Search Results (285)

Driven by China’s carbon peaking and carbon neutrality goals and the low-carbon transition of offshore oil and gas operations, the integration of offshore oil and gas with wind, solar, marine, and hydrogen energy is emerging as an important pathway for balancing energy security, emission reduction, and operational efficiency. Focusing on system boundaries and positions along the energy chain, this paper classifies offshore oil and gas–renewable energy integration into four representative pathways: shore power electrification, platform microgrid integration, hydrogen production and export, and energy islands or regional hubs. This study provides a structured narrative review of the key technologies, major constraints, and feasible implementation approaches associated with these pathways from the perspectives of offshore microgrid architecture, energy storage and backup, energy management, and offshore engineering installation, operation, and maintenance. The results indicate that the coordinated use of multiple energy sources and microgrid-based integration is particularly relevant to single-platform and small-cluster scenarios requiring local power balancing and progressive electrification. However, wider deployment remains constrained by resource intermittency, the safety and lifetime of energy storage systems, cross-system coordinated control, offshore engineering reliability, and the lack of a comprehensive standards system. This study provides a reference for offshore platform electrification retrofits in China, the comparison and selection of integration schemes, and the planning of demonstration projects. Full article

This paper presents a model predictive control (MPC)-based energy management strategy for hybrid power systems combining a proton-exchange membrane fuel cell (PEMFC) with a lithium iron phosphate (LFP) battery storage unit for renewable energy applications. The proposed framework optimizes power allocation between the two sources while respecting operational constraints, including current limits, power balance requirements, and state-of-charge (SOC) bounds with soft constraints to prevent overcharging and deep discharging. Unlike conventional rule-based approaches, the MPC formulation employs a quadratic cost function with tunable weighting factors that enable flexible prioritization of either fuel cell conservation or battery lifetime extension. Accurate yet computationally efficient models are developed for both components: an equivalent circuit model for the LFP battery and a theoretical electrochemical model for the PEMFC. The performance of the proposed strategy is validated through comprehensive simulations under realistic renewable generation and load profiles. Five case studies are examined, each representing different operational scenarios characterized by varying initial SOC conditions and component prioritization weights. The results demonstrate that the MPC-based approach effectively manages power distribution, maintains SOC within safe operating ranges, and adapts to changing system conditions. Quantitative analysis shows that the tunable weighting strategy successfully limits high-current events, reducing high-current operation and potentially mitigating current-related degradations. The proposed framework offers a scalable and flexible solution for improving the reliability of hybrid energy storage in modern renewable grids. Full article

Peutz–Jeghers syndrome (PJS) presents an apparent biological paradox: heterozygous germline pathogenic variants in STK11 predispose to predominantly benign hamartomatous growth while conferring a markedly elevated lifetime risk of cancer, whereas somatic STK11 inactivation in established tumors is frequently associated with aggressive progression and therapeutic resistance. STK11 encodes liver kinase B1 (LKB1), a serine/threonine kinase that integrates metabolic, oxidative, architectural, and immune stress responses. Rather than acting solely as a direct brake on proliferation, LKB1 couples cellular growth, survival, and tissue organization to environmental fitness. We therefore propose a context-dependent stress adaptation framework in which impairment of STK11/LKB1 signaling relaxes stress-imposed constraints on cellular fitness, while the ultimate biological outcome is determined by allelic status, tissue context, and cooperating genetic alterations. In PJS, a heterozygous germline STK11 pathogenic variant creates a constitutional cancer predisposed state in which one functional allele is initially retained, although subsequent loss or impairment of the remaining allele may occur during tumor evolution. In sporadic cancers, somatic STK11 inactivation is often biallelic and frequently cooperates with alterations in KRAS, KEAP1, TP53, NF1, or PI3K-pathway genes. These genetic contexts can promote metabolic reprogramming, redox adaptation, autophagy dependence, immune exclusion, cellular plasticity, and therapeutic resistance, with the strongest mechanistic and clinical evidence currently derived from lung adenocarcinoma (LUAD). Within this framework, enhanced persistence under metabolic, oxidative, immune, and therapy-induced stress does not exclude proliferative effects of STK11 loss but provides a permissive background upon which cooperating oncogenic programs can drive clonal expansion and malignant progression. Stress adaptation creates dependencies on interconnected buffering systems, including antioxidant defenses, autophagy, metabolic plasticity, and ferroptosis suppression. Therapeutic strategies that simultaneously disrupt multiple compensatory pathways may therefore exceed tumor adaptive capacity, convert stress tolerance into therapeutic vulnerability, and provide a rational framework for treating LKB1-deficient tumors and other stress-adapted cancers. Full article

