Search Results (1,947)

In complex energy storage operating scenarios, batteries seldom undergo complete charge–discharge cycles required for periodic capacity calibration. Methods based on accelerated aging experiments can indicate possible aging paths; however, due to uncertainties like changing operating conditions, environmental variations, and manufacturing inconsistencies, the degradation information obtained from such experiments may not be applicable to the entire lifecycle. To address this, we developed a stage-wise state-of-health (SOH) prediction approach that combined offline training with online updating. During the offline training phase, multiple single-cell experiments were conducted under various combinations of depth of discharge (DOD) and C-rate. Multi-dimensional health features (HFs) were extracted, and an accelerated aging probability $p_{AA}$ was defined. Based on the correlation statistics between HFs, $k_{HF}$, the SOH, and $p_{AA}$, all cells in the dataset were divided into general early, middle, and late aging stages. For each stage, cells were further classified by their longevity (long, medium, and short), and multiple models were trained offline for each category. The results show that models trained on cells following similar aging paths achieve significantly better performance than a model trained on all data combined. Meanwhile, HF optimization was performed via a three-step process: an initial screening based on expert knowledge, a second screening using Spearman correlation coefficients, and an automatic feature importance ranking using a random forest regression (RFR) model. The proposed method is innovative in the following ways: (1) The stage-wise multi-model strategy significantly improves the SOH prediction accuracy across the entire lifecycle, maintaining the mean absolute percentage error (MAPE) within 1%. (2) The improved model provides uncertainty quantification, issuing a warning signal at least 50 cycles before the onset of accelerated aging. (3) The analysis of feature importance from the model outputs allows the indirect identification of the primary aging mechanisms at different stages. (4) The model is robust against missing or low-quality HFs. If certain features cannot be obtained or are of poor quality, the prediction process does not fail. Full article

This paper addresses the critical challenge of multi-objective optimization in residential Home Energy Management Systems (HEMS) by proposing a novel framework based on an Improved Multi-Agent Proximal Policy Optimization (MAPPO) algorithm. The study specifically targets the low convergence efficiency of Multi-Agent Deep Reinforcement Learning (MADRL) for coupled Battery Energy Storage System (BESS) and Air Source Heat Pump (ASHP) operation. The framework synergistically integrates an action constraint projection mechanism with an economic-performance-driven dynamic learning rate modulation strategy, thereby significantly enhancing learning stability. Simulation results demonstrate that the algorithm improves training convergence speed by 35–45% compared to standard MAPPO. Economically, it delivers a cumulative cost reduction of 15.77% against rule-based baselines, outperforming both Independent Proximal Policy Optimization (IPPO) and standard MAPPO benchmarks. Furthermore, the method maximizes renewable energy utilization, achieving nearly 100% photovoltaic self-consumption under favorable conditions while ensuring robustness in extreme scenarios. Temporal analysis reveals the agents’ capacity for anticipatory decision-making, effectively learning correlations among generation, pricing, and demand to achieve seamless seasonal adaptability. These findings validate the superior performance of the proposed centralized training architecture, providing a robust solution for complex residential energy management. Full article

This work investigated the impact of vehicle-to-grid (V2G) cycling on the service life of lithium-ion cells, using real-world V2G data from commercial electric vehicle (EV) battery chargers. Commercially available cylindrical lithium-ion cells were subjected to long-term storage and V2G cycling under varying state of charge (SOC), depth of discharge (DOD), and temperature conditions. The ageing results demonstrate that elevated temperature (40 °C) is the dominant factor accelerating degradation, particularly at a high storage SOC (>80% SOC) and increased cycle depths (30–80% SOC, 30–95% SOC). A comparison between V2G cycling and calendar ageing over a similar storage period revealed that shallow V2G cycling (30–50% SOC) leads to comparable capacity fade to storage at a high SOC (≥80% SOC). The comparative analysis indicated that 62% of a full equivalent cycle (FEC) of V2G cycling can be achieved daily, without compromising the cell’s lifetime, demonstrating the viability of V2G adoption during EV idle/charging periods, which can offer potential operational benefits in terms of cost reduction and emissions savings. Furthermore, this work introduced the concept of a V2X capability metric as a novel cell-level specification, along with a corresponding experimental evaluation method. Full article

