Research progress on energy storage technologies of China in 2023 is reviewed in this paper. By reviewing and analyzing three aspects in terms of fundamental study, technical research, integration and demonstration, the progress on China's energy storage technologies in 2023 is summarized on the basis of comprehensive analysis, including hydro pumped energy storage, compressed air energy storage, flywheel, lithium-ion battery, lead battery, flow battery, sodium-ion battery, supercapacitor, new technologies, integration technology, firecontrol technology etc. It is found that important achievements in energy storage technologies have been obtained during 2023, and China is now the most active country in the world in energy storage fields on all the three aspects of fundamental study, technical research, integration and application, which maintained the first in terms of SCI paper number, filed patent number and installed capacity. Looking to 2024, energy storage technologies of China will very likely develop rapidly and need a high-quality development.

This bimonthly review paper highlights 100 recent published papers on lithium batteries. We searched the Web of Science and found 7512 papers online from Feb. 1, 2024 to Mar. 31, 2024. 100 of them were selected to be highlighted. The selected papers of cathode materials focus on high-nickel ternary layered oxides and Li-rich oxides, and the effects of doping, interface modifications and structural evolution with prolonged cycling are investigated. For anode materials, silicon-based composite materials are improved by modification of the micro-structure and optimized the component and binder for liquid and solid battery applications. Efforts have also been devoted to designing artificial interface and controlling the inhomogeneous plating of lithium metal anode. The relation of structure design and performances of oxide-based, sulfide-based and polymer-based solid-state electrolytes has been extensively studied, while different combination of solvents, lithium salts, and functional additives are investigated for the liquid electrolytes. For solid-state batteries, the surface coating of the cathode, the design of composite cathode, the interface to anode/electrolyte interface and 3D anode have been widely studied. Works on lithium-sulfur batteries are mainly focused on the structural design of the cathode and the development of functional coating and electrolytes, and solid state lithium-sulfur battery has also drawn large attentions. New binders and the dry electrode coating technology are developed for Li-ion batteries. Coating process, electrode and battery design are widely studied for solid-state batteries, and solid state Li-S batterydraws large attentions. There are a few papers for the production technologies of electrodes, characterization techniques of structural phase transition of the cathode materials and the composition of SEI, while theoretical papers are mainly related to the study of interfacial ion transport and the optimization of electrode structure.

Proton exchange membrane unitized regenerative fuel cells (PEM-URFCs) are considered to be one of the most ideal energy storage devices. In PEM-URFCs, the processes carried out in a fuel cell and water electrolysis are integrated. Bifunctional oxygen electrodes (BOEs) constitute the core structure of the oxygen reduction reaction and that of the oxygen evolution reaction; this is facilitated by preparing a slurry of Pt and IrO₂ materials. Since dispersing this catalyst slurry is difficult, this results in low catalyst utilization and poor durability. To overcome the above problems, in this study, the dispersion effect and stability of the Pt-IrO₂ slurry were improved by combining the HS-PEG-COOH stabilizer with the interval ultrasonic dispersion process. Subsequently, the BOE was prepared by using this slurry. The experimental results show that the BOE prepared in this study is rich in nanoscale pores, and has a higher catalyst utilization rate and better round-trip efficiency (RTE) than the BOE prepared by the continuous ultrasonic dispersion process. The polarization performance test results show that the RTE of the PEM-URFC assembled by the BOE prepared in this study can reach up to 51.12% under the working condition of 0.5 A/cm2, which is 9% higher than that of reached by the PEM-URFC assembled by the BOE prepared by the continuous ultrasonic dispersion process. Additionally, after 3000 accelerated experiments, it was found that the RTE of the PEM-URFC assembled by the BOE prepared by this study decreased by only 0.249%, which was lower than that of the PEM-URFC assembled by the BOE prepared by the continuous ultrasonic dispersion process by 1.675%. Hence, the proposed method of combining the HS-PEG-COOH stabilizer and interval ultrasonic dispersion technology to improve the dispersion effect of the slurry used to prepare the BOE contributes to the existing knowledge regarding optimizing the preparation process of the BOE.