On-orbit geometric calibration is essential for maintaining the geometric positioning performance of optical remote sensing cameras throughout their operational lifetime. Existing calibration approaches primarily rely on ground control points (GCPs) or stellar observations, which are often constrained by reference availability, observation conditions, and operational requirements, particularly for GEO wide-field imaging systems. To address these limitations, this paper proposes an Earth-limb-constrained framework for on-orbit geometric calibration of GEO wide-field area-array cameras. The proposed framework establishes geometric constraints by relating the observed Earth limb to reference Earth limb geometry derived from the camera imaging geometry and the WGS-84 reference ellipsoid. A unified geometric calibration model is developed by introducing an equivalent camera-to-inertial attitude representation, and terrain elevation information from the Shuttle Radar Topography Mission (SRTM) digital elevation model (DEM) is further incorporated as a local geometric constraint during calibration parameter estimation. The calibration parameters are estimated by minimizing the elevation residuals of multiple Earth limb observations through nonlinear optimization. The proposed framework is validated through both simulation and real GEO on-orbit experiments. Simulation results under different attitude-error settings demonstrate accurate parameter recovery and stable convergence, while experiments using thirteen GEO image scenes show an approximately 69% improvement in geometric positioning accuracy. An additional ablation experiment confirms that incorporating SRTM terrain-elevation information further improves the calibration performance. These results demonstrate the effectiveness and practical applicability of the proposed framework for GEO wide-field area-array cameras. By reducing the dependence on GCPs and dedicated stellar observations, the proposed framework provides a practical approach for the long-term geometric performance maintenance of GEO optical remote sensing systems. Full article

Triboelectric nanogenerators (TENGs) offer promising opportunities for distributed wind energy harvesting owing to their low-speed responsiveness, structural flexibility, and adaptability to non-stationary airflow. This review examines wind-driven TENGs from the perspective of realistic wind-field constraints, focusing on three representative scenarios: urban micro-winds, offshore wind–wave environments, and low-altitude complex flows. Scenario-specific advances in device architectures, materials and interfaces, environmental protection, power management, and system integration are systematically reviewed. Representative devices are further quantitatively compared in terms of wind-speed range, activation threshold, electrical output, power density, durability, and system-level energy delivery. Particular attention is given to inconsistent definitions of cut-in wind speed, output normalization, electrical loading, and validation conditions that limit cross-study comparison. Field-validation evidence is assessed from controlled laboratory tests to long-term field operation. Key challenges involving usable regulated energy, environmental reliability, lifetime prediction, array scaling, sustainability, and deployment economics are critically discussed. Finally, five grand challenges with actionable milestones are proposed to facilitate the transition of wind-driven TENGs from laboratory prototypes toward deployable distributed micro-energy systems. Full article