Metal oxide nanomaterials have emerged as transformative materials in the quest to enhance the energy density and overall performance of lithium-ion batteries (LIBs) and solid-state batteries (SSBs). Their unique properties—including their large surface areas and short ion diffusion pathways—make them ideal for next-generation energy storage technologies. In LIBs, the high surface-to-volume ratio of metal oxide nanomaterials significantly enlarges the active interfacial area and shortens the lithium-ion diffusion paths, leading to an improved high-rate performance and enhanced energy density. Transition metal oxides (TMOs) such as nickel oxide (NiO), copper oxide (CuO), and zinc oxide (ZnO) have demonstrated significant theoretical capacities, while binary systems like NiCuO offer further improvements in cycling stability and energy output. Additionally, layered lithium-based TMOs, particularly those incorporating nickel, cobalt, and manganese, have shown remarkable promise in achieving high specific capacities and long-term stability. The synergistic integration of metal oxides with carbon-based nanostructures, such as carbon nanotubes (CNTs), enhances the electrical conductivity and structural durability further, leading to a superior electrochemical performance in LIBs. In SSBs, the use of oxide-based solid electrolytes like garnet-type Li₇La₃Zr₂O₁₂ (LLZO) and sulfide-based electrolytes has facilitated the development of high-energy-density systems with excellent ionic conductivity and chemical stability. However, challenges such as high interfacial resistance at the electrode–electrolyte interface persist. Strategies like the application of lithium niobate (LiNbO₃) coatings have been employed to enhance interfacial stability and maintain electrochemical integrity. Furthermore, two-dimensional (2D) metal oxide nanomaterials, owing to their high active surface areas and rapid ion transport, have demonstrated considerable potential to boost the performance of SSBs. Despite these advancements, several challenges remain. Morphological optimization of nanomaterials, improved interface engineering to reduce the interfacial resistance, and solutions to address dendrite formation and mechanical degradation are critical to achieving the full potential of these materials. Full article

Alternative energies are essential for meeting the global demand for environmentally friendly energy, especially as the use of fossil fuels is being reduced. In recent years, largely due to diminishing fossil fuel reserves, Portugal has been actively promoting investment in renewable energies to reduce its reliance on energy imports and fossil fuels. However, despite the country’s high daily sunshine hours and utilization of wind and hydropower, energy production remains unstable due to climate variability. Climate instability leads to fluctuations in the energy supplied to the grid and can even partially withstand blackouts such as the one that occurred on 28 April 2025 on the Iberian Peninsula. To address this problem, energy storage systems are crucial to guarantee the stability of the supply during periods of low production or in situations such as the one mentioned above. This paper analyzes the feasibility of implementing an energy storage system to increase the profitability of a wind farm located in Alto Douro, Portugal. The study begins with a demand analysis, followed by simulations of the system’s performance in terms of profitability based on efficiency and power. Based on these assumptions, a modular lithium battery storage system with high efficiency and rapid charge/discharge capabilities was selected. This battery, with less autonomy but high capacity, is more profitable, since a 5% increase in efficiency results in high profits (€84,838) and curtailment (€70,962) using batteries with lower autonomy, i.e., 2 h (power rating of 5 MW combined with 10 MWh energy storage). Therefore, two scenarios (A and B) were considered, with one more optimistic (A) in which the priority is to discharge the batteries whenever possible. In the more realistic scenario (B), it is assumed that the batteries are fully charged before discharge. On the other hand, in the event of a blackout, it enables faster commissioning of the surrounding water installations, because solar and battery energy have no inertia, which facilitates the back start protocol. Full article

With the integrated development of new energy and oil and gas production, introducing wind–solar–storage microgrids in coalbed methane well screw pump discharge systems enhances the renewable energy proportion while promoting green development. However, the cyclical, volatile, and random characteristics of wind and photovoltaic generation create scheduling challenges, with insufficient green power consumption reducing renewable energy utilization efficiency and increasing grid dependence. This study establishes an operation scheduling optimization model for coalbed methane well screw pump discharge systems under wind–solar–storage microgrids, minimizing daily operation costs with screw pump rotational speed as decision variables. The model incorporates power constraints of generation units and production constraints of screw pumps, solved using particle swarm optimization. Results demonstrate that energy storage batteries effectively smooth wind and photovoltaic fluctuations, enhance regulation capabilities, and improve green power utilization while reducing grid purchases and system operation costs. At different coalbed methane extraction stages, the model optimally adjusts screw pump rotational speed according to renewable generation, ensuring high pump efficiency while minimizing operation costs, enhancing green power consumption capacity, and meeting daily drainage requirements. Full article

