Search Results (1,501)

Concurrent cognitive tasks, such as avoiding visual, auditory, chemical, and electrical hazards, and concurrent motor tasks, such as load carriage, are prevalent in ergonomic settings. Trips are extremely common in the workplace, leading to fatal and non-fatal fall-related injuries. Intrinsic factors, such as attention, fatigue, and anticipation, as well as extrinsic factors, including tasks at hand, affect trip recovery responses. Objective: The purpose of this study was to investigate the ankle joint kinematics in unexpected and expected trip responses during single-tasking (ST), dual-tasking (DT), and triple-tasking (TT), before and after a physically fatiguing protocol among young, healthy adults. Methods: Twenty volunteers’ (10 females, one left leg dominant, age 20.35 ± 1.04 years, height 174.83 ± 9.03 cm, mass 73.88 ± 15.55 kg) ankle joint kinematics were assessed using 3D motion capture system during unperturbed gait (NG), unexpected trip (UT), and expected trip (ET), during single-tasking (ST), cognitive dual-tasking (CDT), motor dual-tasking (MDT), and triple-tasking (TT), under both PRE and POST fatigue conditions. Results: Greater dorsiflexion angles were observed during UT compared to NG, MDT compared to ST, and TT compared to ST. Significantly greater plantar flexion angles were observed during ET compared to NG and during POST compared to PRE. Conclusions: Greater dorsiflexion angles during dual- and triple-tasking suggest that divided attention affects trip recovery. Greater plantar flexion angles following fatigue are likely an anticipatory mechanism due to altered muscle activity and increased postural control demands. Full article

The study of vibration characteristics and suppression methods in integrated electric drive systems of electric vehicles is of critical importance. To investigate these characteristics, both current harmonics within the motor and nonlinear factors within the drivetrain were considered. A 17-degree-of-freedom nonlinear torsional–planar dynamic model was developed, with electromagnetic torque and output speed as coupling terms. The model’s accuracy was experimentally validated, and the system’s dynamic responses were analyzed under different working conditions. To mitigate vibrations caused by torque ripple, a coordinated control strategy was proposed, combining a quasi-proportional multi-resonant (QPMR) controller and a full-frequency harmonic controller (FFHC). The results demonstrate that the proposed strategy effectively suppresses multi-order current harmonics in the driving motor, reduces torque ripple by 45.1%, and enhances transmission stability. In addition, the proposed electromechanical coupling model provides valuable guidance for the analysis of integrated electric drive systems. Full article

To enhance the reliability of electric propulsion in electric aircraft and address power interruptions caused by current sensor failures, this study proposes a current sensorless fault-tolerant control strategy for permanent magnet synchronous motors (PMSMs) based on a long short-term memory (LSTM) network. First, a hierarchical architecture is constructed to fuse multi-phase electrical signals in the fault diagnosis layer (sliding mode observer). A symbolic function for the reaching law observer is designed based on Lyapunov theory, in order to generate current predictions for fault diagnosis. Second, when a fault occurs, the system switches to the LSTM reconstruction layer. Finally, gating units are used to model nonlinear dynamics to achieve direct mapping of speed/position to phase current. Verification using a physical prototype shows that the proposed method can complete mode switching within 10 ms after a sensor failure, which is 80% faster than EKF, and its speed error is less than 2.5%, fully meeting the high speed error requirements of electric aircraft propulsion systems (i.e., ≤3%). The current reconstruction RMSE is reduced by more than 50% compared with that of the EKF, which ensures continuous and reliable control while maintaining the stable operation of the motor and realizing rapid switching. The intelligent algorithm and sliding mode control fusion strategy meet the requirements of high real-time performance and provide a highly reliable fault-tolerant scheme for electric aircraft propulsion. Full article

This paper presents an advanced energy management system (EMS) for optimizing power distribution in a battery/ultracapacitor (UC) hybrid energy storage system (HESS) for electric vehicles (EVs). The proposed EMS accounts for all energy flow scenarios within a practical driving cycle. A regenerative braking control strategy is developed to maximize kinetic energy recovery using an induction motor, efficiently distributing the recovered energy between the UC and battery. Additionally, a power flow management approach is introduced for both motoring (discharge) and braking (charge) operations via bidirectional buck–boost DC-DC converters. In discharge mode, an optimal distribution factor is dynamically adjusted to balance power delivery between the battery and UC, maximizing efficiency. During charging, a DC link voltage control mechanism prioritizes UC charging over the battery, reducing stress and enhancing energy recovery efficiency. The proposed EMS is validated through simulations and experiments, demonstrating significant improvements in vehicle acceleration, energy efficiency, and battery lifespan. Full article