The growing adoption of electric vehicles (EVs) and the increasing need for coordinated charging and energy management have highlighted the importance of vehicle-to-everything (V2X) technologies within battery management systems (BMSs). However, existing studies often treat EVs as idealized storage systems, overlooking battery and BMS-related operational constraints, and typically analyze driving, charging, and bidirectional energy exchange in isolation, limiting realistic, end-to-end evaluation of daily operation scenarios. This paper addresses these gaps by analyzing how advanced, BMS-integrated V2X capabilities can be deployed in real-world EV operation, focusing on battery utilization, operational performance, and system-level energy interactions. A unified, scenario-based methodology combines mobility demand, AC/DC charging behavior, and bidirectional V2X services within a single daily operational framework. Representative use cases for both passenger EVs and electric light commercial vehicles (eLCVs) are developed to capture realistic driving patterns, environmental conditions, and energy exchange scenarios. The results indicate that V2X operation can provide substantial gross economic value in the investigated scenarios. For the eLCV cases, the estimated increase in equivalent full cycle (EFC) throughput rate ranges from approximately 14.3% to 30.3%, while combined summer–winter cumulative avoided electricity purchase cost reaches approximately EUR 4033 for the higher-power charging strategy, equivalent to 57.0% of the adopted battery cost reference. The analysis also highlights the strong influence of ambient temperature and usage patterns on energy consumption, charging strategies, and overall system performance. Overall, this work provides a holistic and practical evaluation framework for V2X-enabled BMS operation, demonstrating its potential to improve grid support, enhance energy efficiency, and support sustainable EV integration while balancing economic and battery-lifetime trade-offs. Full article

The large-scale integration of photovoltaic generation into distribution grids has introduced significant operational challenges, including voltage excursions, reverse power flows, and increased variability. Battery energy storage systems (BESSs) offer a versatile solution by providing coordinated active- and reactive-power support. However, their scheduling in active distribution networks is challenging because of the non-convex alternating-current (AC) power-flow equations, the nondifferentiability of battery-degradation modeling, and uncertainty in renewable generation and demand. This paper proposes a two-stage methodology for the day-ahead operation of BESSs in ADNs. In the first stage, parallel particle swarm optimization (PPSO) determines the hourly active- and reactive-power schedules of the BESS units. In the second stage, a matrix-based multi-period AC power flow based on successive approximations evaluates the schedules and verifies voltage, thermal, converter-capability, and state-of-charge (SoC) constraints. A rainflow-counting degradation model is incorporated into the objective function to account for cycling and calendar aging costs. The methodology is assessed through ablation analyses comparing active-power-only and coordinated P–Q dispatches, degradation-unaware and degradation-aware scheduling, and serial and parallel PSO implementations. It is validated on modified 33-, 69-, and 136-node systems under deterministic and uncertainty-based operating conditions, including 100 demand and PV-generation scenarios. PPSO is compared with parallel versions of the adaptive Jaya algorithm (AJAYA), genetic algorithm (GA), multi-verse optimizer (MVO), salp swarm algorithm (SSA), grey wolf optimizer (GWO), and vortex search algorithm (VSA), using operating-cost reduction, computational time, solution variability, feasibility indicators, BESS lifetime, and weekly cost analysis. Additionally, exact one-sided Wilcoxon signed-rank tests with Holm adjustment are used to assess the statistical significance of the economic differences between PPSO and the benchmark methods. Results show that PPSO provides the lowest or most competitive operating costs and the shortest computational time in the evaluated cases, while all network and storage constraints remain satisfied. Full article

