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Thermal energy demand in buildings represents one of the largest contributors to global energy use and CO₂ emissions. Advanced thermal energy systems, including borehole thermal energy storage (BTES) integrated with highly intelligent air handling units, offer promising solutions to reduce emissions while ensuring affordable and reliable comfort. This study examines a state-of-the-art commercial building in Uppsala, Sweden, that already employs a fossil-free and highly efficient BTES–district heating configuration. Although this system is well-designed and operates intelligently, it still has important limitations, including underutilization of borehole potential, limited thermodynamic efficiency from direct-use exchange, and a lack of flexibility under varying energy tariffs. Therefore, this work aims to make an already smart and efficient system even smarter by integrating a ground source heat pump with adaptive seasonal energy management. A comparative benchmarking analysis is carried out using validated TRNSYS simulations and real operational data to evaluate performance, economic viability, and environmental outcomes. The results show that integrating a clever heat pump system enhances the annual heat extraction from the ground by approximately 27 %, resulting in a 29 % decrease in overall heating costs, and improves long-term savings by around 20 %, despite an 11 % rise in upfront investment. Environmentally, the enhanced system substantially reduces CO₂ emissions, cutting the annual impact by more than 90 % compared to the current configuration, aligning with the Swedish zero-emission targets. However, the operational cost savings strongly depend on peak heat (power) costs, which are expected to rise under policymakers' frameworks. This indicates that the long-term viability of adding heat pumps in Sweden is shaped not only by technical performance and CO<inf>2</inf> savings but also by evolving local energy price structures. Yet, the considerable CO₂ savings helped by Sweden's green electricity mix and the opportunity to enjoy hourly spot-price variability through advanced controllers make heat pump integration a compelling option for future-proofing ultra-efficient buildings.

As the penetration of variable renewable energy sources increases, maintaining balance between supply and demand becomes challenging, which necessitates increased flexibility in operations across both sides of the energy system. While substantial research has focused on supply-side flexibility, this work shifts attention to demand-side management and reviews the flexibility potential achieved through the aggregation of diverse loads, including non-controllable loads, shiftable loads, interruptible loads, thermostatically controlled loads and home battery energy storage systems. Additionally, the participation of aggregated loads in various markets, particularly in day-ahead and balancing markets, is investigated showing substantial cost saving potentials: aggregation demonstrates 1 %–44 % operational cost reduction in the day-ahead market, while 6.7 %–43.8 % in balancing market through ferquency reserve services. Multi-market participation strategies combining different load types can achieve up to 50 % overall cost reductions, with larger aggregations showing higher optimization potential through advanced clustering methods. By addressing both technical and economic aspects, this review aims to guide aggregators and policymakers in optimizing demand-side resources to improve grid resilience, enhance renewable energy integration, and maximize user participation in flexibility markets.

Due to the non-uniform heat transfer process of phase change materials, a gradient metal foam structure is designed with varying porosities from inner to outer regions to enhance heat transfer in horizontal phase change energy storage units under rotational conditions. Numerical simulations use the enthalpy-porosity method to verify a numerical model of solid-liquid phase variation under rotation. The solidification characteristics of different gradient metal foam structures are compared and analyzed through an orthogonal test. Results indicate that a metal foam structure with a positive porosity gradient from inner to outer regions significantly improves heat transfer efficiency and uniformity compared to structures with uniform or negative porosity gradients. Specifically, the solidification time of Case 1 with a 0.97–0.98-0.99 porosity gradient foam combination is 12.22 % and 43.60 % lower than that of Case 3 with a uniform foam structure and Case 5 with a negative gradient foam combination, respectively. Furthermore, the mean heat release rate and temperature response are increased by 15.07 % and 18.20 % compared to Case 3, and by 81.82 % and 92.90 % compared to Case 5. The orthogonal experiment demonstrates that the porosity combination has a greater impact on double optimization objectives than PPI, with no interaction between the two factors. The optimal structure identified is a porosity combination of 0.97–0.98-0.99 with PPI = 50, showing the highest mean heat release rate and the shortest solidification time.