To meet the current demand for high-specific capacity electrochemical energy storage materials in new energy generation technology, we prepared high-specific capacity layered lithium-rich manganese-based oxide (Li_1.2Ni_0.13Co_0.13Mn_0.54O₂) by optimizing the proportion of metal ions and acrylic acid in the precursor polymerization process using the polymer-pyrolysis method. Based on the polymerization reaction of acrylic acid to achieve uniform dispersion of metal ions, a Li_1.2Ni_0.13Co_0.13Mn_0.54O₂ cathode material was prepared by secondary heating and calcination. By changing the calcination temperature to prepare cathode material samples at different calcination temperatures, we studied the effect of calcination temperature on the microstructure and electrochemical performance. We employed testing techniques such as X-ray diffraction and scanning electron microscopy to observe differences in the microstructure and crystal structures of different material samples, energy dispersive spectroscopy to observe the distribution of elements in the materials, and the Xinwei battery testing system and electrochemical workstation to study the electrochemical performance of the prepared cathode material. The results show that the Li_1.2Ni_0.13Co_0.13Mn_0.54O₂ cathode material prepared at 925 ℃ has high crystallinity, obvious layered structure, low degree of cation mixing, and uniform dispersion of various elements. During the charge-discharge cycle test in the range of 2.0—4.8 V, the first cycle discharge-specific capacity reached 290.3 mAh/g at a rate of 0.1C. The discharge capacity remained at 204.8 mAh/g for 100 cycles at a rate of 0.5C, with a capacity retention rate of 81.9%, demonstrating good cycling stability. The prepared Li_1.2Ni_0.13Co_0.13Mn_0.54O₂ cathode material exhibits good electrochemical performance. This study promotes the application of lithium-rich manganese-based oxide cathode materials and provides an experimental basis for developing high-specific capacity cathode materials.

In this work, Li₂NiO₂ (LNO) is employed as a cathode prelithiation additive for lithium iron phosphate (LFP) cathodes, paired with a high-capacity graphite-doped silicon oxide anode, to investigate the effects and mechanisms of prelithiation technology on LFP batteries. The electrochemical performance, structural composition, and surface morphology of LNO were meticulously evaluated, revealing that LNO possesses an irreversible capacity of 212.1 mAh/g, primarily comprising active Li_0.63Ni_1.02O₂ postdelithiation. Subsequently, a 32 Ah pouch cell was fabricated by integrating 3% LNO into the cathode slurry, leading to a notable enhancement in the performance of the LFP batteries. Compared to the control group, the energy density and cycle life of the LFP batteries with LNO were increased by 4.9% and 50%, respectively. Moreover, the contribution of prelithiation technology to cycle stability was elucidated using a three-electrode method, revealing that an excessive quantity of cathode prelithiation additive could lead to the retention of active lithium ions on the anode. These ions are gradually released during cycling, ensuring a sustained replenishment of active lithium ions, thereby augmenting the cycle life of lithium-ion batteries. This study advances the application of cathode prelithiation technology in energy storage systems, providing both theoretical and experimental insights for the design and development of high-performance LFP batteries and facilitating the large-scale adoption of silicon-based anode materials.

The cathode film for lithium-ion batteries is fabricated using lithium iron phosphate powder on a production line. During the calendering process, the roll temperature is set to 25 ℃, 60 ℃, 80 ℃, 100 ℃, and 120 ℃, respectively, and the cathode film is rolled to a uniform thickness under consistent roll pressure. Subsequently, the cathode film is fashioned intoCR2032 button batteries for evaluation. Parameters such as thickness uniformity, film resistance, peeling force, thickness rebound, elongation, and scanning electron microscopy (SEM) analyses are assessed. Additionally, electrochemical impedance spectroscopy (EIS), 0.1—2C rate performance, and short-term 1C cycle performance are characterized to ascertain the impact of heated calendering on cathode film and cell performance, and to identify the optimal calendering temperature. Findings indicate that heated calendering does not affect the elongation of the cathode film; however, it enhances the uniformity of film thickness. The process also diminishes the resistance of the cathode film, with the optimal reduction observed at 100 ℃, approximately 2.1%. Moreover, heated calendering reduces thickness rebound, with the lowest rates at 60 ℃ and 100 ℃, approximately half of that at 25 ℃. The peeling force initially increases and then decreases with roll temperature, peaking near 60 ℃. SEM images reveal tighter binding of active materials postheated calendering compared to normal temperature processes, without grain breakage. Heated calendering effectively lowers both ohmic and charge-transfer impedance, with optimal results at around 100 ℃, correlating with film resistance observations. Enhanced rate performance is also noted in cathode films subjected to heated calendering, particularly at 100 ℃. However, no significant differences are observed in the cycle performance data over 100 cycles between cathode films produced via heated and normal calendering. Overall, heated calendering positively influences both film and cell performance, with the most beneficial effects at approximately 100 ℃.

