Search Results (124)

This paper proposes a Quantum-Machine-Learning-enhanced Double Deep Q-Network (QML-DDQN) for the supervisory control of battery–supercapacitor hybrid energy storage systems in islanded microgrids. This method combines a variational quantum circuit as a nonlinear state encoder with a DDQN decision layer for safe discrete dispatch. Three representative islanded cases, Island 1, Island 2, and Island 3, were used to evaluate the robustness under different scales, renewable profiles, and reliability requirements. Compared with deterministic optimization, predictive control, metaheuristics, and classical reinforcement-learning baselines, the proposed controller delivers the best overall trade-off among operating cost, renewable utilization, diesel reduction, and loss-of-power-supply risk. On the three-case averages, QML-DDQN reduces daily cost and LPSP by 0.99% and 4.04% relative to DDQN, by 2.91% and 7.32% relative to DQN, and by 9.09% and 16.63% relative to MILP; it also lowers curtailment and diesel share by up to 13.02% and 9.09%, respectively, across the same benchmark sets. The largest gains appear under volatility-dominated and stress-scenario conditions, where the quantum encoder strengthens the state representation, and the DDQN backbone mitigates value overestimation. These results highlight the practical advantages of the QML-DDQN as a resilient and high-value supervisory strategy for islanded hybrid energy storage operations. Full article

Developing sustainable lithium-ion batteries (LIBs) requires binder–carbon interfaces that combine mechanical compatibility, interfacial cohesion, and reduced environmental impact. In this work, density functional theory calculations were used to evaluate the interactions of representative binder monomers acrylonitrile (AN), pyrrole (PY), vinylidene fluoride (VDF), and tetrafluoroethylene (TFE) with pristine graphene and chitosan-functionalized graphene oxide (GO/chitosan). Structural, energetic, electronic, and topological features were analyzed using counterpoise-corrected interaction energies, frontier-orbital descriptors, molecular electrostatic potential maps, projected density of states, noncovalent interaction analysis, and quantum theory of atoms in molecules topology. Final interaction energies were obtained at the M06-2X/def2-TZVP level with Boys–Bernardi counterpoise correction to provide a more robust description of weak noncovalent adsorption. Most binder–surface interactions fall within a weak, near-thermoneutral adsorption regime. On pristine graphene, AN and PY exhibit weakly favorable adsorption, with minimum counterpoise-corrected interaction energies of −3.13 and −2.10 kcal mol⁻¹, respectively, whereas TFE and VDF show orientation-dependent, near-neutral behavior. GO/chitosan introduces oxygen-containing and amino functionalities that modify the adsorption balance, particularly for selected perpendicular configurations of fluorinated monomers, although the net stabilization remains modest. NCI, QTAIM, MEP, and PDOS analyses indicate that surface functionalization increases the chemical heterogeneity and directionality of local contacts; however, these local descriptors do not necessarily translate into strong global adsorption energies. Overall, the results identify GO/chitosan as a chemically tunable interface for binder–carbon compatibility in LIB electrodes and demonstrate the importance of triple-ζ, counterpoise-corrected calculations for evaluating weak binder–surface interactions. Full article

To address the high dependence on diesel generation, renewable energy variability, and limited demand-side flexibility of remote islanded microgrids, this study develops a low-carbon economic dispatch framework for an islanded photovoltaic–wind–diesel–battery energy storage system with coordinated demand response. The proposed model minimizes the operating cost, pollutant treatment cost, and load-loss penalty cost while satisfying generation-output, battery state-of-charge, charging and discharging, demand-response, and islanded power-balance constraints. To solve the resulting high-dimensional, nonlinear, and strongly constrained optimization problem, a rotation quantum particle swarm optimization algorithm (RQPSO) is proposed. In contrast to the conventional velocity–position update, RQPSO independently encodes each decision variable using a full-dimensional quantum phase representation and performs the search through a shortest-path rotation-guided phase-updating mechanism. Adaptive angular mutation, elite local refinement, and stagnation-aware restart are further incorporated to balance global exploration, local exploitation, and convergence stability. The algorithm is evaluated using nine 30-dimensional benchmark functions and representative 24 h forecasted load and renewable-generation profiles for Island data. Under the reliability-priority scheduling scheme, RQPSO achieves a total scheduling cost of 69,017.69 CNY, diesel fuel consumption of 6636.20 kg, and estimated CO₂ emissions of 18,332.49 kg. Compared with conventional PSO, these three indicators are reduced by 9.34%, 12.25%, and 12.25%, respectively. RQPSO also reduces the total cost by 6.16–27.36% relative to six comparison algorithms. The results demonstrate that the coordination of demand response and battery storage can improve peak–valley regulation, reduce diesel dependence and emissions, and maintain feasible and economical operation under different renewable-generation conditions. Full article

