Search Results (6,115)

Fuel cell systems require high-efficiency DC–DC interfaces capable of regulating rapid voltage variations while respecting the operational constraints of proton-exchange membrane fuel cells (PEMFCs). The floating interleaved boost converter (FIBC) is a strong candidate for this purpose due to its reduced current ripple, improved power sharing, and lower component stress. The design of control strategies for FIBCs supplied by PEMFCs remains challenging because explicitly enforcing fuel cell operational constraints under fast converter dynamics is inherently difficult, particularly when detailed fuel cell models are unavailable or undesirable, as reflected in existing approaches such as classical linear regulators and single-model predictive schemes. Therefore, this paper proposes a multi-model finite control set model-based predictive control (MM-FCS-MPC) strategy for FIBC converters supplied by PEMFCs. The method employs multiple discrete prediction models with cost functions defined by the converter switching mode, integrates a fuel cell-aware reference-generation mechanism to ensure nominal and safe PEMFC operation by enforcing current and power constraints within the predictive framework, and enables fast, accurate output-voltage regulation. Detailed modelling of the FIBC, component sizing, and PEMFC characteristics is provided. Obtained results under load disturbances and reference variations validate the proposed control scheme, demonstrating improved transient dynamics, reduced steady-state error, and enhanced current-sharing performance. Obtained results under load disturbances and reference variations validate the proposed control scheme, demonstrating improved transient dynamics, reduced steady-state error, and enhanced current-sharing performance, with a rise time of approximately 4.4 ms, a $\pm 2\%$ settling time of 10.3 ms, a maximum overshoot of only 0.056%, and a phase delay of approximately 4.26${}^{\circ }$, compared with 9.6${}^{\circ }$ for the conventional PI voltage-tracking baseline. Full article

There is a growing need for high-efficiency power electronic converters that can effectively convert energy, regulate voltages, and enhance power quality in standalone power generators, as the use of renewable energy sources and battery energy storage devices increases. The quasi-impedance-source inverter (qZSI) has attracted significant interest due to its single-stage buck-boost operation, continuous input current, reduced reliance on passive elements, and increased reliability. In this paper, the control strategy and implementation of the qZSI with battery assistance for standalone photovoltaic energy generation are discussed. To analyze the operational characteristics and design the control strategy of the qZSI, the system equations are linearized around the nominal operating point to develop a small-signal model, from which the direct current (DC) side and alternative current (AC) side transfer functions are derived and used as the basis for controller design. Using the proposed model, hybrid controllers are designed to control the shoot-through duty cycle, maintain DC link voltage stability, and battery charging to achieve stable power generation. Furthermore, the SPWM technique is applied to produce AC power with minimal harmonic content and higher efficiency. Application results show stable dynamic behavior, effective battery energy management, improved voltage regulation, and reduced harmonic distortion in the output waveform. The main contribution is a low-complexity coordinated PI and PR control framework for standalone battery-assisted qZSI operation, experimentally validated under DC- and AC-side disturbances without requiring an additional battery-side power-conversion stage. Full article

This paper presents a novel fault-tolerant model predictive control (FTMPC) framework for fuel cell hybrid electric vehicles (FCHEVs) used for postal delivery applications. The main contribution of the proposed FTMPC is the adaptive adjustment of the model predictive control cost function weights based on fault severity. The proposed reformulation incorporates fault characterization across the diverse degradation mechanisms while maintaining reliable vehicle operation. The FTMPC approach dynamically adapts cost function weights and system constraints based on the fault severity index. The resulting control strategy provides fault-aware power allocation between the fuel cell and battery while accounting for the specified operating and safety constraints. To isolate the contribution of the proposed health-dependent adaptation mechanism, a controlled ablation study was performed against a structurally identical fixed-MPC controller under the same vehicle model, driving cycle, initial conditions, prediction and control horizons, solver configuration, and fault scenarios. The adaptive FTMPC achieved a 10.6956% reduction in direct hydrogen consumption relative to the fixed-MPC baseline. Because differences in terminal battery state of charge (SoC) can influence comparisons based solely on hydrogen consumption, a charge-corrected hydrogen-equivalent metric was also evaluated; using this more conservative metric, the adaptive FTMPC retained a 2.7276% improvement. The final quadratic programming implementation achieved a 100% successful optimization rate in the validation run with no fallback-controller activation, while the maximum soft-constraint slack remained on the order of 10−9. Additional sensitivity analyses were conducted to evaluate the influence of relevant vehicle and operating conditions on energy consumption and battery utilization. These results provide direct quantitative evidence of the contribution of the proposed fault-adaptive mechanism and demonstrate its numerical feasibility for FCHEV energy management, while the limitations of the present simulation-based validation are explicitly acknowledged. Full article