A form-stable molten salt-based composite phase change material (PCM) with a low melting point suitable for low-medium-temperature thermal energy storage was prepared and investigated. A so-called cold compressing and hot sintering approach was used for the material preparation, in which a eutectic quaternary nitrate of NaNO₃-NaNO₂-KNO₂-LiNO₃ is utilized as the PCM, MgO as structure supporting material and graphite as thermal conductivity enhancer. A series of characterizations were performed to evaluate the composite microstructure, chemical compatibility, thermal properties, and cycling stability. The results showed that there was no chemical reaction among the quaternary nitrate, MgO, and graphite before and after sintering, indicating excellent chemical compatibility and stability in the composite. Among the different composite mass ratios, the 6∶4 quaternary nitrate-to-MgO mass ratio was optimal. In addition, the composite exhibited an excellent appearance when containing 8% graphite. A fairly low melting point of approximately 70 ℃ and a relatively high decomposition temperature of 610 ℃ were observed, offering the composite a large energy storage density exceeding 749 kJ/kg in the temperature range of 50—580 ℃. The thermal conductivity of the composite containing 8% graphite can be increased from 0.41 W/(m·K) to 0.77 W/(m·K). The composite exhibited excellent cycling stability after 150 heating-cooling cycles. This salt-based composite with a low melting point and a large temperature range is an excellent candidate for thermal energy storage, and this study offers a basis for its practical application in low-medium-temperature thermal energy storage fields.

This article focuses on the relationship between phase change materials (PCMs), fin structures, and various PCM volume ratio working conditions on the heat transfer performance of the phase change radiator. We developed a plate-fin phase change radiator model to study the phase change heat transfer process of cascade phase change technology and measures to optimize the heat transfer performance. The results show that changing the combination of PCMs significantly influences the heat transfer process of PCMs. For a cascaded phase change radiator with dual PCMs, PCM1 and PCM2 are arranged with decreasing phase change temperatures for optimal thermal management performance. Compared with 4-fin units, 16 fins yield lower operating temperatures and 25.3% and 22.5% shorter solidification times for the two cascaded PCM combinations of phase change cooling units. Moreover, the thermal management performance is optimal under the setting of 16 fins with a fin thickness of 0.5 mm. Using paraffin and low melting point alloy materials, when the PCM volume ratio is 1∶2, the thermal management times at two critical temperatures are prolonged by 17.2% and 15%, respectively, and the temperature rise rate also decreases gradually with an increase in the volume occupied by the low melting point alloy. Considering the thermal management time and temperature rise rate together, the thermal management performance of the cascaded phase change radiator is optimal when the PCM volume ratio is 1∶2.

Phase change energy storage materials are a type of high-efficiency energy storage materials that can be combined with building materials to achieve energy-saving effects. Reasonably developing and utilizing phase change energy storage materials is an effective way to optimize residential spaces and promote green development in the construction industry. The paper summarizes the concept, classification, and application value of phase change energy storage materials, and introduces the energy-saving principles of phase change energy storage materials in buildings; The paper also specifically explains the application fields of phase change energy storage materials.

The development of sodium-ion batteries with fast charge performance (ultra-high rate performance) is a research hotspot in the field of energy storage. Prussian blue analogues (PBAs), which have an open three-dimensional skeleton structure conducive to sodium-ion storage and diffusion, have great potential as anode materials for sodium-ion batteries. However, most existing PBAs have structural defects, crystal water, and poor electronic conductivity, resulting in unsatisfactory rate performance. In this study, the problem of poor electrical conductivity of PBAs is analyzed from two perspectives: ionic and electronic conduction. First, the influence of structural defects in PBAs, the presence of crystal water on ionic conduction, and the influence of the valence bond structure on electronic conduction are analyzed. Recent literature on improving the electrical conductivity of PBAs is also reviewed. ①Improving the crystal structure reduces the internal defects and crystal water of PBAs, thereby reducing the migration distance of sodium ions and obstacles. ②The design of special PBA crystal structures can effectively reduce the sodium-ion transport path. ③Composite conductive materials can build new electronic transport routes. In addition, three strategies to improve the conductivity of PBAs are described. Based on our analysis, to optimize the structure of PBAs and achieve optimal rate performance, composite conductive materials should be strengthened to enhance ionic and electronic conductance.