Background: With the deepening coupling of multi-microgrids (MMGs) and transportation systems in smart cities, maintaining frequency stability under extreme conditions increasingly relies on vehicle-to-grid (V2G) flexibility. However, existing V2G dispatch strategies often overlook the noticeable battery degradation caused by high-frequency regulation and the vulnerability of extensive communication networks to false data injection attacks (FDIAs), while the high-dimensional coordination of EV routing and discharging makes classical algorithms struggle to converge. Methods: To address these challenges, this study proposes a quantum-empowered degradation-aware V2G coordination framework for smart-city MMGs considering communication security and user travel demands. At the physical layer, an equivalent RC circuit-based battery degradation model and a traffic flow model are established to quantify capacity loss and travel delays. At the cyber layer, quantum key distribution (QKD) ensures unconditionally secure communication, while a quantum reinforcement learning (QRL) algorithm is developed to achieve fast convergence in high-dimensional multi-objective optimization. Results: Simulation results demonstrate that the proposed framework completely immunizes the system against FDIAs, effectively suppresses frequency fluctuations, and significantly reduces battery degradation costs while preserving user mobility. Conclusions: This framework provides a highly secure and user-friendly pathway for resilient smart-city frequency regulation. Full article

Rechargeable zinc–air batteries (ZABs) are attractive energy storage systems owing to their high theoretical energy density, intrinsic safety, and low cost. Yet, their practical deployment is hindered by parasitic hydrogen evolution reaction (HER), zinc corrosion, and poor interfacial stability in alkaline electrolytes. Herein, biomass-derived carbon quantum dots (CQDs) synthesised from lemon peel waste via a hydrothermal route were employed as multifunctional electrolyte additives to regulate the zinc/electrolyte interface and mitigate these challenges. The CQDs exhibited oxygen-rich surface functionalities and quasi-spherical nanoscale morphology, enabling stable dispersion in 6 M KOH. Electrolyte modification with CQDs significantly altered the physicochemical properties of the electrolyte, increasing the zeta potential from −28.2 to +48.5 mV while maintaining high ionic conductivity. Electrochemical studies demonstrated progressive suppression of HER, evidenced by a shift in HER onset potential from 146 to 291 mV, an increase in overpotential at 10 mA cm⁻² from 398 to 477 mV, and an increase in Tafel slope from 82 to 130 mV dec⁻¹. Corrosion studies revealed enhanced zinc stability, with the charge transfer resistance increasing from 1.35 to 3.80 Ω and a maximum corrosion inhibition efficiency of 64.47% achieved at an optimal CQD loading of 1.0 mg. Furthermore, the CQD-modified electrolyte improved the average operating power density of the ZAB from approximately 4.5 to 5.5 mW cm⁻² and reduced charge–discharge polarisation during cycling. The enhanced performance is attributed to a combination of surface-controlled and transport-related processes, whereby oxygen-functionalized CQDs modify the electrical double layer, retard HER kinetics, and inhibit zinc corrosion. This work demonstrates a sustainable electrolyte engineering strategy for improving the durability and electrochemical performance of ZABs using biomass-derived carbon quantum dots. Full article

