Energy Storage最新文献

Demand for effective and cost-effective energy management solutions has increased due to residential settings' raising reliance on energy storage systems and renewable energy sources; however, integrating these systems seamlessly while preserving balanced grid interaction and financial benefits is a major challenge. This paper proposes an optimal integration strategy for renewable energy and energy storage in Home Energy Management Systems (HEMS) to enhance grid interaction and maximize economic benefits. The proposed approach uses Hiking Optimization (HO) to improve the Peak-to-Average Ratio (PAR) and minimize energy expenditures by integrating renewable energy sources and sophisticated optimization techniques into the HEMS. The HO method is employed to optimize the HEMS by minimizing daily energy costs and reducing the PAR through efficient utilization of energy storage systems and renewable energy sources. The proposed method is implemented on the MATLAB platform and contrasted with existing methods, including Particle Swarm Optimization (PSO), Genetic Flower Pollination Algorithm (GFPA), and Deep Neural Network (DNN). The comparison demonstrates the proposed method's improved performance, which achieves a cost of 440 cents. In contrast, the PSO approach yields 550 cents, the GFPA method achieves 666 cents, and the DNN method reaches 688 cents. This comparison illustrates the performance of the proposed strategy in optimizing HEMS performance for cost reduction in residential applications, outperforming traditional energy management techniques.

Eutectic phase change materials (PCMs) have shown great potential for thermal energy storage applications due to their distinct melting behavior and high latent heat capacities. However, experimental determination of thermophysical properties is time-consuming, costly, and resource-intensive, and accurately predicting their phase behavior and thermal properties during phase transitions remains challenging. This study systematically employs a thermodynamic framework based on the Modified UNIFAC model to predict the thermal properties of binary eutectic systems of eicosane and various fatty acids, including lauric acid, myristic acid, and palmitic acid. The interplay between combinatorial and residual parts of the activity coefficient suggests that the dominance of molecular size/shape difference over energetic interactions influences the overall behavior. The total activity coefficient of less than 1 indicates negative deviations from Raoult's Law. The theoretical predictions for thermal properties are validated against experimental data obtained via Differential Scanning Calorimetry (DSC). The results demonstrate a good agreement between the predicted and experimentally measured values, with errors of just 1.4 wt% in composition, 2.8°C in melting point, and 2 J/g in latent heat of fusion and 0.76 wt%, 3.1°C, and 8.7 J/g for freezing parameters. Eutectic PCMs exhibit a latent heat of approximately 214–236 J/g at a melting range of 31°C–34°C. This unified approach highlights the potential of the Modified UNIFAC model as a reliable tool for estimating the thermal properties of complex eutectic systems, significantly reducing experimental effort and providing a cost-effective pathway for next-generation thermal energy storage design and optimization.

The human brain functions as a highly efficient control center, inspiring the field of neuromorphic computing, which seeks to replicate its structure and behavior through hardware systems. Neuromorphic computing integrates processing and memory functions using artificial neurons and synapses designed with electronic circuits, enabling parallel, energy-efficient data handling. One of the leading technologies supporting this paradigm is phase change memory (PCM), a non-volatile memory that stores data through reversible transitions between amorphous (high resistance) and crystalline (low resistance) states of chalcogenide materials, particularly Ge₂Sb₂Te₅ (GST225). PCM exhibits fast read/write speeds, excellent data retention, and scalability, making it ideal for neuromorphic architectures. This review highlights recent advancements in PCM for neuromorphic computing, including innovations in doping strategies and device engineering. Notable developments include arsenic-doped ovonic threshold switches (OTS) for enhanced selector performance, monolayer Sb₂Te₃ for atomic-scale devices, and heater-all-around (HAA) 3D architectures for reduced energy consumption. Integration with machine learning tools enables precise atomistic modeling, accelerating material and device optimization. Furthermore, emerging variants like ovonic unified memory (OUM) and interfacial PCM (IPCM) offer unique performance advantages. While PCM promises significant benefits, key challenges such as resistance drift, endurance limits, and thermal crosstalk must be addressed. The global neuromorphic computing market is poised for exponential growth, driven by innovations in materials, algorithms, and architectures. The PCM and neuromorphic computing represent a transformative leap toward intelligent, adaptive, and energy-efficient computing systems.