Li/CF _x battery, a primary battery with the highest theoretical specific capacity and energy density, has the advantages of high safety performance, low self-discharge rate, stable discharge voltage, and eco-friendliness and is widely used in the medical, military, electronic technology, aerospace, and other fields. Electrolyte, an indispensable component of Li/CF _x battery, plays a pivotal role in transferring ions between the anode and cathode; it has important research significance. In this review, we summarize the research progress on electrolytes for Li/CF _x batteries, especially emphasizing the relationships between electrolyte factors, such as the physicochemical properties of electrolytes, interface wettability and compatibility, lithium-ion solvation structure, C—F bond activation, LiF formation, and dissolution, and the electrochemical performance of Li/CF _x batteries, such as low-temperature performance, high-rate performance, and discharge voltage platform. Finally, future research directions for Li/CF _x battery electrolytes are discussed.

Aqueous zinc-ion batteries (AZIBs) have significantly shown application potential in the field of electrochemical energy storage due to their high theoretical specific capacity, high safety, low cost, simple manufacturing process, and environmental friendliness. Manganese/vanadium-based oxides are considered to be efficient cathode materials for zinc-ion batteries owing to their rich valence states, excellent structure, and high specific capacity. However, their disadvantages such as poor intrinsic conductivity, slow diffusion kinetics of zinc ions, and dissolution and structural collapse in electrochemical processes limit their further development and practical application. An effective way to alleviate the above problems is to combine manganese/vanadium-based oxides with different functional components to construct heterostructures. These heterostructures can achieve versatility and a synergistic effect by virtue of the characteristics of each component; further, they have heterogeneous interfaces with significant physical and chemical properties, thus achieving efficient zinc storage performance. Based on these developments, this review summarizes the main challenges faced by manganese/vanadium-based oxide cathodes, and focuses on the types, characteristics, preparation methods, and enhancement mechanisms of zinc storage properties of manganese/vanadium-based oxide heterostructures that have been reported in recent years. Finally, this paper discusses the future development of heterogeneous materials for cathodes of aqueous zinc-ion batteries and provides new insights for the development of electrode materials for high-performance zinc-ion batteries.

The escalation in the construction of new energy sources, such as offshore wind power and photovoltaics, has increased the demand for applications in DC transmission, AC-DC interconnection, and energy storage. Presently, research and applications in energy storage technology predominantly focus on AC energy storage. Although the modular multilevel converter based battery energy storage system (MMC-BESS) facilitates energy storage while interconnecting AC and DC networks, the presence of pulsating current components, including power and double frequencies, in the battery can adversely affect battery life. Moreover, the cost associated with retrofitting traditional modular multilevel converter (MMC) converter stations is significant. The proposed DC direct-mounted energy storage device decouples the converter and energy storage functions, ensuring that the battery current comprises only DC and high-frequency pulsation components, thus offering a battery-friendly operating environment. Furthermore, the DC direct-mounted energy storage system necessitates merely one-sixth the number of battery cells required by MMC-BESS, leading to cost reductions. This paper delves into the topology structure and operational principles of DC direct-mounted energy storage devices, designs the quantity and parameters of cascaded submodules, calculates the DC ripple current through carrier phase-shift modulation, and designs the parameters of the grid-connected inductance. It also establishes the mathematical model of the DC energy storage device, derives the control model, and implements power control based on the control diagram. The feasibility and accuracy of the cascaded half-bridge topology in DC direct-mounted energy storage devices are corroborated through simulation and prototype experiments. The experiments demonstrate the effectiveness of the design and control methods, offering valuable insights for the design of high-voltage and large-capacity DC energy storage devices.

In DC microgrids based on photovoltaic and energy storage systems, the bidirectional DC-DC converter is a key interface between the energy storage equipment and DC bus, and its performance under the large signal disturbance of photovoltaic output power and load directly affects the stable operation of the DC bus. Therefore, a nonsingular terminal sliding mode control strategy based on a nonlinear disturbance observer is proposed in this study. First, the state-space model of the bidirectional DC-DC converter system is transformed into Brunovsky's standard form using the exact feedback linearization method based on differential geometry theory, and the disturbances are estimated using the nonlinear disturbance observer as the compensation channel. Moreover, we design a nonsingular terminal sliding mode controller and analyze the stability, as well as convergence, of the proposed control strategy based on Lyapunov stability theory. Finally, we perform simulations to prove the effectiveness of the proposed control strategy. The simulation results show that compared with typical nonlinear control methods in the literature, the proposed control strategy has a fast response time, small overshoot, and steady-state error under the large signal disturbance of photovoltaic output power and load.