Solid-state batteries (SSBs) are emerging as a transformative alternative to conventional lithium-ion batteries (LIBs) for next-generation electric vehicles (EVs) by replacing flammable liquid electrolytes with solid-state materials. Compared with current LIB systems delivering approximately 160–300 Wh/kg at the pack level, SSBs are projected to achieve 400–800 Wh/kg, enabling improvements in driving range of nearly 50–100% while simultaneously reducing battery pack mass by 10–30%. These improvements directly enhance vehicle-level energy efficiency by lowering energy consumption from typical values of 150–180 Wh/km in present EVs to projected levels of 110–140 Wh/km in optimized SSB-based architectures. Furthermore, reduced internal resistance and improved electrochemical stability can increase round-trip efficiency from approximately 85–95% in conventional LIBs to values approaching 95–98% under optimized solid-state configurations. The enhanced thermal stability of solid electrolytes significantly reduces the need for active cooling systems, decreasing parasitic thermal-management energy consumption from 10–30% of total vehicle energy demand to below 5–15% in advanced SSB systems. Fast-charging capability is also substantially improved, with projected charging times decreasing from 20–40 min to approximately 10–15 min for 10–80% state-of-charge operation, while maintaining improved safety and reduced risk of thermal runaway. In addition, SSBs demonstrate projected cycle lifetimes exceeding 3000–5000 cycles, compared with 1000–2000 cycles for conventional LIBs, thereby lowering battery replacement frequency and lifecycle energy losses. This paper examines the electrochemical fundamentals, thermal behavior, charging/discharging efficiency, and vehicle-level implications of SSB technology for EV applications. Comparative analyses demonstrate that replacing LIBs with SSBs can increase EV driving range from approximately 400 km to 700–800+ km under equivalent battery mass conditions, while also improving coulombic efficiency beyond 99.5% and reducing self-discharge rates to below 1–2% per month. Current industrial case studies from Toyota, Factorial Energy, Mercedes-Benz, CATL, BYD, QuantumScape, and Samsung SDI further confirm accelerating commercialization pathways toward 2027–2030. Overall, the study demonstrates that SSBs are not merely incremental battery improvements but represent a system-level efficiency technology capable of simultaneously enhancing energy density, reducing thermal and electrical losses, extending vehicle range, accelerating charging, and improving long-term sustainability. Despite persistent challenges related to manufacturing scalability, interfacial resistance, and cost, SSBs are positioned to become a critical enabler of highly efficient, long-range, and safer electric mobility systems beyond 2030. Full article

Ultra-fast electric vehicle (EV) charging systems are among the most demanding converter-dominated applications due to their high power levels, wide battery-voltage range, strict thermal constraints, and the need for adaptive charging control. Conventional design and tuning approaches often rely on fixed control policies and computationally expensive iterative optimization, which limits their ability to address nonlinear multi-objective trade-offs across the full charging envelope. This paper proposes a hybrid AI–quantum co-design framework for a SiC-based dual active bridge (DAB) converter intended for ultra-fast EV charging applications. The proposed approach combines a physical converter model, an AI surrogate-learning layer for rapid prediction of converter performance, and a quantum-assisted optimization layer for multi-objective exploration of design and control variables. To demonstrate the framework, a representative modular 350 kW ultra-fast charging case study is considered, implemented by four parallel 87.5 kW SiC-based DAB modules and including converter-level optimization and adaptive charging-policy refinement. The revised manuscript introduces a complete system schematic, an explicit DAB converter topology, a clarified methodological workflow, and a simulation-based proof-of-concept evaluation. Representative results indicate improved design-space exploration and more balanced trade-offs between efficiency, thermal stress, ripple, and dynamic response compared with a conventional baseline tuning approach. Although the study does not claim hardware-level quantum advantage, it provides a structured and practically interpretable computational framework for intelligent co-design of high-power charging converters. Full article

To address the difficulty of efficiently removing calcium impurities from the manganese sulfate stripping solution obtained during the recycling of spent lithium batteries, this work proposed a binary synergistic extraction system. Quantum chemical calculations were used to screen the optimal combination (2A + 2B). The binding energy indicated the molecules combined with calcium are relatively more stable. Experimental optimization determined the optimal conditions as follows: 50 vol% of A, 25 vol% of B, saponification rate 60%, phase ratio (O/A) 2.5:1, and pH 6.0. In continuous extraction tank experiments, the calcium concentration decreased from 681 mg/L to 5 mg/L after a seven-stage counter-current extraction, with an extraction efficiency of about 99.3%. Infrared spectroscopy confirmed that the P=O double bond was the key functional group. This study provides an efficient and feasible technological pathway for the preparation of battery-grade manganese sulfate. Full article