Active distribution networks (ADNs) require battery energy storage system (BESS) scheduling strategies that reduce operating costs while preserving electrical feasibility and battery lifetime. This paper proposes a parallel adapted JAYA-based methodology for the day-ahead coordinated active and reactive power dispatch of BESS units in this type of grid. The novelty of this research lies in four key contributions: (i) the coordinated optimization of active and reactive power from BESS converters, exploiting their full capabilities for both energy management and voltage support; (ii) the integration of battery degradation costs within the optimization framework, preventing short-term economic strategies that accelerate aging; (iii) the implementation of a parallel adapted JAYA algorithm (AJAYA) with stagnation control and population reactivation mechanisms to enhance solution quality and convergence; and (iv) a comprehensive assessment under both deterministic and uncertainty-based operating conditions, providing a realistic validation of the proposed approach. Our model minimizes conventional generation, DER operation and maintenance, and BESS degradation costs while subject to power balance, distributed energy resource limits, voltage and current constraints, converter capacity, and state of charge (SoC) requirements. Each solution is encoded as BESS active/reactive power setpoints and evaluated through a multi-period AC power flow based on the successive approximations method, including SoC verification and a penalized fitness function. The methodology was validated in modified 33- and 69-node ADNs under deterministic and uncertainty scenarios (based on the conditions observed in Colombia), and it was benchmarked against the population-based genetic algorithm (PGA), the multiverse optimizer (MVO), the salp swarm algorithm (SALPS), the grey wolf optimizer (GWO), and the vortex search algorithm (VSA). According to the results, AJAYA outperformed the comparison methods, providing the best economic performance and exhibiting a robust behavior, with standard deviations below 0.06% and processing times below 0.05 h within a 24-h scheduling horizon. These findings demonstrate that the proposed framework constitutes an AC-feasible and degradation-aware academic contribution and a practical decision-support tool for operators and BESS owners, enabling a cost-effective and reliable BESS scheduling that preserves battery lifetime while improving network operation. Therefore, this research addresses the critical need for advanced energy management strategies that balance short-term economic benefits, technical feasibility, and long-term asset sustainability in modern distribution networks. Full article

Solid electrolyte interphase (SEI) characterization is needed to interpret the performance, degradation, and lifetime of graphite and Si-containing carbon-based negative electrodes in lithium-ion batteries. However, SEI claims are often difficult to compare because measured signals, inferred assignments, sample history, and electrode architecture are not always clearly separated. This review presents a claim-bounded framework for SEI characterization that distinguishes direct observables from inferred chemical, molecular, structural, morphological, and functional information. Photoelectron spectroscopy methods provide chemical-state and relative-depth-sensitivity constraints; secondary-ion mass spectrometry methods provide fragment and isotope distributions; vibrational spectroscopies support functional-group and local vibrational evidence; nuclear magnetic resonance and molecular mass spectrometry provide molecular or product-level constraints; and microscopy, tomography, and atomic force microscopy provide morphology, architecture, local thickness, topography, and mechanical response. Across these methods, rinsing, drying, sputtering, beam exposure, extraction, and limited sampling can alter the observable and therefore the defensible claim. The review emphasizes the distinction between native electrode-associated SEI features and extracted, soluble, or electrolyte-phase products, and between morphology-only evidence and chemically assigned morphology. It concludes by proposing claim-driven correlative workflows and reporting guidance for reproducible interpretation on graphite, Si/graphite, Si/C, and carbon-coated Si architectures where directly studied or present. Full article

This paper presents an integrated day-ahead battery energy storage system (BESS) scheduling framework for radial active distribution networks with photovoltaic generation. Its main contribution is a reproducible evaluation chain combining non-ideal state-of-charge (SoC) feasibility correction, sequential AC power-flow verification, consistent metaheuristic benchmarking, and post-dispatch battery-aging analysis. The operating-cost objective coordinates hourly BESS active-power exchanges while enforcing storage and AC-network constraints. A parallel Coyote Optimization Algorithm (COA) is compared with parallel GWO, GA, PSO, and MVO implementations under a common formulation, correction procedure, evaluator, and computational environment. Validation uses modified 33-, 69-, and 136-bus radial feeders: 100 independent runs for the deterministic 33-bus benchmark, 100 independently optimized Monte Carlo scenarios for the 69-bus assessment, and seven representative daily profiles for the 136-bus weekly case. COA achieved an average cost reduction of 1.0084%, with the lowest dispersion of $\sigma =0.0070\%$, in the 33-bus system; a mean scenario-wise reduction of 1.8337% in the 69-bus system; and a weekly reduction of 0.4402% in the 136-bus system. It obtained the lowest operating costs among the evaluated calibrated configurations, and all pairwise comparisons remained significant after Holm’s step-down adjustment applied separately within each system, although COA required greater computational effort than PSO. The reported schedules satisfied the imposed BESS and AC-network limits. Battery aging was evaluated only after scheduling and was not included in the optimization objective. The resulting cost-oriented schedules produced equivalent full-cycle values near 0.8 day⁻¹ and projected 80% SoH lifetimes of approximately 6–8 years. These results provide an AC-feasible basis for comparing economic performance and post-dispatch battery-health implications under the evaluated conditions. Full article