The increasing penetration of variable renewable energy sources has intensified the need for ancillary services to maintain grid stability, and demand-side flexibility, particularly through distributed energy systems (DESs), is expected to play an important role. This study proposes a two-stage optimization framework for DESs under CO₂ constraints that enables gas engines and battery energy storage systems (BESS) to provide regulating power equivalent to Load Frequency Control (LFC). The framework consists of an Equipment Sizing Optimization Model (ESM) and an Equipment Operation Optimization Model (EOM), both formulated as mixed-integer linear programming (MILP) models. The ESM determines equipment capacities using simplified operational representations, where partial-load efficiencies are approximated through linear programming (LP)-based constraints. The EOM incorporates detailed operational characteristics, including start-up/shutdown states and partial-load efficiencies, to perform daily scheduling. Information obtained from the ESM, such as the CO₂ emissions, the equipment capacities, and the BESS state of charge, is passed to the EOM to maintain consistency. A case study shows that providing regulating power reduces total system cost and that CO₂ reduction constraints alter the equipment mix. These findings demonstrate that the proposed framework offers a practical and computationally efficient approach for designing and operating DESs under CO₂ constraints. 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

Existing design methodologies for off-grid wind–solar–hydrogen integrated energy systems (WSH-IES) are typically case-specific and lack portability. This study aims to establish a unified design framework to enhance cross-scenario applicability while retaining case-specific adaptability. The proposed framework employs the superstructure concept, dividing the off-grid WSH-IES into three subsystems: energy production, conversion, and storage subsystems. The framework integrates equipment selection and capacity sizing into a unified optimization process described by a mixed-integer programming model. Additionally, the modular constraint template ensures generalizability across scenarios by linking the local resource protocol to the techno-economic parameters of the equipment, allowing the model to be adapted to various situations. The model was applied to two case studies. Economic analysis indicates that the pure electricity architecture is dominated by energy storage (battery costs account for 96.8%), while the hybrid architecture redistributes expenditures between batteries (67.8%) and electrolyzers (28.4%). It utilizes hydrogen as a complementary medium for long-duration energy storage, achieving cost risk diversification and enhanced resilience. Under current techno-economic conditions, real-time bidirectional electricity–hydrogen conversion offers no economic benefits. This framework quantifies cost drivers and design trade-offs for off-grid WSH-IES, providing an open modeling platform for academic research and planning applications. Full article

The increasing demand for next-generation rechargeable batteries that offer high energy density, a long lifespan, high safety, and low cost has led to a need for better electrode materials for lithium-ion batteries. This also involves developing alternative storage systems using common resources such as sodium-ion batteries, beryllium-ion batteries, or magnesium-ion batteries. Tin carbide (SnC) is highly promising as an anode material for lithium, sodium, beryllium, and magnesium ion batteries due to its ability to form nanoclusters like Sn(Li₂)C, Sn(Na₂)C, Sn(Be₂)C, and Sn(Mg₂)C. A detailed study was done using computational methods, including analysis of charge density differences, total density of states, and electron localization function for these hybrid clusters. This research suggests that SnC could be useful in multivalent-ion batteries using Be²⁺ ions because its properties can match or even exceed those of monovalent ions. The study also shows that the maximum capacity, stability energy, and ion movement in these materials can be understood by looking at atomic-level properties like the coordination between host atoms and ions. Recent findings on using tin carbide in these types of batteries and methods to improve their performance have been discussed. Full article

Distributed energy systems (DESs) are crucial for renewable deployment, but decentralised generation substantially increases flexibility requirements. Flexibility is framed as a system property that emerges from the coordinated operation of demand, storage and dispatchable generation across multi-energy carriers. Demand response schemes and demand-side management can provide flexibility, but their effective potential is constrained by user participation. Sector-coupling strategies and energy storage systems enable temporal and cross-sector decoupling between renewable generation and demand. Electrochemical batteries are technically mature and well suited for short-term balancing, but costs and environmental impacts are significant. Power-to-Heat with heat pumps and thermal energy storage is a cost-effective solution, especially when combined with low-temperature district heating. Electric vehicles, when operated under smart-charging and vehicle-to-grid schemes, can shift large charging demands feeding energy into the grid, facing battery degradation and infrastructure costs. Power-to-Gas and Power-to-X use hydrogen and electrofuels as long-term storage but are penalised by low round-trip efficiencies and significant capital costs if power-to-power with fuel cells is applied. On the supply side, micro-CHP can provide dispatchable capacity when fuelled by renewable fuels and combined with seasonal storage. Costs and efficiencies are strongly scale-dependent, and markets, regulation, digital infrastructure and social acceptance are key enablers of flexibility. Full article