The increasing electrification of urban transportation has formulated a tightly coupled energy-mobility nexus. Under extreme disaster events or grid faults, rapidly restoring power supply capacity and re-dispatching shared electric vehicle (EV) fleets are critical for enhancing system resilience. Existing co-optimization methods face the curse of dimensionality when dealing with high-dimensional discrete grid reconfigurations and continuous spatio-temporal EV queuing dynamics. While multi-agent deep reinforcement learning (MADRL) offers real-time responsiveness, it inherently struggles to satisfy strict physical constraints, frequently generating infeasible and unsafe actions. To bridge this gap, this paper proposes a heuristic-guided safe multi-agent reinforcement learning (Safe-MADRL) framework for the resilient dispatch of the energy-mobility nexus. Instead of relying solely on black-box neural networks, the framework structurally embeds physical models and heuristic solvers into the learning loop. A quantum particle swarm optimization (QPSO) algorithm acts as a heuristic action refiner to ensure that grid topology actions strictly comply with non-linear power flow and voltage constraints. Simultaneously, a mixed-integer linear programming (MILP) model coupled with a single-queue multi-server (SQMS) model serves as a safety projection layer. This layer mathematically guarantees EV battery energy continuity and accurately quantifies spatio-temporal queuing delays at charging stations. Case studies on a coupled IEEE 33-node distribution system and a regional transportation network demonstrate that the proposed Safe-MADRL framework achieves zero physical violations during training and significantly outperforms traditional mathematical optimization and pure learning-based methods in computational efficiency, system power loss reduction, and overall operational economy. Full article

Two series of N-hydroxyalkyl-1,8-naphthalimide derivatives were synthesized to investigate the influence of structural variables on their electrochemical and photophysical properties. The first series includes compounds containing N-hydroxyalkyl substituents of various chain lengths and branches. The second series includes derivatives functionalized in the naphthalene core with electron-donating or electron-withdrawing groups. Cyclic voltammetry, UV–Vis spectroscopy and fluorescence measurements were supported by theoretical DFT calculations. Branched hydroxyalkyl chains enhanced photoluminescence quantum yields by up to 19%, compared to less than 4% for linear chains. Functionalization of the naphthalene core at the C4 position strongly affected optical band gaps, electrochemical properties and photoluminescence quantum yields. DFT calculations revealed significant changes in the energies of the frontier orbits: the HOMO energy varied from −6.95 eV to −5.51 eV, while the LUMO energy varied from −3.24 eV to −1.94 eV. Preliminary tests have demonstrated the suitability of the selected derivatives as cathode materials in lithium-ion batteries, achieving an initial capacity of 47 mAh/g. Full article

The progressive miniaturization of devices devoted to energy manipulation and storage calls for extending thermodynamic concepts towards regimes where quantum effects become unavoidable. In this context, quantum thermodynamics provides the proper framework for understanding and exploiting non-classical effects for energy applications. Within this framework, we present a comprehensive review of the role played by spin systems as versatile platforms for quantum energy technologies, focusing on their dual role as Quantum Thermal Machines and Quantum Batteries. We discuss how the combination of discrete spectra, engineered interactions and long coherence times enables the realization of high-performance quantum devices. We then highlight how genuinely quantum features can be exploited to achieve performance beyond classical limits. Beyond theoretical developments, we review the rapid experimental progress across leading spin platforms, including nuclear magnetic resonance systems, trapped ions, nitrogen-vacancy centers in diamond and superconducting circuits, which are bringing quantum energy devices from conceptual proposals to actual realizations. By presenting a unified spin-based framework that integrates energy conversion and storage, this review outlines the foundations of the emerging field of quantum energy and identifies key challenges and future directions for scalable quantum energy technologies. Full article

