Search Results (3,136)

The influence of standardized driving-cycle characteristics on the dynamic and energy performance of a parallel hybrid electric vehicle controlled by a fixed-gain PID speed controller was investigated. A control-oriented MATLAB/Simulink model was developed, including an electric traction subsystem, an electric battery pack, a simplified internal combustion engine subsystem, a supervisory torque-split controller and longitudinal vehicle dynamics. The same controller configuration was evaluated under the FTP75, HWFET and US06 cycles, with the shorter cycles repeated to obtain comparable durations. Control quality was assessed using RMSE, MAE, IAE and ITAE, whereas energy performance was quantified using battery state-of-charge variation, fuel consumption, engine utilization and traction motor current loading. FTP75 yielded favorable performance, with RMSE = 0.265 m/s, fuel consumption of 4.824 L/100 km and an SoC decrease of 19.698%, whereas US06 proved severe, with RMSE = 4.567 m/s, fuel consumption of 10.328 L/100 km, an SoC decrease of 41.630% and a peak motor current of 580.9 A. Sensitivity analysis showed that ±20% PID-gain variations do not materially alter the principal conclusion, while supervisory energy-management parameters exert a stronger influence on the trade-off between tracking quality, fuel expenditure and charge maintenance. The results confirm that fixed-gain PID control is cycle-dependent and becomes inadequate under aggressive driving conditions. Full article

There is a growing trend towards vehicles powered by alternative energy sources due to the environmental pollution caused by fossil fuel vehicles. Electric vehicles (EVs) are thought to make a significant contribution to reducing environmental pollution. This study presents a performance comparison of field-oriented control (FOC) and finite control set-based model predictive current control (FCS-MPCC) methods for controlling PMSM motors, which are commonly preferred for EV applications. A multilevel ANPC inverter topology, which has a higher-quality power flow than classical two-level inverters, was preferred to power the PMSM. While the classical FOC method has a fixed switching frequency by including cascaded PI controllers and a pulse width modulation (PWM) modulator, the FCS-MPCC method determines a variable frequency-switching signal that minimizes the cost function by predicting the future current behavior of the PMSM using the mathematical model of the system. The performance comparison of FOC and FCS-MPCC methods was carried out by conducting real-time experimental studies. Both control algorithms were analyzed under variable speed and load conditions using the same motor and drive structure. Performance analysis of FOC and FCS-MPCC control algorithms was carried out in terms of speed tracking, torque, current, and harmonics. According to the results obtained, the total harmonic distortion (THD) value of the stator current was 7.03% in the FOC method, while it was 22.19% in the FCS-MPCC method. Furthermore, a comparative analysis was conducted on the dynamic performance of the two methods in different scenarios using the mean absolute error (MAE), root mean square error (RMSE), integral absolute error (IAE), integrated time absolute error (ITAE), and integral squared error (ISE) criteria. The FCS-MPCC method was observed to be superior in different speed scenarios according to these criteria. In terms of processor load, it was calculated as 17.09% in the FOC method and 63.75% in the FCS-MPCC method. This study is important for determining the control strategy of PMSMs used in EV drives. Full article

Considering that conventional heavy-duty mining trucks equipped with centralized drive systems suffer from low transmission efficiency and limited flexibility in power distribution, this study focuses on distributed independent-drive heavy-duty mining trucks and develops energy-saving control strategies from two perspectives: drive torque control and regenerative braking. For the drive torque control, based on the principle of optimal driving efficiency, the overall efficiency of the drive motors is selected as the objective function, and an adaptive genetic algorithm (AGA) is employed to optimize the torque distribution coefficients among the axles offline. For regenerative braking, a fuzzy-control-based electromechanical braking distribution strategy and a dynamic-load-based inter-axle braking force allocation strategy are proposed. Finally, a co-simulation was conducted using MATLAB/Simulink and TruckSim based on specific open-pit mining conditions. Compared with the conventional baseline without energy-saving control, the simulation results demonstrate that under the single-cycle operation, the proposed strategy increases the driving energy utilization rate by 5.69% and achieves a braking energy recovery rate of 39.41%. Furthermore, under the full-mine cyclic operation, the proposed strategy extends the vehicle’s operational duration on a single charge by 200%. These findings demonstrate the strong potential of the proposed strategy to improve overall driving efficiency and fully exploit the regenerative braking capabilities of heavy-duty mining trucks, thereby providing theoretical support for enhancing their economic efficiency and driving range. Full article