Thermal management systems have become increasingly important in addressing the critical challenges associated with lithium-ion battery operation. Proper temperature regulation is essential for maintaining safety, optimizing electrochemical performance, and extending cycle life. This review provides a comprehensive and structured analysis of the latest developments in battery thermal management systems (BTMS), encompassing foundational commercial systems and advanced active, passive, and hybrid cooling strategies. The discussion integrates insights from materials science, thermodynamics, systems engineering, and artificial intelligence-based control strategies. Among the most significant advancements are phase change materials (PCMs) with enhanced thermal conductivity, such as graphene-reinforced paraffin composites, which improve heat absorption and dissipation. Another key innovation is the use of microchannel liquid cooling systems, particularly those optimized through advanced topological design techniques, enabling more efficient heat transfer. Additionally, intelligent control mechanisms, including digital twin-assisted thermal management systems, allow for real-time monitoring and adaptive cooling strategies. The review critically examines the trade-offs between cooling performance, energy efficiency, and cost considerations, evaluating technologies based on key performance indicators. It also highlights several transformative developments, including self-healing thermal interface materials, 3D-printed microchannel cold plates, radiative cooling surfaces, and smart, self-regulating materials. Looking ahead, emerging frontiers such as digital twin-assisted thermal control, blockchain for lifecycle management, and quantum-optimized design are identified as promising next-generation solutions with potential to enhance scalability and sustainability. These innovations have the potential to significantly improve thermal management in both electric vehicles and grid-scale energy storage applications, ensuring safer and more reliable battery operation.

The rise of electric vehicles (EVs), driven by pollution-control policies, relies on lithium-ion batteries that face performance issues from temperature fluctuations. Thermal runaway remains a major safety risk, highlighting the need for efficient battery thermal management systems (BTMS). The present work numerically investigates the effectiveness of phase change materials (PCMs) and fins in BTMS performance. An 8-cell module operating at an 8C discharge rate was selected for analysis. ANSYS-based simulations were conducted to analyze the thermal behavior of prismatic battery modules under high discharge conditions. Both organic and inorganic PCMs were evaluated, alongside fins of varied geometry, orientation, and number. Results show that high thermal conductivity PCM, such as capric acid, lowered peak battery temperatures by 36 K compared to modules without cooling. Under natural convection, vertical fins were more effective than horizontal fins, whereas under elevated convective heat transfer coefficients (50 W/m²·K), horizontal fins achieved a 31 K reduction relative to no cooling. The combined effects of high thermal conductivity and specific heat capacity of PCMs were found to be critical for thermal regulation. Optimized PCM thickness outperformed fin-only configurations in overall effectiveness. However, achieving the right balance between fins and PCM remains essential for compactness and practical design integration. The advanced thermal management strategies improve battery safety and reliability and effectively address the United Nations Sustainable Development Goals.

The present study focuses on the development of erythritol-based activated biochar composite phase change materials (PCMs) targeting medium-temperature energy storage applications, including waste heat recovery, solar desalination, and solar thermal energy storage. The activated biochar composites were produced from coconut shell using pyrolysis. The fabricated samples were characterized using X-ray diffraction (XRD), differential scanning calorimetry (DSC), and Fourier-transform infrared spectroscopy (FTIR) to evaluate the phase composition, thermal properties, and functional group analysis of the composites. Biochar composites exhibited enhanced thermal energy storage properties and thermal stability compared to pure PCM. TGA was employed to assess weight changes during controlled temperature increase to analyze thermal stability and decomposition. The degradation kinetics for both materials were evaluated to determine the activation energy needed for degradation processes using Kissinger–Akahira–Sunose (KAS), Flynn–Wall–Ozawa (FWO), and Starink models. The results indicate that the activation energies for pure PCM, determined using the KAS, FWO, and Starink methods, are 82.81, 88.04, and 83.54 kJ/mol, respectively. For PCM + 0.25% G + 20% BC, activation energies varied between 325.67 and 347.37 kJ/mol. For PCM + 0.5% G + 20% BC, activation energies varied between 235.05 and 256.94 kJ/mol. For PCM + 20% BC, activation energies varied between 13.83 and 24.10 kJ/mol. Overall, the findings highlight the impact of graphene with biochar on the thermal properties of both pure and composite biochar PCMs. The 20% biochar composite with 0.25% graphene demonstrated improved thermal stability, highlighting its potential for effective medium-temperature energy storage solutions.