Unmanned Aerial Vehicles have started to be used more and more due to the benefits they bring. Failure of Unmanned Aerial Vehicle components may result in loss of control, which may cause property damage or personal injury. In order to increase the operational safety of the Unmanned Aerial Vehicle, the implementation of a Predictive Maintenance system using the Internet of Things is required. In this paper, the authors propose a new architecture of Predictive Maintenance system for Unmanned Aerial Vehicles that is able to identify the fault type of Brushless DC electric motor and determine the Remaining Useful Life of the Li-ion batteries. In order to create the Predictive Maintenance system within the Unmanned Aerial Vehicle, an architecture based on Fog Computing was proposed and Machine Learning was used to extract knowledge from the data. The proposed architecture was practically validated. Full article

This article presents a comparative study of the storage of energy produced by photovoltaic panels by means of two types of batteries: Lead–Acid and Lithium-Ion batteries. The work involved the construction of a model in MATLAB-Simulink for controlling the loading/unloading of storage batteries with energy produced by photovoltaic panels through a buck-type DC-DC convertor, controlled by means of the MPPT algorithm implemented through the method of incremental conductance based on a MATLAB function. The program for the MATLAB function was developed by the author in the C++ programming environment. The MPPT algorithm provides maximum energy transfer from the photovoltaic panels to the battery. The electric power taken over at a certain moment by Lithium-Ion batteries in photovoltaic panels is higher than the electric power taken over by Lead–Acid batteries. Two types of batteries were successively used in this model: Lead–Acid and Lithium-Ion batteries. Based on the results being obtained and presented in this work it may be affirmed that the storage battery Lithium-Ion is more performant than the Lead-Acid storage battery. At the Laboratory of Electrical Machinery and Drives of the Engineering Faculty of Bacau, an experimental stand was built for a storing system for electric energy produced by photovoltaic panels. For controlling DC-DC buck-type convertors, a program was developed in the programming environment Arduino IDE for implementing the MPPT algorithm for incremental conductance. The simulation part of this program is similar to that of the program developed in C++. Through conducting experiments, it was observed that, during battery charging, along with an increase in the charging voltage, an increase in the filling factor of the PWM signal controlling the buck DC-DC convertor also occurred. The findings of this study may be applicable to the storage of battery-generated electrical energy used for supplying electrical motors in electric cars. Full article

The growing popularity of Personal Light Electric Vehicles (PLEVs), such as e-scooters, has revolutionized urban mobility by offering compact, cost-effective, and environmentally friendly transportation solutions. However, safety concerns, including inadequate infrastructure, poor protective measures, and high accident rates, remain critical challenges. This paper presents the design and development of an innovative self-balancing microvehicle under the H2020 LEONARDO project, which aims to address these challenges through advanced engineering and user-centric design. The vehicle combines features of monowheels and e-scooters, integrating cutting-edge technologies to enhance safety, stability, and usability. The design adheres to European regulations, including Germany’s eKFV standards, and incorporates user preferences identified through representative online surveys of 1500 PLEV users. These preferences include improved handling on uneven surfaces, enhanced signaling capabilities, and reduced instability during maneuvers. The prototype features a lightweight composite structure reinforced with carbon fibers, a high-torque motorized front wheel, and multiple speed modes tailored to different conditions, such as travel in pedestrian areas, use by novice riders, and advanced users. Braking tests demonstrate deceleration values of up to 3.5 m/s², comparable to PLEV market standards and exceeding regulatory minimums, while smooth acceleration ramps ensure rider stability and safety. Additional features, such as identification plates and weight-dependent motor control, enhance compliance with local traffic rules and prevent misuse. The vehicle’s design also addresses common safety concerns, such as curb navigation and signaling, by incorporating large-diameter wheels, increased ground clearance, and electrically operated direction indicators. Future upgrades include the addition of a second rear wheel for enhanced stability, skateboard-like rear axle modifications for improved maneuverability, and hybrid supercapacitors to minimize fire risks and extend battery life. With its focus on safety, regulatory compliance, and rider-friendly innovations, this microvehicle represents a significant advancement in promoting safe and sustainable urban mobility. Full article