Magnetic gears offer an energy-efficient alternative to conventional mechanical gears through magnetic fields rather than physical contact. A non-contact operation eliminates frictional losses, mitigates wear, and reduces vibration, noise and maintenance requirements. In recent years, the development of magnetic gear technologies has accelerated, driven by advances in materials, innovative gear topologies, and emerging applications. This paper presents a comprehensive review of magnetic gear technologies with particular emphasis on their applications in battery electric vehicles and hybrid electric vehicles. First, the development of magnetic gears is reviewed from early converted magnetic analogues of mechanical gears to high-performance field-modulated variable gear designs. The review subsequently examines the use of magnetic gears in electric vehicle applications, including magnetic geared in-wheel motors, traction modules, and magnetic gears in hybrid electric vehicles, such as magnetic variable gears for hybrid vehicle applications, magnetic geared electric variable transmissions and power-splitting devices. The review also discusses novel and less-established MG applications before examining the challenges limiting their widespread adoption in EV and HEV drivetrains. Full article

This study presents the preliminary development of a clustered hybrid propulsion module, and its experimental validation from static motor characterization to dynamic 1-D vertical drop tests to assess the feasibility of a hybrid propulsion system for soft-landing applications. The research progresses from preliminary design of core components (such as fuel, oxidizer supply system, engine configuration), to the performance verification of the clustering module. First, the trade-off between high regression rates and mechanical integrity was evaluated for paraffin-based fuels. However, high-density polyethylene (HDPE) was utilized as the baseline to ensure predictable combustion behavior. Second, cold flow tests of the designed multi-port manifold demonstrated a highly uniform oxidizer distribution, validating the geometric design with a maximum spatial pressure deviation of 2.44% across the four engines. Third, static fire tests confirmed robust dynamic control capabilities, successfully throttling the average chamber pressure from 100% (7.00 bar) down to 43% (3.01 bar) and back to 100% (7.01 bar) with a transient response time of approximately 0.6 s. Finally, the 1-D vertical drop test validated the operational readiness of the system; the open-loop thrust modulation successfully counteracted the module’s dynamic weight, achieving a terminal descent velocity of 1.46 m/s, which strictly satisfies planetary soft-landing safety criteria. These results demonstrate the feasibility and performance of clustered hybrid propulsion systems for planetary exploration, extending to surface launch technology for sample return missions from the Moon and Mars, and precision booster recovery for small launch vehicles. Full article

Multirotor unmanned aerial vehicles (UAVs) rely on electronic speed controllers (ESCs) that decode motor commands from pulse-width modulation (PWM) signals, making the flight-controller-to-ESC command path a physical-layer attack surface for intentional electromagnetic interference (IEMI). This paper presents a mechanism-based analysis of IEMI attacks that induce motor stoppage in UAV brushless DC motor controllers. We develop a timing-error model in which a sinusoidal disturbance on the PWM line shifts the detected edge instants and drives the decoded pulse width into stop-equivalent regimes, and we show that the disturbance reaching the ESC’s thresholding node is shaped by a frequency-selective cascade of the PWM cable’s coupling response and the ESC’s input-path transfer function. We experimentally characterize this model on five commercial ESCs through conducted and radiated injection. The measured thresholds differ by more than an order of magnitude across ESCs and are reordered between frequency bands and injection modes; comparing conducted and radiated results allows us to attribute these differences primarily to the cable coupling response and reveals cases where it either hides or amplifies an ESC’s susceptibility. The susceptible frequency also shifts with PWM cable length in qualitative agreement with transmission-line resonance, confirming that observed radiated susceptibility reflects the joint design of ESC and cable rather than a single intrinsic property. The cable lengths examined here (45–125 cm) are longer than those of compact multirotors and were chosen to place resonances within our antenna’s band; we discuss the implications of this choice and identify shorter, deployment-realistic cables as a priority for future work. Full article