Since key equipment of new energy technology is connected to the power grid, grid-forming energy storage converters have stability problems in strong power grids. Aiming to resolve this problem, this study proposes a method for stability optimization of grid-forming energy storage converters based on virtual bus voltage control. First, the state space small signal model of a grid-forming energy storage converter is established, and the characteristic root analysis method is used to obtain the stability conditions of the grid-forming energy storage converter that becomes worse or even oscillates under strong power grid conditions. Subsequently, a stability optimization method based on virtual bus voltage control is proposed. By changing the position of the active power loop tracking the grid voltage phase, the grid impedance is equivalently improved, thus improving the stability of the grid-forming energy storage converter system under strong power grid conditions. Subsequently, the eigenvalue change trend and stability improvement effect of the converter system before and after adding the virtual bus voltage control under different working conditions are analyzed. Simultaneously, the influence of the compensation coefficient k and the time constant t in the virtual bus voltage control parameters on the stability of the converter system is analyzed and the general parameter design range is provided. Finally, a grid-forming model of the grid-forming energy storage converter is built in MATLAB/Simulink for time-domain verification, and a hardware-in-the-loop experimental platform is further built for semi-physical verification. The study results show that the stability of the grid-forming energy storage converter based on virtual bus voltage control is significantly improved under strong power grid conditions.

Current research on high-power, large-capacity flywheel energy storage systems remains insufficient. This study focuses on a newly developed prototype of a MW/100 MJ flywheel. We analyzed the structural mechanics of both built-in and surface-mounted flywheel motor rotors, assessed the impact of different dynamic balance block materials on stress and deformation, and performed a dynamic characteristics analysis of the shaft system. Experimental validation of the flywheel prototype was conducted to ascertain system stability. Findings from numerical calculations suggest that the surface-mounted design substantially reduces stress on the silicon steel sheet, although this configuration typically necessitates a carbon fiber reinforced layer to prevent the magnetic steel from detaching from the silicon steel sheet under centrifugal forces during operation. Stress values increased by over 45% when using stainless steel for the dynamic balance block compared to aluminum alloy. The shaft system's dynamic analysis revealed two rigid vibration modes at operational speeds of 1300 r/min and 4200 r/min, corresponding to translational and conical movements, respectively. Experimental observations confirmed a peak vibration at 1300 r/min, corroborating the numerical simulations. However, the anticipated critical speed (conical motion) at 4200 r/min did not manifest as a significant peak in actual tests, indicating that translational vibration modes are more prone to excitation in this shaft configuration.

Based on the current demand for energy research, this paper reviews the research status of micro electromechanical control technology for compressed air energy storage systems. Firstly, the internal operating logic of the compressed air energy storage system was elaborated in detail, including the operating mode and related mechanical structures. On this basis, this paper explores the research status of micro electromechanical control technology for energy storage systems from the perspectives of speed and energy storage motors. Finally, the control optimization research of relevant technologies was listed, providing a certain foundation for the practical development of control technology for compressed air energy storage systems in the future.

The electrothermal carbon dioxide energy storage and liquid carbon dioxide energy storage systems have the characteristics of a wide application range and high energy storage density; hence, they are research hotspots in the field of compressed gas energy storage technologies. However, the differences in energy storage forms and processes of the above systems result in differences in the energy storage efficiency and energy density of these systems, which have not been systematically studied to date. Consequently, in this study, the energy storage principle and process configuration characteristics of the abovementioned systems are described. Subsequently, a thermodynamic analysis model is established to characterize the performance indexes and irreversible loss distribution characteristics of these systems under the set working conditions. Further, the influence of key parameters on the performance of these systems is discussed. The results show that the electrothermal carbon dioxide energy storage system converts the pressure energy into cold energy of working medium to store it, obtaining a higher energy storage density (7.36 kWh/m³) as compared to that of the liquid carbon dioxide energy storage system; additionally, the liquid carbon dioxide energy storage system directly stores the pressure energy through a high-pressure tank to avoid additional turbomachinery loss, thus showing a higher energy storage efficiency (63.60%) as compared to that of the electrothermal carbon dioxide energy storage system. Additionally, this study shows that increasing the isentropic efficiency and outlet pressure of the compressor significantly improves the performance of the liquid carbon dioxide energy storage system. Further, it shows that increasing the isentropic efficiency leads to the improvement of electrothermal carbon dioxide energy storage system. Hence, this study's results provide technical support for carbon dioxide energy storage technology path selection and performance improvement.

