Search Results (717)

A real-time optimized model predictive control–equivalent consumption minimization strategy (MPC-ECMS) is proposed for the energy management of shipboard gas turbine–photovoltaic hybrid energy storage (GT-PV-HESS) power systems. Different from conventional MPC-ECMS methods that only adopt single-level SOC-based feedback regulation, the strategy aims to overcome the limitations of conventional methods, including the poor adaptability of rule-based strategies and the lack of foresight in traditional ECMS, which cannot achieve simultaneous improvements in fuel economy, generation efficiency, and battery lifespan while maintaining system stability under dynamic operating conditions. The proposed strategy integrates the forward-looking optimization ability of MPC and the real-time decision-making advantage of ECMS. MPC is used to predict short-term load and photovoltaic power and identify operating modes, and a two-level equivalent factor adjustment mechanism is designed based on predicted conditions and battery state of charge (SOC). The optimized factor is applied in ECMS to achieve optimal power allocation between the gas turbine and battery under system constraints, while the supercapacitor implements power secondary correction to suppress bus voltage fluctuations caused by gas turbine operation. The architectural novelty lies in the two-level coordination mechanism and the marine-oriented hybrid energy storage cooperation. Simulation studies are conducted on the MATLAB/Simulink R2021b platform, and the results validate that it yields superior performance to the rule-based control and traditional ECMS under typical ship operating conditions. It increases gas turbine efficiency to 15.62% (0.47% and 6.24% higher than the two conventional methods). Over the 120 s simulation period, the proposed strategy reduces total fuel consumption to 1.049 kg, which is lower than 1.054 kg for the rule-based strategy and 1.192 kg for conventional ECMS. The battery SOC fluctuation is restricted to only 3.89%. The maximum DC bus voltage fluctuation rate is controlled within 3.28%, which meets the stability requirements of shipboard DC microgrids. The proposed strategy achieves a comprehensive and superior balance among fuel economy, power generation efficiency, and battery life while ensuring stable system operation under all working conditions. This two-level MPC-ECMS framework provides a high-performance and practically feasible energy management solution for shipboard hybrid power systems. Full article

To address the limitations of traditional energy management strategies in fuel cell hybrid power systems—specifically their difficulty in simultaneously accommodating dynamic driving condition adaptability, hydrogen fuel economy, and energy storage system stability—this study proposes a power distribution optimization strategy based on Deep Deterministic Policy Gradient (DDPG). The strategy targets a hybrid powertrain architecture dominated by a fuel cell (FC) and assisted by a lithium battery and a supercapacitor. By constructing a multi-dimensional state space that integrates vehicle speed, acceleration, the state of charge (SOC) of the energy storage system, and load power demand, a multi-objective reward function encompassing hydrogen consumption, SOC deviation, system efficiency, and power fluctuation is designed to achieve dynamic power allocation in a continuous action space. Simulation studies are conducted under three typical driving cycles—WLTP, CLTC-P, and UDDS—with comparative evaluations against the conventional Equivalent Consumption Minimization Strategy (ECMS) and Deep Q-Network (DQN)-based strategies. The results demonstrate that the DDPG-based strategy reduces hydrogen consumption to 607.1 g/100 km, 580.2 g/100 km, and 560.0 g/100 km under the three driving cycles, respectively, achieving a maximum reduction of 28% compared with ECMS. The average system efficiency increases to 64–66%, representing an improvement of 38.9%, while the operating proportion of the fuel cell within the high-efficiency region (40–80% load) increases by 15%. In addition, the strategy exploits the high-frequency response capability of the supercapacitor to smooth instantaneous power fluctuations, effectively reducing the inefficient start–stop events of the fuel cell. Although the SOC fluctuation range of the lithium battery increases by 32.5% compared with ECMS, a dynamic balance between energy efficiency and battery lifespan can be achieved through optimized weighting of the SOC deviation penalty term in the reward function. Overall, this study provides a solution with both theoretical significance and engineering feasibility for global energy optimization of fuel cell–energy storage systems under complex driving conditions. Full article