Designing low-voltage (LV) power distribution systems for mass-sensitive electric vehicles involves several unresolved technical challenges, including parasitic $I^{2}R$ losses, excessive mass of commercial off-the-shelf distribution units, and difficulties in isolating thermal phenomena during vehicle operation. In dynamic racing conditions, temperature measurements of LV components are strongly influenced by external heat sources such as traction batteries, motors, and inverters, complicating accurate assessment of conductor self-heating and distribution losses. This work presents a load-driven methodology for the specification, implementation, and validation of LV architectures, demonstrated using a Formula Student electric race car. The proposed approach combines harness current mapping, resistive loss modeling, and component-level topology optimization to support the development of lightweight and electrically robust systems. Within this framework, a mass-optimized programmable solid-state power distribution unit (PDU), an auxiliary battery system with a battery management system (BMS), and an optimized LV wiring harness were developed and experimentally validated through controlled subsystem tests and in-vehicle operation. The proposed methodology enabled reduction in PDU mass by 40–80% relative to commercially available solutions while maintaining programmable protection, integrated current sensing, and stable thermal operation under representative racing loads. This reduction was achieved through load-driven conductor sizing, application-specific protection threshold optimization, and elimination of redundant protection and interconnection hardware. The developed PDU achieved a mass of 155 g with measured channel resistances of 40–70 m$\mathsf{\Omega }$. The auxiliary battery pack exhibited an average internal resistance of 64.2 m$\mathsf{\Omega }$ at a total mass of 2190 g, while the optimized harness demonstrated resistivity in the range of 14.72–33.98 m$\mathsf{\Omega }$/m. Experimental validation confirmed stable operation below critical thermal limits under both nominal and off-nominal load conditions. The obtained results demonstrate that the proposed methodology enables measurable reductions in both system mass and resistive power losses through application-specific optimization of the LV architecture. However, the presented approach is primarily suited for motorsport and other highly mass-constrained applications, where reduced packaging volume, efficiency, and weight justify the increased design complexity and lower universality compared to commercial off-the-shelf solutions. Full article

Hybrid tractors are a promising solution for reducing fuel consumption and emissions in agricultural machinery. However, their low-speed, high-torque operation with frequent load fluctuations demands an energy management strategy (EMS) that is both real-time capable and highly adaptive. This study focuses on a coaxial parallel hybrid electric tractor, developing a forward simulation model that integrates longitudinal vehicle dynamics, engine, motor, battery, and transmission systems. An improved equivalent fuel consumption minimization strategy (ECMS) with state-of-charge feedback correction, termed F-ECMS, is proposed. It dynamically adjusts the equivalence factor based on real-time battery SOC to approach optimal fuel economy while sustaining charge. Dynamic programming (DP) is used to establish a global benchmark. Simulations under a typical plowing cycle show that over 14,400 s, the F-ECMS maintains SOC (0.5964) close to the DP reference (0.6000), while achieving a 1.51% reduction in equivalent fuel consumption compared to a rule-based strategy. The results demonstrate that the proposed F-ECMS offers an effective balance between real-time performance and fuel economy, showing strong potential for practical implementation in hybrid agricultural vehicles. Full article