In recent years, energy storage technology has become an effective means of smoothing wind power fluctuations and improving the acceptance capacity of wind power in the grid; however, wind power fluctuations are complex and variable, and a single energy storage mechanism does not have the required high energy and high power density characteristics. Therefore, to solve the problem of wind power generation power smoothing in terms of its stochastic gap and other typical characteristics, this study intends to use a hybrid energy storage technology, that combines the advantages of lithium-ion batteries and supercapacitors, to design a two-layer power decomposition and allocation strategy based on the adaptive sliding average filtering-wavelet packet decomposition; further, it aims to utilize the sliding average filtering algorithm to complete the adaptive decomposition of wind power, and obtain the minimum power required for grid-connected power and hybrid energy storage systems. The minimum power required for grid-connected power and hybrid energy storage systems is obtained. Considering that the output power of a hybrid energy storage system continues to contain rich information and the rules for determining the number of wavelet packet decomposition layers and the power cut-off point, the wavelet packet algorithm is used to decompose the suppressed wind power twice and optimize the power allocation of the hybrid energy storage system by combining with the inverter entropy value strategy. This improves the fluctuation of the wind power effectively and realizes the optimal decomposition and reasonable allocation of hybrid energy storage power. Comparative analysis of different power allocation methods uses evaluation indexes such as the fluctuation of the national standard time scale, fluctuation before and after the introduction of energy storage, power amplitude, charging and discharging times, etc. The results show that the proposed method can meet the requirements of national standards for wind farm power fluctuation, reduce the impact of fluctuations on the grid, and show better adaptability compared with the traditional wavelet packet decomposition algorithms, with the overall fluctuation situation being improved by 23.69%. The proposed method reduces the lithium battery charging and discharging times by 58.2%. The model evaluation results show that the proposed method can improve the overall economy of the hybrid energy storage system and extend the life of lithium batteries.

With the rapid expansion of internet data centers (IDCs), the deployment of uninterruptible power supply (UPS) systems in IDCs has seen significant growth. Despite their critical role, UPS battery systems remain largely underutilized, being active only during infrequent power outages. This paper advocates for the enhanced utilization of the energy storage capabilities of UPS battery systems, promoting an economical and efficient approach to energy management without compromising backup power reliability. We introduce an advanced architecture for energy storage type of UPS (EUPS), delineate control strategies for its diverse energy storage applications, and present a framework for its integration into multiscenario power grid regulations through coordinated control strategies. The efficacy of the proposed EUPS system architecture and control methodologies is substantiated through a fully operational EUPS demonstration system.

Considering the increasing importance of wind and solar energy, the power system increasingly faces prominent energy regulation pressure. Presently, energy storage, as an important supporting factor for building the new power system, faces problems such as high configuration costs and insufficient utilization. Based on Clausius entropy theory, this study constructs a physical quantity, power entropy, to quantify and evaluate the energy-time decoupling ability of an energy system. Taking an energy storage system as an analysis case, this study systematically studies the internal relationship between power entropy and key decision variables of energy storage configuration, such as power, duration, and capacity. It is found that there is a consistent positive correlation between power entropy and a variety of key decision variables, that can measure the comprehensive regulation performance of energy storage; additionally, power entropy also reflects the timing characteristics of energy storage. Based on these characteristics demonstrated by power entropy, this study establishes a power entropy-based optimization analysis method for power system energy storage configuration, and verifies the role of power entropy in energy storage configuration optimization through simulation case analysis. The results show that under different energy storage configurations, the increase in system power entropy is different; additionally, they show that the direction of system optimization is the direction in which the increase in power entropy introduced by energy storage decreases gradually, that is, the direction in which the redundancy of energy storage regulation performance decreases gradually. Thus, the concept of power entropy provides a new path for the optimization of energy storage configuration in future power systems and also for the optimization of multiple energy forms in the integrated energy system.

In order to alleviate the peak of urban electricity consumption, the paper puts forward the idea of building a large air conditioning power storage system in the suburbs of the city. On the one hand, the system can store the electricity in the suburbs and transmit it to the urban area during the peak period; on the other hand, it is equipped with solar panels to absorb the solar energy and increase the urban power supply. In the aspect of intelligent air conditioning system design, this paper proposes the intelligent control system of air conditioning based on thermal comfort and temperature big data, and realizes the optimal design of air conditioning energy consumption combined with the decision algorithm.