Background and Objective: Temperature management is key for reliable operation of permanent magnet synchronous motors (PMSMs). The lumped-parameter thermal network (LPTN) is fast and interpretable but struggles with nonlinear behavior under high power density. We propose OLTEM, a physics-informed deep model that combines LPTN with a thermal neural network (TNN) to improve prediction accuracy while keeping physical meaning. Methods: OLTEM embeds LPTN into a recurrent state-space formulation and learns three parameter sets: thermal conductance, inverse thermal capacitance, and power loss. Two additions are introduced: (i) a state-conditioned squeeze-and-excitation (SC-SE) attention that adapts feature weights using the current temperature state, and (ii) an enhanced power-loss sub-network that uses a deep MLP with SC-SE and non-negativity constraints. The model is trained and evaluated on the public Electric Motor Temperature dataset (Paderborn University/Kaggle). Performance is measured by mean squared error (MSE) and maximum absolute error across permanent-magnet, stator-yoke, stator-tooth, and stator-winding temperatures. Results: OLTEM tracks fast thermal transients and yields lower MSE than both the baseline TNN and a CNN–RNN model for all four components. On a held-out generalization set, MSE remains below 4.0 °C² and the maximum absolute error is about 4.3–8.2 °C. Ablation shows that removing either SC-SE or the enhanced power-loss module degrades accuracy, confirming their complementary roles. Conclusions: By combining physics with learned attention and loss modeling, OLTEM improves PMSM temperature prediction while preserving interpretability. This approach can support motor thermal design and control; future work will study transfer to other machines and further reduce short-term errors during abrupt operating changes. Full article

To improve the stability and safety of electric vehicles during medium-to-high-speed cornering, this paper investigates torque differential control for dual rear-wheel hub motor drive systems, extending beyond traditional speed control based on the Ackermann steering model. A nonlinear three-degree-of-freedom vehicle dynamics model incorporating the Dugoff tire model was established. By introducing the maximum correntropy criterion, an unscented Kalman filter was developed to estimate longitudinal velocity, sideslip angle at the center of mass, and yaw rate. Building upon the speed differential control achieved through Ackermann steering model-based rear-wheel speed calculation, improvements were made to the conventional exponential reaching law, while a novel switching function was proposed to formulate a new sliding mode controller for computing an additional yaw moment to realize torque differential control. Finally, simulations conducted on the Carsim/Simulink platform demonstrated that the maximum correntropy criterion unscented Kalman filter effectively improves estimation accuracy, achieving at least a 22.00% reduction in RMSE metrics compared to conventional unscented Kalman filter. With torque control exhibiting higher vehicle stability than speed control, the RMSE values of yaw rate and sideslip angle at the center of mass are reduced by at least 20.00% and 4.55%, respectively, enabling stable operation during medium-to-high-speed cornering conditions. Full article

This paper presents a structured and experimentally validated approach to the parameter identification, modeling, and real-time speed control of a brushless DC (BLDC) motor. Electrical parameters, including resistance and inductance, were measured through DC and AC testing under controlled conditions, respectively, while mechanical and electromagnetic parameters such as the back electromotive force (EMF) constant and rotor inertia were determined experimentally using an AVL dynamometer. The back EMF was obtained by operating the motor as a generator under varying speeds, and inertia was identified using a deceleration method based on the relationship between angular acceleration and torque. The identified parameters were used to construct a transfer function model of the motor, which was implemented in MATLAB/Simulink R2024b and validated against real-time experimental data using sinusoidal and exponential input signals. The comparison between simulated and measured speed responses showed strong agreement, confirming the accuracy of the model. A proportional–integral (PI) controller was developed and implemented for speed regulation, using a low-cost National Instruments (NI) USB-6009 data acquisition (DAQ) and a Kelly controller. A first-order low-pass filter was integrated into the control loop to suppress high-frequency disturbances and improve transient performance. Experimental tests using a stepwise reference speed profile demonstrated accurate tracking, minimal overshoot, and robust operation. Although the modeling and control techniques applied are well known, the novelty of this work lies in its integration of experimental parameter identification, real-time validation, and practical hardware implementation within a unified and replicable framework. This approach provides a solid foundation for further studies involving more advanced or adaptive control strategies for BLDC motors. Full article

The design and optimization of drive distribution strategies are critical for enhancing the performance of Formula Student electric racing cars, which face demanding operational conditions such as rapid acceleration, tight cornering, and variable track surfaces. Given the increasing complexity of racing environments and the need for adaptive control solutions, a multi-mode adaptive drive distribution strategy for four-wheel-drive Formula Student electric racing cars is proposed in this study to meet specialized operational demands. Based on the dynamic characteristics of standardized test scenarios (e.g., straight-line acceleration and figure-eight loop), two control modes are designed: slip-ratio-based anti-slip control for longitudinal dynamics and direct yaw moment control for lateral stability. A CarSim–Simulink co-simulation platform is established, with test scenarios conforming to competition standards, including variable road adhesion coefficients (μ is 0.3–0.9) and composite curves. Simulation results indicate that, compared to conventional PID control, the proposed strategy reduces the peak slip ratio to the optimal range of 18% during acceleration and enhances lateral stability in the figure-eight loop, maintaining the sideslip angle around −0.3°. These findings demonstrate the potential for significant improvements in both performance and safety, offering a scalable framework for future developments in racing vehicle control systems. Full article