Photovoltaic (PV) power generation is inherently intermittent due to unpredictable irradiance variations, posing significant challenges for grid integration. While conventional power smoothing strategies mitigate short-term fluctuations, they do not explicitly enforce the tracking of a scheduled power trajectory. This paper proposes a dispatchable PV framework that integrates a hybrid convolutional neural network-long short-term memory (CNN-LSTM) model for precise day-ahead power forecasting with a real-time supercapacitor (SC) compensation strategy. The CNN-LSTM network captures complex spatiotemporal meteorological dependencies to generate a robust day-ahead reference trajectory. Concurrently, a supercapacitor energy storage system (SC-ESS) integrated at the DC-link level via a bidirectional buck–boost converter actively balances the instantaneous mismatch between this forecast trajectory and the actual PV generation. Unlike filter-based hybrid methods, the SC-ESS is employed as a direct forecast error actuator in a closed-loop control scheme. This strategy strictly enforces real-time forecast tracking while preserving maximum power point tracking (MPPT) and DC-link voltage stability. Simulations and laboratory experiments under rapidly varying irradiance confirm that the proposed method significantly reduces power deviations from the forecast reference and improves short-term power predictability without imposing excessive stress on the SC. This forecast-aware strategy effectively enhances the dispatchability of PV systems, providing a practical solution for grid-supportive operation. Full article

The increasing accumulation of poly(ethylene terephthalate) (PET) waste poses a significant environmental challenge and highlights the need for sustainable, value-added recycling strategies. In this study, porous carbon derived from PET was synthesized via carbonization and chemical activation and subsequently combined with manganese dioxide (MnO₂) to fabricate hybrid electrodes for aqueous supercapacitors. The PET-derived carbon exhibits a highly microporous structure with a large specific surface area and functions as a conductive and mechanically stable matrix that improves MnO₂ dispersion, charge transport, and electrochemical utilization. Systematic electrochemical investigations reveal strongly electrolyte-dependent charge-storage behavior. In an alkaline electrolyte, the capacitance is dominated by MnO₂ pseudocapacitive redox reactions, whereas in a neutral electrolyte, the response is primarily governed by electric double-layer charge storage. In a ferricyanide-containing redox-active electrolyte, additional electrolyte-mediated faradaic processes significantly enhance the apparent electrochemical performance. Under these conditions, the hybrid electrodes deliver a high apparent specific capacitance of 240–250 F g⁻¹ at moderate current densities. The electrodes further demonstrate stable cycling behavior and high apparent Coulombic efficiency, reflecting time-dependent utilization of both MnO₂ pseudocapacitance and redox-active electrolyte species during charge–discharge. Crucially, this work demonstrates that PET-derived carbon/MnO₂ hybrid electrodes exhibit complex, electrolyte-controlled charge-storage mechanisms and underscores the critical role of electrolyte selection in accurately interpreting electrochemical metrics and optimizing the performance of sustainable supercapacitors based on recycled polymer-derived carbons. Full article

The growing use of electrochemical batteries in renewable energy systems has intensified the need for storage architectures that can sustain power delivery while limiting transient electrical stress and voltage instability challenges. This study addresses the research gap in experimentally establishing a physically interpretable framework that links battery-centered hybrid storage behavior at the DC bus to AC-side inverter performance under load and source disturbances. A laboratory-scale renewable microgrid integrating photovoltaic and wind generation, programmable load variation, inverter-based AC delivery, and hybrid battery–supercapacitor storage is experimentally implemented and evaluated against a battery-only baseline, supported by a unified analytical framework that quantifies how transient buffering improvements propagate through the power conversion chain. The results show that the hybrid configuration reduces DC-bus voltage droop from about 1.1 V to 0.6 V under heavy-load transitions, and from approximately 0.85 V to 0.44 V during source-side variability (e.g., photovoltaic and wind turbine variations). The hybrid system also improves AC-side behavior, yielding unified stabilization indices of 103.03% for the root-mean-square voltage and 79.51% for the peak-to-peak voltage. These findings demonstrate that the experimentally implemented lab-scale renewable microgrid with hybrid battery–supercapacitor storage provides an effective pathway for improving battery-supported microgrid stability, waveform quality, and transient resilience. Full article