To mitigate the risk of fires in containerized lithium-ion battery energy storage systems, we propose an early warning method for fire safety. This method involves analyzing the heat generation process in such systems and establishing the correlation between temperature, voltage, and the fire safety of the storage system. By collecting operational data, including voltage, current, temperature, and acoustic signals, we assess the system's surface health and predict its operational trends based on changes in the operational data and surface health status. Considering various influencing factors, we establish early warning levels for the system's fire safety and implement a comprehensive early warning system. Performance testing demonstrates that, compared to conventional warning methods, our optimized approach significantly reduces the rates of false positives and negatives by more than 0.90%. Furthermore, the method exhibited only one warning error across different warning levels, and achieved a warning accuracy of 99.7% across various surface damage locations, proving its effectiveness and reliability.

Thermal storage systems can effectively alleviate the power generation problems caused by the randomness and volatility of solar energy, and are currently one of the most important systems for the operation of photovoltaic power plants. However, due to the frequent switching of working states in daily applications, it is prone to data anomalies. The introduction of big data and the design of big data based thermal storage systems are important attempts to solve their problems. The article first analyzes the latest research and application theories of thermal storage system models, and takes the Andasol solar thermal storage system in the United States as an example to focus on the design and construction principles of its model, as well as the calculation method of energy balance. Finally, from two aspects: network independence testing and fusion model data diagnosis, the important application of big data technology in the security of thermal storage model systems is further elaborated.

For facilitating the achievement of carbon neutrality by 2060, the corresponding huge demand for energy storage needs to be met; however, the outputs of conventional pumped storage sites in China are insufficient to meet this demand. To solve this problem, this paper reviews the research on underground pumped storage in the United States, Russia, Singapore, Japan, and other countries. Subsequently, it proposes a low-cost underground pumped storage scheme based on hard rock boring machine (TBM) excavation. Further, it expounds the development status of three different types of underground pumped storage, namely, underground pumped storage with artificial excavation of underground space, underground pumped storage with abandoned mine reconstruction, and other underground (sea) pumped storage type. This paper introduces the key technologies and challenges associated with underground pumped storage, including the current situation of underground engineering construction and operation, and the development status of high-head pump turbine technology. Finally, this paper discusses the challenges of developing underground pumped storage, and proposes suggestions to prioritize the development of underground pumped storage with artificial excavation of underground space, further proposing key technologies for implementing this technology. This review shows that underground pumped storage is technologically feasible, economically feasible, and has obvious advantages. Thus, the authors of this paper suggest that China should strengthen the research and development, promotion, and application of underground pumped storage technology to achieve the carbon neutrality goal.

Lithium metal anodes have attracted extensive research attention because of their high theoretical capacity (3860 mAh/g) and low redox potential (-3.04 V vs. SHE). However, the current practical application of lithium metal anodes still faces various challenges, among which the problem of lithium dendrite growth is notable. Among several solution strategies, electrolyte optimization is a promising strategy for inhibiting lithium dendrite growth because of its simple preparation process, strong system compatibility, low cost, and remarkable effect. In this study, we summarize several lithium dendrite growth models, including the solid electrolyte interface diffusion model, surface nucleation growth diffusion model, charge induction model, and space charge model, focusing on the model basis for electrolyte regulation strategies to inhibit dendrite growth. As a result, the physical and chemical properties of the electrode interface layer (SEI) determine the deposition behavior of metallic lithium, and the SEI composition, mechanical properties, and desolvation process are affected by the electrolyte components. Next, the research progress on electrolyte optimization strategies is systematically reviewed, mainly considering film-forming additives, solvent-regulated SEI additives, charge-induced additives, alloy additives, high-concentration salt electrolytes, and local high-concentration salt electrolytes. The advantages and disadvantages of various optimization strategies are summarized. Finally, future research directions for electrolyte optimization strategies are suggested.

The cascaded utilization of batteries is an economic and efficient metho d for handling retired lithium batteries. However, during the process of cascaded utilization, some issues may arise due to the imbalance caused by the simultaneous use of batteries of different ages and conditions, affecting the safe and stable operation of the battery system. This study analyzes the issues of current loops and low battery system capacity utilization caused by the imbalance arising when the batteries prepared for cascaded utilization are directly paralleled. To address these issues, this study proposes a battery system in parallel and a balancing control algorithm based on power converters. In this approach, each battery module is connected to a power converter to form a power module. By adjusting the output of the voltage loop, current loop, and state-of-charge loop, the charging and discharging currents of each battery module are controlled independently, achieving balanced and optimized control for cascaded utilization of batteries. This study provides a detailed analysis of the control strategy and discusses experimental results to evaluate and validate the effectiveness of the proposed balancing method. The experimental test results demonstrate that the proposed balancing control strategy can effectively and stably control the currents of different batteries, ensure balanced state-of-charge parameters between batteries, and improve the overall efficiency of the battery system. Thus, this study promotes the effective cascaded utilization of retired batteries and provides new ideas for implementing balancing control in battery systems. By achieving balanced utilization of batteries, their lifespan can be extended, the risk of system failure can be reduced, and the reliability and stability of the battery system can be improved.