Battery energy storage systems enhance grid flexibility by enabling participation in frequency containment reserves (FCR), day-ahead (DA), and peak shaving (PS) markets—each with distinct operational and economic rules. Yet, operators face a key challenge: how to stack services without compromising reliability or lifespan? This study presents a unified mixed-integer linear programming framework for optimal multi-service stacking, rigorously integrating technical constraints — including real-world fuse limits and battery degradation — alongside market participation requirements. Uniquely, the model balances both droop-based and energy-based FCR participation, precise day-ahead market trading, and behind-the-meter cost management, all while tracking the interplay of physical and regulatory boundaries. The framework is tested using real industrial data from Sweden, under the coordinated rules of Svenska kraftnät. Results reveal that holistic co-optimization is not just a theoretical ideal but a practical economic lever: stacking services increases net profit by about 83% compared to the single-market strategy (DA). This highlights the need for a holistic approach that manages state of energy (SoE), degradation, and fuse limits. The analysis shows that moderate relaxations in fuse limits boost revenue, but benefits plateau, suggesting that reasonable sizing captures most economic gains without costly upgrades to fuses or grid infrastructure. 

Electrochemical impedance spectroscopy (EIS) offers valuable insights into the dynamic behaviors of lithium-ion batteries, making it a powerful and non-invasive tool for evaluating battery health. However, EIS falls short in quantitatively determining the degree of specific degradation modes, which are essential for improving battery lifespan. This study introduces a novel approach employing deep neural networks enhanced by an attention mechanism to identify the degree of degradation modes. The proposed method can automatically determine the most relevant frequency ranges for each degradation mode, which can link impedance characteristics to battery degradation. To overcome the limitation of scarce labeled experimental data, simulation results derived from mechanistic models are incorporated into the model. Validation results demonstrate that the proposed method could achieve root mean square errors below 3% for estimating loss of lithium inventory and loss of active material of the positive electrode, and below 4% for estimating loss of active material of the negative electrode while requiring only 25% of early-stage experimental degradation data. By integrating simulation results, the proposed method achieves a reduction in maximum estimation error ranging from 42.92% to 66.30% across different temperatures and operating conditions compared to the baseline model trained solely on experimental data. 

The low temperature environment poses a significant challenge to the application of electric vehicles (EVs). At low temperatures, the dynamic characteristics inside the battery become significantly different from those in the temperature range of 10–40 °C, resulting in high uncertainties in the estimation of state of charge (SOC). Experimental studies on two types of lithium-ion batteries have found that due to changes in battery polarization characteristics at low temperatures, the open circuit voltage (OCV) identified by the commonly used equivalent circuit models and parameter identification methods becomes more distorted. This is the reason for the failure of most SOC estimation methods based on OCV-SOC mapping. A part of polarization voltage is incorrectly involved in the OCV by online parameter identification. Based on this phenomenon, a novel method is proposed to achieve accurate SOC estimation at low temperatures by compensating this part of polarization voltage. The compensation voltage is calculated by a function, which is identified from experimental data using genetic algorithm. The validation against experimental results demonstrates that the proposed method can achieve a root mean square error and mean absolute error of less than 3 % for the SOC estimation in temperatures down to −20 °C. Moreover, this method only needs experimental data of dynamic operating conditions measured at two temperatures which cover most of the battery's working temperature range. And its computational complexity is low, making it suitable for onboard applications. 

Supercapacitors are widely used in transportation and renewable energy fields due to their high power density, stable cycling performance, and rapid charge–discharge capabilities. To ensure efficient applications of supercapacitors, accurately predicting their degradation trajectories and remaining useful life (RUL) is crucial. For this purpose, a physics-informed neural network (PINN) model is developed using Long Short-Term Memory (LSTM) as the base architecture. Physical equations are embedded into the loss function to ensure consistency with domain knowledge, allowing the loss function to incorporate both physical and data-driven components. The balance between these two loss components is dynamically determined through Bayesian optimization, to enhance the model's accuracy further. Validation results show a root mean square error (RMSE) of 3 ​mF (the rated capacity is 1 F) in the degradation trajectory prediction and a RMSE of 269 cycles (the average cycle life is 5180 cycles) for the RUL. Ablation experiments were conducted to validate the effectiveness of integrating physical information into the LSTM framework. Results demonstrate that the proposed model outperforms both the data-driven LSTM method and the empirical equation-based method that the PINN model can reduce the RMSE by 85% and 87.5% for degradation trajectory prediction, and 86.5% and 94.6% for RUL prediction, respectively. In addition, a comparison with advanced models demonstrates that our model reduces the requirement significantly on training data while maintaining comparable prediction accuracy, which favors scenarios where data is scarce. 