This paper proposes a low weight hybrid battery–supercapacitor energy storage system interfaced with bidirectional DC/DC converters with high power/current capability for aircraft applications. The supercapacitor converter having high power uses two pairs of interleaved coupled inductors to reduce the overall current ripple whilst increasing the converter’s power density. Due to the sensitive performance to saturation of the coupled inductors, a phase current balancing strategy is proposed to counter the effect current imbalance in the channels that would cause saturation degrading overall performance. A power management strategy (PMS) is implemented along with a low pass filter to separate the supercapacitor high frequency power component reference from the battery low frequency power component; therefore, separating the energy and power requirement for the energy storage system contributing to minimizing its weight whilst ensuring the current/power stresses are correctly handled. The validity of the system design is validated by a series of transient tests is conducted both in a simulation model as well as experimentally. Full article

The rapid demand for sustainable and efficient energy storage solutions has prompted the pursuit of eco-friendly electrode materials. Biomass-derived carbons from food waste offer a promising pathway to meet this need by combining waste valorization, environmental benefits, and high electrochemical performance. This review highlights that food waste biomass is an effective and inexpensive source of precursors for producing high-performance carbon materials for supercapacitors. Food waste, which includes fruit peels and vegetable residues, cereal husks, and oilseed residues, is a good source of lignocellulosic components, heteroatoms, and structural features that determine the electrochemical characteristics of the derived carbons. These wastes produce hierarchically porous carbons with high surface areas (>1500 m² g⁻¹) on pyrolysis and activation that provide superior ion transport, wettability and pseudocapacitive behaviour. Their electrochemical performance includes capacitances up to 520 F g⁻¹ and energy densities of 35–70 Wh kg⁻¹ in optimized systems, particularly under extended voltage windows or in hybrid supercapacitor configurations, and high cycling stability is equal to or even better than traditional carbons such as activated carbon and graphene. Additionally, biomass valorization contributes to a high level of greenhouse gas capture, decreases landfill, and correlates with the idea of a circular economy. The commercialization potential of biomass-based supercapacitors is supported by recent developments in AI-based optimization, combined with scalable synthesis methods, which would support ecologically, economically, and technologically sustainable energy storage on a large scale. Full article

With the large-scale integration of wind power, modern power systems are facing reduced equivalent inertia, weakened primary frequency regulation capability, and insufficient coordination between wind turbines and energy storage during joint frequency support. To address these issues, this paper investigates a wind–storage hybrid system composed of doubly fed induction generators (DFIG) and supercapacitor energy storage and proposes a coordinated primary frequency regulation strategy combining fuzzy logic control (FLC) and model predictive control (MPC). Considering the variations in rotor kinetic energy reserve and frequency support capability under different wind speed regions, a coordinated regulation mechanism is developed for multiple operating conditions. In addition, a variable-coefficient synthetic inertia control scheme with rotor speed safety constraints is designed to adaptively adjust the turbine regulation coefficients, while an SOC-feedback-based adaptive virtual droop strategy is introduced to improve the sustained support capability of the energy storage unit. On this basis, a multi-objective model predictive control framework is established to optimize the reference power allocation between the wind turbine and the energy storage unit in a rolling manner. The proposed method is characterized by three coordinated features, namely, multi-region wind–storage frequency regulation, rotor-speed-safe adaptive support of the wind turbine and SOC-aware adaptive support of the storage unit, as well as MPC-based rolling power allocation. Simulation results show that the proposed strategy improves the frequency nadir, reduces the steady-state frequency deviation, and enhances coordinated power sharing, thereby improving the primary frequency regulation performance and overall frequency stability of the wind–storage hybrid system. Full article

The demand for flexible and wearable electronics has intensified the need for conformable, high-performance, and self-sustaining power sources. Flexible supercapacitors (FSCs) and flexible batteries (e.g., lithium-ion and lithium–sulfur) are promising owing to their high-power density, long cycle life, and mechanical flexibility. A transformative solution lies in integrating these storage devices with mechanical energy harvesters, particularly triboelectric nanogenerators (TENGs), to create autonomous self-charging power systems (SCPSs). TENGs exhibit high output, versatile operational modes, material flexibility, and efficient energy harvesting from body movements. This review provides an overview of the recent advances in flexible energy storage technologies, encompassing carbon-based materials, MXenes, polymers, metal oxides, metal–organic frameworks (MOFs), and their hybrid architectures. It discusses the synergistic integration of these storage devices with TENGs to realize multifunctional SCPSs. It also highlights the fundamental design principles of flexible devices, the critical interplay of materials and architecture, and the journey towards monolithic system integration. The review also underscores the importance of managing harvesters’ pulsed output for efficient storage. Finally, a critical analysis of the challenges, including the energy density–flexibility compromise, environmental stability, and safety, is presented, alongside a forward-looking perspective on commercialization pathways for these technologies to power the next generation of autonomous wearable and sustainable electronic systems. Full article