Previous research has primarily focused on the restorative effects of environments on the general population, often overlooking the specific restorative capacity of urban settings for the disabled population. There is a lack of comprehensive investigation into the interaction between accessibility elements and urban restorativeness. This study, conducted in Shenzhen, Guangdong Province, China, categorizes streetscape accessibility elements for the disabled population and develops a recognition system based on an enhanced DeeplabV3+ framework. Semantic segmentation of streetscape accessibility elements was performed using 201,860 sampling points and 807,440 street view images. This study employed a combination of TrueSkill scoring, sentiment semantic analysis, LDA topic modeling, and LAB color clustering to quantify and visualize urban restorativeness. The impact of accessibility elements on urban restorativeness was explored using the XGBoost-SHAP model. Results indicate significant effects of architectural space constraints and high-density motor vehicle distribution on the safety of the disabled population’s mobility. The low pixel ratio of accessibility facilities and signs indicates insufficient infrastructure, while high landscape recognition rates exhibit significant spatial coverage heterogeneity. Detection rates for the disabled population in street views are nearly zero, highlighting a severe lack of inclusivity in pedestrian environments. Urban restorativeness exhibited a pattern of being higher in the south and east, and lower in the north and west. Among the accessibility elements, public green spaces (PGS) contributed the most to urban restorativeness, accounting for 25% of the impact, and the study elucidates the mechanisms through which various elements affect urban restorativeness. This absence stems from spatial competition, missing co-design, threshold effect conflicts, and color interference mechanisms. This research breaks away from traditional linear analytical frameworks and reveals the complex non-linear relationship between accessibility elements and urban restorativeness through the XGBoost-SHAP model, providing a quantitative decision-making tool for planning accessible environments in high-density cities. Full article

Fault detection and diagnosis of three-phase inverter-fed motor drives is essential for ensuring system reliability, safety, and continuous operation in applications such as electric vehicles and industrial automation. This paper proposes a data-driven fault detection framework based on normalized current features and a lightweight bidirectional long short-term memory (BiLSTM) network which can be generalized to different motor power rating in the same controller system. A compact set of six time-domain features, consisting of the mean and root-mean-square (RMS) values of the phase currents, is extracted and normalized with respect to the average RMS value. This normalization effectively removes dependency on operating conditions, enabling the model to generalize across different load levels and motor power ratings without retraining. A lightweight BiLSTM architecture is employed, reducing computational complexity while maintaining high diagnostic performance. The proposed method is validated under various operating conditions, including different speeds, load variations, motor power ratings, and noisy conditions. The results demonstrate an overall classification accuracy of 99.65%, with reliable fault detection achieved within less than half of a fundamental cycle. The proposed approach provides an efficient, robust, and scalable solution for inverter fault detection and diagnosis, offering strong potential for practical deployment in modern motor drive systems. Full article

The reliability of the steering system directly impacts the safety of autonomous driving. Addressing the issue of trajectory deviation easily caused by motor failure in redundant steer-by-wire (SBW) systems, this paper aims to improve vehicle tracking accuracy under fault conditions. A hierarchical fault-tolerant control strategy based on a zeroing neural network (ZNN) is proposed: the upper layer uses the Stanley algorithm for path planning, while the lower layer designs a ZNN controller with preset performance constraints, and instantaneous power reconfiguration is achieved through Jacobi pseudo-inverse. Simulation results show that under high-speed lane changes and sinusoidal conditions, this strategy can achieve millisecond-level task reassignment, and compared to PID control, the maximum absolute error of lateral tracking under fault conditions is reduced by over 50%, and the root mean square error is reduced by over 30%. This method effectively improves driving safety and trajectory fidelity when actuators fail. Full article

This paper presents a mathematical modeling-based vision-guided triple-loop control method for lane tracking of an underactuated bicycle robot. To describe the coupling between lateral balance and path tracking, a reaction-wheel-based inverted-pendulum model is established using the Lagrange formulation. Based on the linearized dynamics, the transfer function between the flywheel rotational speed and the motor torque is derived, providing a mathematical basis for designing the gain-scheduled triple-loop PID controller. To generate continuous control inputs under practical visual disturbances, an improved Hough transform, a near-field multi-layer sliding window detector, and a multi-scenario finite-state-machine strategy are incorporated for lateral deviation estimation and path reconstruction. A cascaded smoothing filter is further introduced to reduce high-frequency command fluctuations and improve the closed-loop control response. Real-vehicle experiments on an STM32F407-based underactuated bicycle robot demonstrate that the proposed framework achieves stable dynamic balance and robust lane tracking. Compared with a conventional Hough-transform and sliding window method, the lateral RMSE is reduced by 40.2%, 39.85%, and 32.35% in straight, left-turn, and right-turn scenarios, respectively. Full article