Lithium plating, triggered by low-temperature and high-rate charging, leads to capacity degradation and poses significant safety risks in lithium-ion batteries (LIBs). To ensure safe and efficient LIB operation, this study improves the impedance-based lithium plating detection method and proposes a non-destructive online detection method for lithium plating based on the lithium stripping mechanism. By monitoring changes in battery relaxation impedance during brief charging pauses after every 1 % increment in the state of charge (SOC), the onset SOC for lithium plating is accurately identified. The method is theoretically validated using an electrochemical-thermal coupling model and experimentally verified under both low and room temperatures, as well as under fast and slow charging conditions, through voltage relaxation profiles and dynamic electrochemical impedance spectroscopy. Furthermore, a stepwise intermittent charging (SIC) strategy is developed, leveraging the progressively decreasing current and current pause to mitigate lithium plating. The SIC strategy reduces capacity degradation by 85.7 % after 80 cycles compared to constant current charging at the same charging speed. This research offers practical insights for enhancing fast and safe charging technologies in LIBs, providing a foundation for real-world applications. 

Accelerated and accurate degradation diagnosis is imperative for the management and reutilization of commercial lithium-ion batteries in the upcoming TWh era. Different from traditional methods, this work proposes a hybrid framework for rapid and accurate degradation diagnosis at the electrode level combining both deep learning, which is used to rapidly and robustly predict polarization-free incremental capacity analysis (ICA) curves in minutes, and physical modeling, which is used to quantitatively reveal the electrode-level degradation modes by decoupling them from the ICA curves. Only measured charging current and voltage signals are used. Results demonstrates that 11 points collected at any starting state-of-charge (SOC) in a minimum of 2.5 ​minutes are sufficient to obtain reliable ICA curves with a mean root mean square error (RMSE) of 0.2774 Ah/V. Accordingly, battery status can be accurately elevated based on their degradation at both macro and electrode levels. Through transfer learning, such a method can also be adapted to different battery chemistries, indicating the enticing potential for wide applications.

The world's oceans, covering approximately 71 % of the Earth's surface, harbor vast wave energy resources, offering a potential solution to the pressing energy crisis and environmental pollution caused by fossil fuel combustion. In recent years, there has been a global surge in exploration and development of wave energy conversion technologies, aimed at effectively harnessing wave energy to realizing sustainable and intelligent sea solutions. This comprehensive review examines the advancements, challenges and future research directions of current mainstream wave energy conversion technologies. Firstly, the distribution of global wave resources and energy conversion process involved in wave energy extraction are analyzed. Subsequently, various wave energy conversion technologies are meticulously classified based on their power take-off systems, and the strengths and challenges of each category are comprehensively investigated. Especially, a universal standard consisting of 5 key indicators has been established to evaluate and compare the characteristics of various wave energy conversion technologies based on different transduction mechanisms, providing comprehensive and intuitive valid references for developers with different needs. The evaluation reveals that the wave energy converters based on hybrid systems demonstrate significant promise as conversion technologies. Moreover, the review presents a summary and analysis of the latest advancements in the application of artificial intelligence within wave energy conversion technologies. This emerging integration of artificial intelligence showcases promising development and potential for further enhancing wave energy conversion systems. Lastly, the review explores the application and future research directions of wave energy conversion technologies. Notably, the investigation highlights the potential of developing a multi-energy complementary power generation system that can concurrently harness multiple renewable energy sources coexisting at sea. This concept represents a promising avenue for future research and development.