fabricate back-gated memtransistors, and use a single device as a time-multiplexed reservoir node for one-step Lorenz-63 prediction. Mobility, ON/OFF, hysteresis, and drift are quantified to identify stable, tunable bias regimes. We used a transistor with field-effect mobility on the order of $10 {\mathrm{cm}}^2 {\mathrm{V}}^{-1} {\mathrm{s}}^{-1}$, an ON/OFF ratio above ${10}^5$, and a moderate hysteresis window quantified by $H\approx 2.1 \mathsf{\mu }$A·V at $V_{\mathrm{DS}} = 50$ mV and $H\approx 17 \mathsf{\mu }$A·V at $V_{\mathrm{DS}} = 500$ mV over $V_{\mathrm{GS}}\in [-10,30]$ V. Performance is bias/memory-limited rather than FET-metric-limited. Sweeping gate-window and reservoir hyperparameters shows an optimum at intermediate hysteresis with moderate drift. Performance improves when the input clock matches the fading-memory time, achieving normalized root mean square error (NRMSE) = 0.09 for one-step Lorenz-63 x-prediction. Device-level statistics (discussed in the main text) show that, despite substantial scattering in electrical parameters, the resulting device-to-device NRMSE variation remains very small under fixed operating conditions. Classical FET metrics are not limiting here; NRMSE improvement instead requires engineering the hysteresis spectrum and gate stack. The demonstration of Lorenz-63 prediction using CVD-grown monolayer ${\mathrm{MoS}}_2$ memtransistors highlights their potential as a wafer-scalable platform for compact chaotic time-series predictions. Full article