Battery technology has been a hot spot in the research community, owing to the radical unification of renewable sources into the electric power industry. Redox flow batteries (RFBs), which are electrolyte-based, are preferred and have found viable applications in microgrids (MGs) due to their scalable nature, operational flexibility, and environmental friendliness. Acknowledging the complexity of the MG system and the importance of effective battery operation, this paper presents a systematic and comprehensive review on battery and energy management for RFBs. Utilizing the bibliographical analysis, this research critically examines the existing literature on battery and energy management, their research trends, and associated challenges. The summary reveals that existing approaches lack the implementation of advanced techniques that enable experiential learning and tailored operational strategies required for safer, reliable operation in convergence with other energy sources. Considering the challenges, the paper emphasizes emerging technology, including artificial intelligence (AI), system modeling, and digital twins (DTs), for effective development, monitoring, and furthering reliability in RFB. IoT-integrated BMS and Energy Management System (EMS) systems can aid data collection, allowing integration of intelligence systems performing accurate forecasting and system optimization, whereas AI agents can help with cybersecurity and fault response, realizing state-of-the-art battery/EMS. Subsequently, existing drawbacks and future prospects are presented for the research community and are expected to act as a catalyst to advance EMS and BMS research, tailored for RFB.

Since 1991 to the present, both metropolitan districts and rural communities in Iraq have been reliant on gasoline or diesel generators to make up for the lack of grid energy. The combination of Photovoltaic (PV) and Battery Storage systems (BSS) as energy sources is widespread in the global energy industry. This case study is based on actual monthly electricity consumption statistics over 1 year for a home in the Al-Latifiya district, south of Baghdad, Iraq, to install a roof PV system instead of a Diesel Generator (DG) to compensate for the interruption of the public grid. Using computer modeling and simulation with the HOMER software, an optimal power generation system was designed. Two modeling scenarios were conducted, one for DG and the grid and the other for PV/BSS and the grid. Based on simulation findings, the PV/BSS and grid systems have been determined to be a technically and economically viable solution for mitigating DG and implementing this alternative power generation at a fair cost. The proposed system can meet the demand side with a penetration level of 60.4% and a PV energy share of 48.4%, resulting in a reduction in electricity bills to $108.58/year and a lower COE ($0.0772/kWh) than the current system (grid and diesel generator) ($0.0.126/kWh). The proposed system also achieved an annual emissions reduction of 5279 kg of CO₂ per year due to displacing the fuel consumption of diesel generators and reducing the energy use of the public grid by 31%.

The relationship between state of charge (SoC) and open circuit voltage (OCV) is fundamental to SoC estimation in equivalent circuit models (ECMs). While its dependency on temperature and aging is recognized, the influence of real-time capacity variations is often underexplored. This study investigates the impact of capacity degradation on the SoC–OCV relationship across different temperatures, aging levels, and OCV testing methods, using the CALCE and NASA battery datasets. Results show that when SoC is normalized by the degraded capacity, the SoC–OCV relationship remains nearly constant for SoC values above 20%. Leveraging this property, we propose a real-time algorithm capable of simultaneously estimating SoC and capacity throughout the battery lifecycle. The algorithm also estimates state of health (SoH) by independently quantifying resistance and capacity related degradation. A first-order ECM with a single resistor-capacitor branch models battery dynamics, while Kalman filtering enables real-time state updates. The method is validated under diverse conditions including partial and full discharges, varying temperatures, dynamic load profiles (e.g., US06, FUDS, BJDST, HPPC), and different aging states. Experimental results demonstrate robust performance, with SoC estimation errors within ±0.01 and capacity estimation errors within ±0.05 Ah, confirming the algorithm's effectiveness for real-world battery management system applications.

This paper proposes a novel grid-friendly multi-objective approach to optimize energy management in an integrated source-grid-load-storage microgrid (MG). To enhance the MG's grid integration potential and cost-effectiveness, this approach develops a grid-friendly multi-timescale energy scheduling optimization (Gf-MtESO) strategy and a new evaluation metric ($��{\omega }_{\mathrm{Gf}}�$). Gf-MtESO first establishes electricity market coordination by pre-submitting energy demand as subsequent scheduling constraints, effectively mitigating power exchange fluctuations between MGs and the main grid. Additionally, $��{\omega }_{\mathrm{Gf}}�$, by holistically evaluating dependency and volatility, facilitates comprehensive assessment of MGs' grid integration potential. To resolve conflicting objectives and multi-constraints challenges in developing the Gf-MtESO strategy, this approach applies an improved elitist non-dominated sorting genetic algorithm based on stepwise-solving and rotating-population optimization (SRO-NSGA-II). SRO-NSGA-II first decouples the problem and updates the population using rotated binary crossovers to accelerate the search for feasible domains. Results indicate that SRO-NSGA-II concurrently maintains solution diversity and convergence speed, outperforming NSGA-II in hypervolume metrics. Particularly, the novel approach demonstrates faster scheduling plans development and improves grid-connection friendliness by 90.76% with a 4.86% cost variation compared to benchmark methods, which provide a systematic approach to realize friendly grid integration while ensuring economic viability in MGs' applications.