This manuscript investigates the feasibility of Four-In-Wheel Electronic Differential Systems (4 IW-EDSs) within contemporary electric vehicles (EVs), emphasizing their benefits for stability regulation predicated on steering angles. Through an extensive literature review, we conduct a comparative analysis of various in-wheel-motor models in terms of power output, efficiency, and torque characteristics. Furthermore, we explore the distinctions between IW-EDSs and steer-by-wire systems, as well as conventional systems, while evaluating recent research findings to determine their implications for the evolution of electric mobility. Moreover, this paper addresses the necessity for fault-tolerant methodologies to boost reliability in practical applications. The findings yield valuable insights into the challenges and impacts associated with the implementation of differential steering control in four-wheel independent-drive electric vehicles. This study aims to explore the interaction between these systems, optimize torque distribution, and discover the most ideal control strategy that will improve maneuverability, stability, and energy efficiency, thereby opening up new frontiers in the development of next-generation electric vehicles with unparalleled performance and safety features. Full article

Blind and visually impaired (BVI) people face significant challenges in perception, navigation, and safety during travel. Existing infrastructure (e.g., blind lanes) and traditional aids (e.g., walking sticks, basic audio feedback) provide limited flexibility and interactivity for complex environments. To solve this problem, we propose a real-time travel assistance system based on deep learning. The hardware comprises an NVIDIA Jetson Nano controller, an Intel D435i depth camera for environmental sensing, and SG90 servo motors for feedback. To address embedded device computational constraints, we developed a lightweight object detection and segmentation algorithm. Key innovations include a multi-scale attention feature extraction backbone, a dual-stream fusion module incorporating the Mamba architecture, and adaptive context-aware detection/segmentation heads. This design ensures high computational efficiency and real-time performance. The system workflow is as follows: (1) the D435i captures real-time environmental data; (2) the processor analyzes this data, converting obstacle distances and path deviations into electrical signals; (3) servo motors deliver vibratory feedback for guidance and alerts. Preliminary tests confirm that the system can effectively detect obstacles and correct path deviations in real time, suggesting its potential to assist BVI users. However, as this is a work in progress, comprehensive field trials with BVI participants are required to fully validate its efficacy. Full article

This paper proposes an anti-disturbance model predictive fault-tolerance control strategy for open-circuit faults of five-phase flux intensifying fault-tolerant interior permanent magnet (FIFT-IPM) motors. This strategy is applicable to electric agricultural equipment that has an open winding failure. Due to the rich third-harmonic back electromotive force (EMF) content of five-phase FIFT-IPM motors, the existing model predictive current fault-tolerant control algorithms fail to effectively track fundamental and third-harmonic currents. This results in high harmonic distortion in the phase current. Hence, this paper innovatively proposes a multi-plane virtual vector model predictive fault-tolerant control strategy that can achieve rapid and effective control of both the fundamental and harmonic planes while ensuring good dynamic stability performance. Additionally, considering that electric agricultural equipment is usually in a multi-disturbance working environment, this paper introduces an adaptive gain sliding-mode disturbance observer. This observer estimates complex disturbances and feeds them back into the control system, which possesses good resistance to complex disturbances. Finally, the feasibility and effectiveness of the proposed control strategy are verified by experimental results. Full article

The electromechanical composite transmission technology for tracked vehicles demonstrates excellent performance in energy efficiency, mobility, and ride comfort. However, due to frequent operation under harsh conditions, the components of the electric drive system, such as drive motors, are prone to failures. This paper proposes three fault-tolerant control methods for three typical fault scenarios of the electromechanical composite transmission system (ECTS) to ensure the normal operation of tracked vehicles. Firstly, an ECTS and the electromechanical coupling dynamics model of the tracked vehicle are established. Moreover, a double-layer anti-windup PID control for motors and an instantaneous optimal control strategy for the engine are proposed in the fault-free case. Secondly, an anti-windup PID control law for motors and an engine control strategy considering the state of charge (SOC) and driving demands are developed in the case of single-side drive motor failure. Thirdly, a B4 clutch control strategy during starting and a steering brake control strategy are proposed in the case of electric drive system failure. Finally, in the straight-driving condition of the tracked vehicle, the throttle opening is set as 0.6, and the motor failure is triggered at 15 s during the acceleration process. Numerical simulations verify the fault-tolerant control strategies’ feasibility, using the tracked vehicle’s maximum speed and acceleration at 30 s as indicators for dynamic performance evaluation. The simulation results show that under single-motor fault, its straight-line driving power drops by 33.37%; with electric drive failure, the drop reaches 43.86%. The vehicle can still maintain normal straight-line driving and steering under fault conditions. Full article