Precise estimation of lithium-ion battery State of Health (SOH) is highly demanded for reliable battery management systems, lifetime prediction, and safety assurance in electric vehicle and energy storage applications. Traditional data-driven approaches such as multilayer perceptron (MLP) often suffer from poor generalization and may produce non-physical degradation trends due to the absence of domain knowledge constraints. To address these limitations, this work proposes a monotonic Physics-Informed Residual MLP neural network framework for SOH estimation using the NASA battery dataset (B0005, B0006, B0007, and B0018). The proposed model incorporates a physics-based monotonic degradation constraint by penalizing positive gradients of SOH with respect to cycle index, thereby enforcing physically consistent capacity fade behavior. A loss function is employed to improve robustness and enhance late-cycle learning. Experimental results demonstrate that the proposed approach achieves an RMSE of 0.0287, MAE of 0.0181, and MAPE of 2.69%, indicating accurate and stable SOH prediction across multiple degradation patterns. The use of physics-informed constraints markedly enhances deterioration consistency and diminishes overfitting relative to solely data-driven models. The proposed structure offers a faithful solution for State of Health estimation in practical battery management systems. Full article

Uncoordinated charging and discharging of large-scale electric vehicles (EVs) exacerbates grid peak–valley fluctuations, while deep discharging accelerates battery degradation. To address these challenges, this study proposes a coordinated charging–discharging optimization strategy integrating dynamic discharge restraint and a three-dimensional weighted comprehensive objective covering electricity price signals, grid operational constraints and battery health state. First, an EV travel behavior model is established to characterize spatiotemporal availability. Subsequently, a coupled battery aging model is developed by combining a power-law-based cycle aging formulation with a square-root calendar aging model, based on which an adaptive linkage mechanism between the depth-of-discharge upper bound and a net-revenue threshold is introduced. Building on these components, this model is constructed to jointly optimize three sub-objectives: charging station revenue maximization, battery lifetime cost minimization, and load fluctuation suppression, thereby mitigating grid peak–valley differences while reducing battery degradation and discharge costs. Multi-scenario simulations demonstrate that the proposed strategy, by coupling discharge restraint with spatiotemporal dynamic pricing, enables precise peak shaving of discharge power. For a fleet of 50 EVs, the charging station revenue reaches 1073.7 CNY, the grid peak–valley difference is reduced by 9.8%, and the battery degradation cost decreases by 23.1% compared with conventional strategies, corresponding to a carbon emission reduction of 386.4 tCO₂. When scaled to 100 EVs, the revenue increases by 101.9%, while the peak–valley difference is further reduced by 0.6%, demonstrating the effectiveness of the proposed strategy in enhancing economic performance, extending battery lifetime, and supporting grid stability. Full article

This work addresses statistical inference for multi-level step-stress accelerated life testing. Under the assumption that product lifetime follows the Gompertz distribution at each stress level, featuring a common shape parameter and stress-varying scale parameters, the cumulative exposure model (CEM) serves to link the lifetime distributions across different stress levels. Given the physical principle that lifetime decreases with an increase in stress, order-restricted parameter estimation methods have been developed. Within the classical frequentist framework, the order restriction is converted into box-constrained optimization via parameter reparameterization, obtaining maximum likelihood estimators (MLEs) and constructing asymptotic confidence intervals (CIs). With the Bayesian approach, weakly informative priors are adopted to prevent posterior impropriety, and a Markov chain Monte Carlo (MCMC) algorithm is employed for posterior sampling to avoid weight degeneracy in importance sampling. Extensive Monte Carlo simulations demonstrate that, after incorporating the order restriction, the Bayesian estimates yield smaller values in terms of bias, mean squared error (MSE), and interval length, while the MLEs provide classical asymptotic CIs as a reference. Lastly, a real dataset on fish swimming endurance is examined to demonstrate the practicality as well as the effectiveness of these proposed approaches. Full article