Interior Permanent Magnet Synchronous Motors (IPMSMs) are widely used in traction and industrial drive systems because they combine high efficiency, high power density, and excellent performance over a wide speed range. Rotor eccentricity, however, remains one of the most significant faults affecting their reliable operation; diagnosing it early is crucial for avoiding unexpected breakdowns. To achieve this, the analysis utilizes a simulation-based fault diagnosis framework that combines the Finite Element Method (FEM) with machine learning. A 550 W, 220 V IPMSM was modeled in ANSYS Maxwell to simulate dynamic eccentricity faults at three severity levels: 10%, 20%, and 40%. A fixed-length, non-overlapping window segmentation approach was used to pull statistical features from the stator current and radial air-gap flux density signals. These features were then fed into several supervised machine learning algorithms, evaluated using a consistent 5-fold cross-validation protocol across all investigated classifiers, with the Ensemble Bagged Trees classifier achieving validation accuracies of 93.12% for radial air-gap flux density and 87.86% for stator current. By integrating finite-element analysis, statistical feature extraction, and comparative machine learning, the proposed framework demonstrates the feasibility of simulation-based dynamic eccentricity severity classification in IPMSMs. The results indicate that Ensemble Bagged Trees provide the best classification performance among the evaluated classifiers, establishing a foundation for future experimental validation and real-time condition-monitoring applications. Full article

In this paper, the existence and uniqueness of solutions for a class of impulsive fractional Volterra–Fredholm integro-differential equations (VFIDEs) with Caputo derivative are investigated. The mathematical framework is developed in the piecewise continuous space $\mathsf{PC}\left(\right[0,1],\mathsf{X})$ to properly accommodate impulsive solutions with state jumps. The existence of the solution is obtained based on the application of Krasnoselskii’s fixed-point theorem (FPT), while the uniqueness is established using Banach’s FPT under suitable Lipschitz and boundedness conditions. The main contribution of this work concerns the controllability investigation, where sufficient conditions for the controllability of the impulsive fractional system are derived. A feedback control function is constructed using the controllability Gramian operator, and the existence of a solution to the controlled system is proved via Schauder’s FPT. The controllability criteria are validated through a numerical example with explicit parameter verification, where the corrected integral boundary condition properly accounts for impulse contributions occurring in the interval $[0,\eta ]$. To bridge theory and applications, we investigate artificial neural networks (ANNs) as surrogate models for fast state prediction. The input features are restricted to quantities computable solely from $(\zeta ,\mathsf{u}(\zeta \left)\right)$, eliminating target leakage. The dataset is split by complete trajectories to ensure proper generalization assessment. The predictive power of the designed neural network gives an ${\mathcal{R}}^2\approx 0.995$ score on unseen test trajectories, with additional verification of equation residuals, impulse errors and boundary condition errors. This indicates that the network effectively captures both the non-local memory behavior and the impulsive discontinuities of the system. The hybrid mathematical–AI framework presented in this work provides both theoretical guarantees and practical computational tools for complex fractional dynamical systems with memory effects and impulsive properties. Full article

In photovoltaic-based hybrid energy storage systems (PV–HESS), rapid power transients accelerate battery degradation, directly reducing the operating lifetime and sustainability of renewable power resources. To address this issue, the current study proposes an optimal coordinated framework for the simultaneous and coordinated tuning of battery and supercapacitor current-loop proportional–integral (PI) controllers. The proposed framework treats the four PI gains of the battery and supercapacitor controllers as a unified optimization problem, applying five metaheuristic algorithms: Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Gazelle Optimization Algorithm (GOA), Artificial Protozoa Optimizer (APO), and White Shark Optimization (WSO). The optimization problem is directly coupled with a full nonlinear MATLAB 2022b/Simulink PV–HESS model, capturing the dynamic interactions among the PV array, bidirectional converters, DC-link capacitor, storage units, and load. A combined Integral of Time-weighted Absolute Error (ITAE) objective function is used to minimize current tracking errors, ensuring the supercapacitor absorbs fast power fluctuations while shielding the battery from high-frequency thermal and electrical stress. The controllers are evaluated across four operating scenarios involving steady irradiance shifts, rapid irradiance fluctuations, load disturbances, and a simultaneous irradiance drop from 1000 W/m² to 400 W/m² with a 33% load increase. The results confirm stable DC-link regulation and effective power sharing. Specifically, APO delivers superior performance in the high-stress scenario, GOA minimizes transient-error indices, and GA achieves the lowest DC-link voltage RMSE. These findings demonstrate that coordinated tuning effectively balances high-frequency dynamics, extending battery service life and enhancing the long-term operational sustainability of solar microgrid storage. Full article