Islanded photovoltaic microgrids with limited inertial support can undergo steep frequency excursions after sudden generation loss or abrupt load changes. This paper develops and evaluates a synthetic inertia strategy supported by a supercapacitor energy storage unit for fast frequency containment in this type of system. The proposed approach commands rapid active-power injection or absorption from the measured rate of change of frequency, thereby emulating the immediate inertial contribution usually associated with rotating machines while preserving a simple and physically interpretable control structure. The supercapacitor is represented through a resistance–capacitance model that includes equivalent series resistance and is interfaced through a bidirectional buck–boost power converter subject to practical current, voltage, and power limits. Rather than claiming a fundamentally new storage-support concept, the contribution of this paper lies in providing a transparent and constraint-consistent benchmark that integrates measured operating profiles, explicit supercapacitor limits, hybrid frequency–RoCoF support, and stress-aware comparative assessment under a common set of plant assumptions. The methodology is assessed in time-domain simulations under representative benchmark disturbances, including an approximately ten percent photovoltaic generation loss, a ten percent load increase, and a combined event. Performance is evaluated through the peak rate of change of frequency, frequency nadir, integral error indices, time outside the admissible band, and supercapacitor stress indicators such as current peaks, voltage depletion, and energy throughput. An additional non-ideal assessment is also included to examine the behavior of the RoCoF-based support law under bounded frequency-measurement perturbations and delayed control action. A complementary variability-driven case based on a highly fluctuating measured irradiance window is also used to examine the behavior of the adaptive energy-management mechanism under repeated photovoltaic-power variations. A local small-signal analysis is also included to show that the selected gain region is dynamically plausible in the unsaturated regime. The results show that the proposed adaptive hybrid strategy improves the overall frequency response while maintaining admissible supercapacitor operation, thus providing a stronger methodological basis for rapid frequency support in islanded photovoltaic microgrids. Full article

This study presents a real-world performance assessment of a 48 V mild-hybrid urban bus equipped with a crankshaft starter–generator (CSG, denoted as KSG in German terminology), together with model-based validation for KSG Active operation. The 17.8-ton Euro VI test vehicle uses a 160 F supercapacitor module operated within a 38–52 V DC/DC converter voltage window (≈40 Wh usable) to buffer transient high-power events in stop-and-go duty. A controlled A/B comparison (KSG Active vs. KSG Passive) was performed using repeated 0–50–0 km/h launch cycles (15 test cycles per mode). Vehicle CAN signals were recorded using a datalogger and analyzed in Vector vSignalyzer 19.0. Field results show a 17.1% reduction in fuel consumption (32.21 to 26.70 L/100 km) and a 30.4% reduction in time-averaged ICE power demand (58.90 to 40.99 kW). A MATLAB/Simulink R2020a longitudinal dynamics digital twin was developed and validated for the KSG Active mode only against 20 Hz CAN measurements, achieving NRMSE below 5% for key variables. The findings should be interpreted as a controlled same-vehicle comparison under repeatable test-track conditions rather than as a certification-grade fleet-level benchmark. Full article

Bio-based, biodegradable, and renewable polymers offer a promising alternative to traditional synthetic polymers derived from petroleum or other non-renewable resources. However, their use is limited by suboptimal properties and high costs. Incorporating sustainable reinforcements into the polymer matrix significantly improves biopolymer performance while preserving key properties, sustainability, and cost-effectiveness. Bio-based polymeric composites have emerged as a crucial category of biopolymers, playing a key role in advancing a sustainable, circular economy. This review provides an updated overview of bio-based polymer composites and nanocomposites, focusing on reinforcement strategies using natural nanofillers and engineered nanoparticles. We summarize key synthesis and processing methods, discuss structure–property relationships, and highlight recent advances in applications such as food packaging, biomedical devices, energy systems, environmental remediation, 3D printing, and supercapacitors. Polymer nanocomposites are versatile, with their performance depending on the type, size, and interactions between the fillers and the polymer matrix. Progress in metallic, ceramic, carbon-based, natural, and hybrid fillers has improved their properties. Using bio-based polymers and renewable fillers supports sustainability. Natural nanofillers derived from renewable sources and industrial byproducts offer a sustainable approach to developing high-performance, biodegradable nanocomposites. Smart nanocomposites can react to external stimuli by integrating specialized fillers that enhance their mechanical and mobility properties. Shape memory nanocomposites can be remotely activated—using heat, electricity, magnets, or light—enabling advanced applications. Finally, we address major challenges and outline future directions for scalable, circular-material solutions, drawing on perspectives from the circular economy and life cycle assessment (LCA). Full article