India’s rapid expansion of utility-scale solar photovoltaic (PV) capacity is increasingly constrained by aging assets and the temporal mismatch between generation and peak demand. This study develops a techno-economic framework integrating battery energy storage systems (BESSs) with repowered solar PV projects, using repowered electricity as a low-cost charging source. A capacity-based assessment estimates national repowering potential of 7.2 GWp under power purchase agreement constraints and 10.9 GWp under technical limits. The levelized cost of repowered electricity is ₹1.40/kWh, significantly lower than prevailing utility-scale solar tariffs under stated assumptions. Levelized storage costs range from ₹5.08 to ₹4.12/kWh for 2–6 h durations, declining with improved inverter and balance-of-system utilization. Financial analysis under a ₹10 per kWh peak tariff arbitrage scenario yields internal rates of return between 17.5% and 24.2%, with positive project viability across configurations. Sensitivity analysis identifies capital cost as the dominant economic driver. Environmental benefits include annual greenhouse gas reductions of 13.8–17.2 MtCO₂, accumulating to 411–514 MtCO₂ over the project lifetime. These findings demonstrate that repowering-integrated battery storage offers a cost-effective, scalable pathway to enhance renewable integration, displace fossil fuel peak generation, and support India’s low-carbon transition, highlighting a viable framework for improving system flexibility and overall system performance. Full article

Carbon fibre-reinforced polymer (CFRP) composites represent promising lightweight materials for automotive powertrain systems, where increasing demands for weight reduction, energy efficiency, and emission reduction are driving the replacement of conventional metallic components. However, automotive powertrain environments expose CFRP materials to elevated temperatures, cyclic mechanical loading, chemical exposure, and tribological interactions, creating complex degradation conditions that significantly influence long-term durability and reliability. This review systematically analyzes CFRP composites for automotive powertrain applications, focusing on the relationship between operational requirements, material selection, reinforcement architecture, manufacturing technologies, and degradation mechanisms. High-performance thermoplastic systems such as CF/PEEK, CF/PPS, and CF/PEKK are critically compared with conventional thermoset composites. CF/PEEK systems demonstrate superior thermomechanical stability, maintaining significant mechanical performance at temperatures approaching 250 °C and tensile strengths of approximately 1400–1600 MPa, whereas CF/PPS composites provide a more economically efficient compromise between thermal resistance, chemical stability, manufacturability, and recyclability for medium-temperature applications. The review further analyzes dominant degradation mechanisms, including creep deformation, fatigue damage, delamination, fibre–matrix interface degradation, and tribological wear. CFRP degradation is shown to result from the interaction of multiple coupled mechanisms rather than from isolated material failure modes. Tribological wear rates typically range from 10⁻⁶ to 10⁻⁵ mm³/(N·m), while creep–fatigue interactions may reduce component lifetime by up to 40–60% under combined thermomechanical loading. Advanced design strategies, including fibre orientation optimization, laminate architecture tailoring, thickness gradation, and hybrid metal–composite structures, are evaluated together with major manufacturing technologies such as injection moulding, compression moulding, overmoulding, automated fibre placement, and additive manufacturing. The presented review establishes an integrated framework linking material systems, operating conditions, manufacturing processes, and durability requirements for automotive powertrain applications. The analysis demonstrates that no universal CFRP system exists for all powertrain components and that optimal material selection requires balancing thermal stability, fatigue resistance, tribological performance, manufacturability, recyclability, and economic constraints according to the specific operating conditions of each component category. Full article