The application of DC power electronics has expanded significantly across modern transmission and distribution systems, spanning medium-voltage direct current (MVDC) and high-voltage direct current (HVDC) networks, as well as low-power DC supplies for nanoscale applications, in which experimental validation remains indispensable for verifying system performance and control strategies. However, developing experimental platforms for high-power, high-voltage, or nanoscale DC systems is technically challenging and expensive and has not been adequately addressed in the existing literature. To overcome these limitations, this paper presents a per-unit-based experimental twin (Ex-Twin) framework for Buck converters implemented with a nested-loop control architecture. By transforming the converter dynamics from the time domain to an angular-domain representation, the proposed per-unit framework enables the development of dynamically equivalent experimental twins across a wide range of operating scales. The feasibility and effectiveness of the proposed Ex-Twin framework are validated through comprehensive electromagnetic (EMT) simulation studies and hardware experimental results, demonstrating its potential as a scalable and cost-effective platform for the development, testing, and validation of advanced DC power electronic systems. EMT simulations verify the cross-scale dynamic equivalence of the converter models over power ratings from 2 kW to 2 MW and voltage levels from 54 V to 54 kV, provided that the corresponding per-unit parameters and normalized disturbances are preserved. Under the investigated operating conditions, the systems achieved efficiencies of 95.3%, 96.7%, and 94.3% at 2 kW, 20 kW, and 2 MW, respectively, using both the real-scale and corresponding per-unit controllers. These results demonstrate consistent performance across the evaluated power scales. In addition, laboratory tests conducted on a 1 kW, 50 V Buck-converter platform validate the real-time implementation of both the real-scale and per-unit controllers under reference-voltage and load disturbances. An experimentally measured efficiency of 92.1% was achieved with both controllers, further demonstrating their consistent performance. Full article

With the increasing electrification and intelligence of commercial vehicles, the on-board power demand has risen significantly, rendering conventional independent power supply architecture inadequate. This study proposes a parallel power supply architecture utilizing two identical 28 V/120 A generators, where simplified droop control inherent in standard alternator regulation achieves automatic load sharing without complex active controllers, offering a more cost-effective and redundant alternative to conventional independent power supply systems. To validate the feasibility and stability of this parallel system, a simulation model was established, focusing on load sharing and current stability under dynamic conditions. Furthermore, empirical tests were conducted on a 10-m commercial bus under extreme conditions. The results demonstrate that the parallel architecture maintains a lower air conditioner current, thereby improving system efficiency and operational reliability compared to conventional independent power supply architecture. Full article

This work introduces a novel self-sustainable wireless communication architecture that combines RIS and NOMA with a hybrid acoustic–magnetic energy harvesting framework. The source node operates under strict energy constraints and is powered by harvesting ambient acoustic vibrations and surrounding magnetic fields, enabling autonomous transmission to multiple NOMA users. To enhance propagation conditions, an RIS is deployed to intelligently control the wireless channel by optimizing its phase response, thereby strengthening desired signals and suppressing interference.The performance of the proposed system is analytically characterized under the hybrid energy harvesting model. The derived expressions provide insight into the interaction between harvested energy dynamics, RIS configuration, and NOMA transmission. Simulation results confirm that the proposed RIS-NOMA architecture significantly outperforms conventional orthogonal and non-orthogonal access schemes in both spectral and energy efficiency. In addition, the hybrid harvesting mechanism ensures more stable energy availability, improving reliability in highly energy-constrained environments. Full article