Flat-wire windings have been widely used in high-power-density electric vehicle motors because of their high slot fill factor and high efficiency. However, conventional flat-wire conductors usually have relatively large cross-sectional dimensions, which may lead to significant AC winding losses under high-frequency operation due to the combined effects of the rotor magnetic field and the armature-reaction field. To address this issue, this paper proposes a multilayer thin flat-wire continuous-wave winding and its end-winding transposition method. The parallel multilayer thin flat-wire structure effectively suppresses AC losses by reducing the characteristic dimension of each conductor, while the end-winding transposition method reduces or even eliminates circulating-current losses among parallel strands without compromising slot utilization. An analytical calculation method is established to investigate the AC loss characteristics of the multilayer thin flat-wire winding, and the main influencing factors of winding losses are analyzed. To address the circulating-current loss issue, the loss suppression effect of the transposition method is quantitatively evaluated, and an intermittent transposition method with both effective circulating-current suppression and fewer end-winding crossovers is proposed. Finally, the proposed method is validated by finite-element analysis (FEA) and prototype experiments. The results show that the proposed winding can significantly reduce AC losses over a wide speed range, providing a low loss and manufacturable winding design solution for high-power-density electric vehicle traction motors. Full article

The microstructure of as-cast Al-xSi (x = 4, 7, 10) alloys solidified under various cooling rates was characterized using scanning electron microscopy (SEM). To overcome the multicollinearity among eutectic silicon parameters, Principal Component Regression (PCR) analysis was employed to quantitatively evaluate the effects of silicon morphology, scale, and content on the electrical conductivity of the alloys. The results demonstrate that rapid solidification significantly refines the plate-like eutectic silicon and reduces its volume fraction, leading to improved electrical conductivity. The PCR model shows that a hierarchical mechanism: volume fraction (PC1) acts as the principal determinant, increasing baseline resistance primarily by truncating the electron mean free path (MFP); meanwhile, within identical alloy systems, morphological parameters (PC2) play a dominant regulatory role. A semi-quantitative electron drift path model was established, confirming that the morphological deviation of eutectic silicon from a spherical shape (i.e., increased aspect ratio) causes a non-linear increase in the amplitude of electron detours. This geometric elongation significantly degrades electrical conductivity, providing theoretical guidance for the microstructural design of high-conductivity Al-Si alloys, which can be practically applied to the manufacturing and optimization of lightweight, heat-dissipating enclosures for new energy vehicle (NEV) motors and power distribution systems. Full article

This paper presents the design and experimental validation of a self-contained rotating Halbach array—based demonstrator for electrodynamic suspension (EDS) systems. The proposed platform was developed to bridge the gap between conventional externally powered laboratory testbeds and large-scale EDS vehicles by enabling investigation of levitation behavior under realistic onboard mass and subsystem integration constraints. The system integrates rotating circular Halbach arrays, onboard power supply, sensing, motor control, and structural support within a single levitated architecture. Experimental validation was conducted under a constrained one-degree-of-freedom configuration allowing vertical motion only. The system achieved stable levitation of a 35 kg platform and supported additional payloads approaching a 1:2 ratio relative to the baseline mass, while maintaining air-gap stability within approximately ±0.1 mm. The experimental results further reveal that the operational limit of the system is governed by actuation power and current constraints rather than electromagnetic levitation capability, highlighting a key distinction between self-contained and externally powered EDS systems. The proposed demonstrator provides a compact and practical experimental platform for the validation and performance evaluation of Halbach-array-based EDS systems. In addition, the study presents practical engineering insights regarding payload distribution, actuator saturation, structural integration, and system-level design constraints relevant to future self-contained EDS platforms and control-oriented levitation systems. Full article