Lithium iron phosphate (LiFePO₄, LFP) batteries dominate grid-scale energy storage, yet their cycle life is capped by its capacity fade issues. Conventional failure workflows suffer from redundant tests, high cost, and long turnaround time because the underlying mechanisms remain unclear. Herein, multi-scale characterization coupled with electrochemical tests have been quantitatively established to reveal four synergistic fade modes of LFP: active-Li loss, FePO₄ secondary-phase formation, SEI rupture, and particle fracture. A two-tier “screen–validate” protocol is proposed to accurately and efficiently disclose its mechanism. In the screening tier, capacity, cyclic voltammetry, electrochemical impedance spectroscopy, low-magnification scanning electron microscopy, and snapshot X-ray diffraction (XRD) rapidly flag the most probable failure cause. The validation tier then deploys mechanism-matched in situ/ex situ tools (operando XRD, TEM, XPS, ToF-SIMS, etc.) to build a comprehensive evidence chain of dynamic structural evolution, materials loss tracking, and quantitative proof. The streamlined workflow preserves scientific rigor and reproducibility while cutting analysis time and cost, offering a closed-loop route for fast failure diagnosis and targeted optimization of next-generation LFP batteries. Full article

Isolated microgrids with green hydrogen storage offer a promising solution for supplying electricity to remote communities where conventional grid expansion is infeasible. Designing such systems requires balancing two conflicting objectives: minimizing installation and operation costs while maximizing supply reliability. This paper proposes a multi-objective optimization methodology, based on the Non-dominated Sorting Genetic Algorithm II, to determine the optimal sizing of multiple microgrid components. This sizing explicitly addresses both the power capacities (kW) (for photovoltaic panels, wind turbines, electrolyzers, and fuel cells) and the energy storage capacities (kWh and kg) (for batteries and hydrogen tanks, respectively), aiming to generate Pareto-optimal solutions that explore this trade-off. The proposed method evaluates the trade-off by minimizing two objectives: the Net Present Value, which includes investment, replacement, and maintenance costs, and the total expected interruption hours, derived from an hourly energy balance analysis. The methodology’s effectiveness is validated using four distinct case studies. Three of these are based on real locations with specific load profiles and climate data. To test the method’s robustness, a fourth case study uses a fictitious load profile, designed with pronounced seasonal variations and a clear distinction between weekday and weekend consumption. Our results demonstrate the method’s ability to identify efficient hybrid renewable topologies combining photovoltaic and/or wind generation, batteries, and hydrogen systems (electrolyzer, storage tank, and fuel cell). The obtained cost–reliability curves provide practical decision-support tools for system planners. Full article

The maritime industry faces significant challenges from energy consumption and air pollution. Fuel cells, especially hydrogen types, offer a promising clean alternative with high energy density and rapid refueling, but their slow dynamic response necessitates integration with lithium batteries (energy storage) and supercapacitors (power storage). This paper investigates a hybrid vessel power system combining a fuel cell with a Hybrid Energy Storage System (HESS) to address these limitations. An improved droop control strategy with adaptive coefficients is developed to ensure balanced State of Charge (SOC) and precise current sharing, enhancing system performance. A comprehensive protection strategy prevents overcharging and over-discharging through SOC limit management and dynamic filter adjustment. Furthermore, the Parrot Optimization Algorithm (POA) optimizes HESS capacity configuration by simultaneously minimizing battery degradation, supercapacitor degradation, DC bus voltage fluctuations, and system cost under realistic operating conditions. Simulations show SOC balancing within 100 s (constant load) and 135 s (variable load), with the lithium battery peak power cut by 18% and the supercapacitor peak power increased by 18%. This strategy extends component life and boosts economic efficiency, demonstrating strong potential for fuel cell-powered vessels. Full article

As the adoption of electric vehicles increases, hybrid energy storage systems (HESS) combining batteries and supercapacitors mitigate the conflict between high energy capacity and power demand, particularly during acceleration and transient loads. However, frequent current fluctuations accelerate battery degradation, reducing long-term performance. This study presents a multi-objective Whale–Particle Swarm Optimization Algorithm (MOWPSO) for tuning the control parameters of a HESS composed of a lithium-ion battery and a supercapacitor. The proposed full-active configuration with dual bidirectional DC converters enables precise current sharing and independent regulation of energy and power flow. The optimization framework minimizes four objectives: mean battery current amplitude, cumulative aging index, final state-of-charge deviation, and an auxiliary penalty term promoting consistent battery–supercapacitor cooperation. The algorithm operates offline to identify Pareto-optimal controller settings under the Federal Test Procedure 75 cycle, while the selected compromise solution governs real-time current distribution. Robustness is assessed through multi-seed hypervolume analysis, and results demonstrate over 20% reduction in battery aging and approximately 25% increase in effective cycle life compared to battery-only, rule-based and metaheuristic algorithm strategies control. Cross-cycle validation under highway and worldwide driving profiles confirms the controller’s adaptability and stable current-sharing performance without re-tuning. Full article