Addressing the dual threats of malicious jamming and time-varying fading faced by wireless communication links in complex dynamic electromagnetic adversarial environments, existing intelligent anti-jamming methods predominantly focus on single-dimensional resource optimization under quasi-static channels. This focus neglects the nonlinear superposition effects of multi-path deep fading and dynamic strong jamming in the time-frequency domain, making it challenging for systems to balance transmission reliability and system energy efficiency in physical environments where fading and suppression coexist. To address this issue, this study proposes a joint intelligent anti-jamming method for channel switching and transmit power control based on a Deep Q-Network (DQN). Initially, a composite communication environment model incorporating Markov time-varying fading and jamming is constructed. Subsequently, the joint resource scheduling problem is formulated as a Markov Decision Process. The environment state space is reconstructed by integrating continuous channel state estimation and jamming observation features, accompanied by the design of a highly aggregated two-dimensional discrete action space for both channel and power. Finally, a composite reward function evaluating both communication success rates and power consumption costs is proposed to guide the agent in multi-dimensional resource joint optimization. Simulation results demonstrate that the proposed algorithm effectively extracts implicit features under the composite state of fading and jamming. When encountering extreme deep fading or full-band blocking, the agent strategically triggers a silent mechanism to avoid exorbitant invalid energy consumption penalties, while precisely matching interference-free channels with the minimum effective transmit power during favorable communication windows. Simulation results show that compared with traditional xx algorithms, the proposed method significantly improves the dynamic successful transmission rate and system energy efficiency in complex, highly dynamic scenarios, achieving an effective optimization of anti-jamming reliability and low power overhead. Full article

Agricultural drip fertigation is a highly coupled dynamic process in which precision resource management depends on the coordinated performance of the entire equipment chain. Against the backdrop of global water scarcity and excessive fertilizer application, improving the full-process precision of mixing, injection, sensing, control, distribution, and terminal delivery has become a prerequisite for the wider adoption of fertigation. This review evaluates advanced process-monitoring technologies and closed-loop control architectures within modern cyber-physical fertigation systems, covering fertilizer solution preparation and mixing, injection devices, liquid- and solid-phase state sensing, intelligent control algorithms, and pipeline distribution with terminal emitters. Online mixing has evolved from gravity-based batch pre-mixing toward continuous metered injection with vortex-guided static mixing, electrical conductivity (EC) sensing with drift compensation and granular mass flow detection form the perceptual basis of closed-loop regulation, control has advanced from proportional–integral–derivative (PID) controllers through variable-universe fuzzy logic to artificial neural network (ANN) hybrids with metaheuristic optimization, and pipeline pressure regulation together with emitter anti-clogging strategies determine long-term distribution uniformity. A quantitative analysis shows that the attainable precision of the sensing–decision–execution chain is bounded by the coupling among sensor accuracy, process delays, control performance, and actuator response rather than by any single device. The review identifies five unresolved gaps—sensor reliability, multi-season field validation, interoperability, low-cost automation, and fertilizer-type adaptability—and recommends that future research prioritize low-cost Internet of Things (IoT) sensor arrays on low-power wide-area networks, edge–cloud collaborative control, and foundation-model-driven autonomous decision-making, co-designed as one coupled specification. Full article

Photovoltaic microinverters operating under rapidly varying irradiance are subject to large changes in input voltage and power flow, for which controllers designed from a single operating-point linearisation may lose their intended dynamic properties. This paper develops an exact feedback linearisation framework for a grid-connected photovoltaic microinverter equipped with a hybrid energy storage system comprising a battery and an ultracapacitor. Exact feedback linearising laws are derived for the current-control loops of the photovoltaic boost converter and of the bidirectional battery and ultracapacitor converters. An admissible operating region is obtained explicitly, ensuring that the control actions remain within modulation limits. The resulting inner-loop dynamics become independent of the operating point and of input-voltage variations. The proposed method is evaluated through detailed simulations under severe irradiance transients and is further validated on a laboratory-scale prototype. For an input-voltage step to $12.1\mathrm{V}$, the proposed controller reduces the current-tracking charge deficit from 5.72 A·ms to 0.0224 A·ms over a $5\mathrm{ms}$ interval when compared with a conventional small-signal design. During the most demanding irradiance transient, the peak-to-peak deviation of the dc-link voltage stays below 16% while the ultracapacitor supplies up to 2.8 A. The model agrees well with the experimental results at steady state and at the extremes of the dc-link, but it overestimates the transient deviation of the dc-link by 27%, the peak current of the ultracapacitor by 69%, and its switching ripple by 63%. Full article