With the integration of renewable energy into traction power supply systems at a high proportion and penetration level, the intermittency and randomness of renewable energy output significantly intensify the fluctuation characteristics of traction loads, posing severe challenges to the stable operation and precise dispatch of the system. To effectively address the dynamic tracking and anti-disturbance issues arising from the dual uncertainties of source and load, this paper proposes a dual-timescale two-layer optimization dispatch strategy based on Model Predictive Control (MPC). In the upper-layer optimization, with the objective of optimal system economic operation, a multi-step rolling optimization method is adopted to formulate a long-timescale baseline dispatch plan, fully considering the temporal correlation of photovoltaic and wind power outputs and the periodic characteristics of traction loads. In the lower-layer optimization, aimed at smoothing power fluctuations and correcting prediction deviations, the technical advantages of supercapacitors—high power density and fast response—are utilized to perform real-time tracking and dynamic compensation of the upper-layer baseline plan. This effectively reduces the impact of prediction errors on control accuracy, achieves smooth control of tie-line power, and enhances overall system stability. Case study results based on an actual railway traction power supply system demonstrate that the proposed method can fully leverage the coordinated and complementary characteristics of the hybrid energy storage system, effectively suppress power fluctuations from renewable energy output and traction loads, and achieve economic operation objectives while ensuring system disturbance rejection performance, thereby validating the effectiveness and practicality of the strategy. Full article

Wind–solar coupled hydrogen production DC microgrids have significant potential for improving renewable energy utilization and reducing the cost of hydrogen production. However, the randomness of wind–solar power causes frequent electrolyzer start–stop operations, accelerating lifetime degradation, while a single energy storage system cannot simultaneously suppress power fluctuations and regulate energy. Therefore, this study proposes a two-stage day-ahead energy scheduling optimization framework. A DBSCAN–K-means hybrid clustering method generates representative wind–solar power scenarios. A supercapacitor-based strategy mitigates high-frequency power fluctuations using empirical mode decomposition. Furthermore, a dual-scenario-driven electrolyzer scheduling strategy adapted to different wind–solar output conditions is developed, where power allocation is determined by battery state-of-charge and electrolyzer operating states, enabling stepwise power compensation and dynamic operating-state optimization. Case studies comparing wind–solar-only supply, a conventional strategy, and the proposed strategy demonstrate that the proposed strategy balances hydrogen production and economic objectives, and reduces annual electrolyzer start–stop cycles by 73%, thereby prolonging electrolyzer lifetime. Furthermore, the proposed framework enhances renewable energy utilization, reduces curtailment, and lowers lifecycle costs, thereby contributing to the development of sustainable hydrogen production systems. Full article

In the era of renewable dominated grids, integration of dynamic loads such as EV charging stations have increased the operational challenges in multifolds, particularly in DC microgrids (DC MGs). Traditional battery-dominated grid energy management strategies (EMSs) are often not capable of handling fast transients due to the limitations of battery electrochemistry. To overcome this limitation, a hierarchical hybrid energy management strategy is proposed that uses the combination of data-driven and metaheuristic algorithms. The designed optimization framework consists of particle swarm optimization (PSO) and a neural network (NN) implemented in the central controller of a 4-bus ringmain DC MG. An efficient decoupling of fast and slow storage dynamics is performed, where the supercapacitor (SC) is optimized using the NN and the battery is optimized using PSO. This selective optimization reduces the computational overhead on the PSO making it more feasible for real-time implementation. The designed hybrid PSO-Neural EMS framework is initially designed on MATLAB and further validated on a real-time hardware setup. Robustness of the control scheme is verified with various case studies, such as renewable intermittency, dynamic loading and partial shading scenarios. An effective optimization of the SC in both transient and heavy load scenarios are observed. LabVIEW interfacing is used for MODBUS-based interaction with PV emulators and DC-DC converters. Full article