Carbon-neutral industrial parks require large consumers, such as data centers, to balance low-carbon operation and service reliability. This paper proposes a resilience-constrained stochastic dispatch framework for a data-center virtual power plant (VPP) with renewable generation, short-duration batteries, and long-duration storage units. The dispatch is formulated as a two-stage stochastic program with normal and outage scenarios. To solve the resulting large mixed-integer problem, we develop a hybrid quantum–classical L-shaped method: the integer master is solved heuristically by quantum annealing, while scenario subproblems are solved exactly by classical optimization. In a case study based on real-world industrial-park data, the proposed storage strategy eliminates critical load shedding for the tested 6 h outage scenarios with a 3.7% increase in expected daily cost. The QA-driven method reaches the same best-known objective as the classical baseline with an empirical 1.36× runtime speedup. Full article

Accurate estimation of the state of charge (SOC) is essential for the safe and efficient operation of lithium-ion batteries. Conventional Adaptive Unscented Kalman Filter (AUKF) methods often exhibit limited accuracy, primarily due to the empirical selection of process and measurement noise covariance matrices. To overcome this limitation, this study proposes a QPSO-AUKF algorithm based on a second-order RC equivalent circuit model, which integrates Quantum-behaved Particle Swarm Optimization (QPSO) with online parameter identification. In this approach, the QPSO algorithm optimizes the noise covariance matrices, which are subsequently used within the AUKF framework for SOC estimation. MATLAB R2020a simulations conducted on the Maryland and Wisconsin datasets demonstrate that the QPSO-AUKF reduces the root mean square error (RMSE) by more than 60% compared with the conventional AUKF, indicating a significant improvement in SOC estimation accuracy. Full article

Multimodal forecasting is increasingly needed to maintain energy levels, storage capacity, and compute efficiency in off-grid, renewable-powered cloud environments. Variable sensor quality, uncertain interactions with renewable energy, and rapidly changing weather patterns make real-time forecasting difficult. Current transformer, GNN, and CNN systems suffer from sensor noise instability, multimodal temporal–spectral correlation issues, and challenges in the interpretability of operational decision-making. In this research, Q-RCANeX, a quantum-guided residual convolutional attention network for off-grid cloud infrastructures, estimates battery state of charge, renewable energy sources, and microgrid efficiency to overcome these restrictions. The system uses a Hybrid Quantum–Bayesian Evolutionary Optimizer, quantum feature embedding, temporal–spectral attention, residual convolutional encoding, and signal decomposition preprocessing. These parameters reinforce features, reduce noise, and align forecasting behavior with microgrid dynamics. Q-RCANeX obtains 98.6% accuracy, 0.992 AUC, and 0.986 R³ values for REAF, WGF, SOC-F, and EEIF forecasting tasks, according to a statistical study. Additionally, it determines inference latency to 4.9 ms and model size to 18.5 MB. Even with 20% of sensor data missing or noisy, the model outperforms 12 state-of-the-art baselines and maintains 96.8% accuracy using ANOVA, Wilcoxon, Nemenyi, and Holm tests. The findings indicate that the forecasting framework has high accuracy, clarity, and resilience to failures. This makes it useful for real-time, off-grid management of renewable cloud microgrids. Full article

The Variational Quantum Eigensolver (VQE) is a quantum algorithm for estimating ground-state energies, with promising applications in material science, drug discovery, and battery research. A key challenge is the limited number of qubits available on current quantum devices, which restricts the size of molecular systems that can be studied. To address this limitation, we apply the Fragment Molecular Orbital (FMO) method in combination with VQE, referred to as FMO-VQE. This approach divides a system into smaller fragments, making the quantum calculations more tractable. While earlier studies demonstrated this method only for hydrogen clusters, we extend the application to lithium cobalt oxide, a widely used cathode material in lithium-ion batteries. Using FMO-VQE, we estimate the ground-state energy of this complex system while reducing the number of required qubits from 24 to 14, without significant loss of accuracy compared to classical methods. This reduction highlights the potential of FMO-VQE to overcome hardware limitations and make quantum simulations of larger molecules feasible. The results suggest a practical path for applying near-term quantum computers to real-world challenges, opening opportunities for advancements in the battery industry and drug design. Full article