Reinforcement learning (RL) has become a powerful tool for quadrupedal locomotion, and a sim-to-real approach is widely adopted to avoid hardware damage during training. However, the “sim-to-real gap” remains a critical challenge, particularly for robots driven by high-gear-ratio actuators, in which nonlinear friction effects are strongly amplified. Conventional methods, such as actuator networks or heuristic domain randomization, often require specialized sensors or extensive trial-and-error to tune appropriate randomization ranges. Building on a recent system-identification framework for actuator dynamics, we extend it with an augmented friction model that incorporates the Stribeck effect to capture the low-velocity nonlinearities characteristic of high-gear-ratio actuators. The physical parameters are identified from real-robot trajectory data using an evolutionary algorithm, and the resulting simulation is used to train a locomotion policy that is transferred zero-shot to a 55 kg quadruped without additional fine-tuning or base- or controller-level dynamics randomization. On our platform, adding the Stribeck term lowers the actuator identification error by 16% relative to a Coulomb–Viscous model on the trajectory used for identification, and this advantage generalizes to an unseen trajectory not used for identification. It also lowers the simulation-to-reality mean-velocity degradation from 40.2% and 26.8% for the Coulomb–Viscous model to 27.2% and 21.6% for our method at the 0.3 and 1.0 m/s commands, respectively. The trained policy achieves stable locomotion on flat ground as well as rough terrain including steps and stairs. These results indicate that explicitly modeling low-velocity friction is beneficial for high-fidelity sim-to-real transfer in high-reduction systems. Full article

To enhance renewable energy accommodation and operational flexibility under high renewable energy penetration, this study proposes a multi-objective optimal capacity configuration method for an electricity–heat–hydrogen integrated energy system incorporating a reversible solid oxide cell (RSOC). First, considering the bidirectional electricity–hydrogen conversion capability and waste heat recovery of the RSOC, an electricity–heat–hydrogen multi-energy complementary system is constructed, and efficiency correction models are established for key energy conversion devices to characterize their part-load characteristics. Second, representative source–load scenarios are generated using Latin hypercube sampling and K-means clustering, and a multi-objective optimal capacity configuration model is formulated to minimize the annualized total cost and the wind and photovoltaic power curtailment rate. Finally, given the limitations of the non-dominated sorting genetic algorithm II (NSGA-II) in complex capacity configuration problems, such as premature convergence to local optima and insufficient population diversity, an adaptive crossover and mutation mechanism, a local search strategy, and a dynamic selection mechanism based on comprehensive crowding distance are introduced to improve its optimization performance. A balanced configuration scheme is then selected based on the knee point of the Pareto front obtained by the algorithm. Case-study results show that the Pareto solution set obtained by the improved NSGA-II (INSGA-II) has better overall quality than those obtained by NSGA-II and multi-objective particle swarm optimization (MOPSO). The resulting balanced configuration scheme has an annualized total cost of CNY 422.9 million and a wind and photovoltaic curtailment rate of 2.797%. The proposed method effectively coordinates system economic performance and renewable energy accommodation, enhances the coordinated utilization of electricity, heat, and hydrogen energy flows, and provides a reference for capacity planning of integrated energy systems under high renewable energy penetration. Full article

With the large-scale integration of Electric Vehicles (EVs) into distribution systems, the spatiotemporal uncertainty of charging loads and the interplay between user charging behavior and network operational constraints present new challenges to the safe and economical operation of the power system. To address the insufficient coordination among flexibility characterization, distributed optimization, and user-side responses, this paper proposes a closed-loop collaborative dispatch strategy. The strategy integrates flexibility aggregation, endogenous dynamic pricing, and user charging-station selection behavior. Firstly, a three-tier collaborative architecture comprising the Distribution System Operator (DSO), Electric Vehicle Aggregators (EVAs), and EV users is established, with rolling updates implemented using Model Predictive Control (MPC). A flexible aggregation model is developed based on set operations of vehicle-level constraints, dynamically calculating power boundaries and energy feasibility domains. Furthermore, a distributed coordinated optimization model between the DSO and multiple EVAs is established and solved via the Alternating Direction Method of Multipliers (ADMM) under privacy-preserving conditions. By analyzing the correlation between ADMM dual variables and the marginal value of network constraints, a Distribution Locational Marginal Pricing (DLMP) -inspired dynamic price signal—endogenous to the optimization—is constructed to guide spatial reallocation of charging loads. Joint simulations based on an IEEE 33-node distribution network and the Sioux Falls transport network demonstrate that the proposed strategy reduces 24 h network losses from 9.05 MWh (uncoordinated) to 8.41 MWh, lowers user total costs from 22,200 yuan to 7100 yuan, and eliminates voltage limit violations (duration reduced from 1.50 h to 0), while exhibiting good distributed solution performance and closed-loop control capability. Full article