Rapidly obtaining accurate information about the remaining useful life (RUL) and health status of a lithium battery is critical to maintaining its reliability. To solve the problems of low prediction accuracy regarding the RUL of lithium batteries, unsatisfactory hyperparameter optimization results, and poor prediction effect of the traditional Gaussian process regression (GPR) model, in this study, the Antlion optimization algorithm was used to optimize the hyperparameters of Gaussian process regression (hereinafter referred to as "ALO-GPR") to accurately predict the RUL of lithium batteries. First, according to the cycle curve of battery voltage during battery charging, six parameters were extracted as the health factors of the battery; subsequently, the correlation between these factors and the battery capacity was verified by using the Pearson correlation coefficient. Finally, the following four parameters were selected as the health factors: the average discharge voltage, the amount of charge amount stored by the battery in the constant current charging stage, the amount of charge stored by the battery in the whole charging stage, and the discharge temperature in the time integral. Finally, support vector regression, GPR, and ALO-GPR were used to predict the RUL of lithium batteries, and various indicators were compared and analyzed. The model proposed in this study is compared with models proposed in other literatures. The effectiveness of the proposed model is verified by using the NASA lithium battery dataset. The experimental results show that the RUL prediction model of ALO-GPR has a small error; the root mean square error is controlled within 1%; and, the average absolute error is controlled within 0.65%. Thus, ALO-GRP shows strong generalization and a good application prospect regarding the prediction of RUL of lithium batteries.

Proton exchange membrane water electrolysis (PEMWE), recognized as an efficient and eco-friendly hydrogen production technology, has garnered significant attention for its potential to expedite the transition toward a sustainable energy framework. Nonetheless, the widespread adoption of PEMWE is hindered by factors such as cost and longevity. Addressing these challenges necessitates foundational research, particularly in the realm of insitu electrochemical characterization. Cyclic voltammetry (CV) testing emerges as a pivotal technique in this context, offering insights into critical electrochemical parameters including the electrochemical active surface area (ECSA) and catalyst efficiency, among others. The requirement for humidified gas during CV testing presents a notable obstacle for PEMWE applications. This study systematically investigates the conditions requisite for effective PEMWE CV testing, aiming to establish a more feasible methodology for conducting these assessments.

Accurate prediction of lithium-ion battery capacity degradation trajectories enhances the efficiency of battery materials research. Aiming to resolve the challenges associated with the Transformer network in the prediction of lithium-ion battery capacity degradation trajectory, this study adopts the sliding window strategy and constructs a lithium-ion battery capacity degradation trajectory prediction method based on Informer, a time series forecasting model. First, the sliding window is used to divide and re-splice the dataset; this facilitates the neural network to exploit the correlation within the dataset; subsequently, the global timestamp applicable to lithium-ion battery data is designed according to the periodic time series capturing ability of Informer; finally, the model output is realized through the multistep rolling prediction method by using the first 10% of the battery capacity data to alleviate the error accumulation in the prediction, subsequently obtaining the complete prediction trajectory. The accuracy and training efficiency of the established model are verified using the lithium-ion battery dataset provided by the University of Maryland. Different error evaluation and time overhead metrics are selected in the training process; additionally, the generalizability of the model is verified using the lithium-ion battery dataset provided by NASA. Comparing the prediction results of the model in this study with that of the multilayer perceptron neural network, recurrent neural network, and Transformer network model, the following is observed: the degraded trajectories obtained in this study are best fitted to the real trajectories; the training time overhead is small; and, the average absolute and root mean square errors of the prediction results are controlled at 2.57% and 3.5%, thus verifying the validity of the proposed prediction method.

In pursuit of high-precision and robust state monitoring of lithium-ion batteries under an environment with rapid temperature fluctuations, we propose a state-of-charge (SOC) estimation method based on an improved battery model and an optimized adaptive cubature Kalman filter (CKF). First, the discrepancies in SOC definition between a pseudo-two-dimensional electrochemical model and an equivalent circuit model are discussed. Introducing the improved battery model, the SOC results from the equivalent circuit model, calculated by ampere-hour integration, are rectified using intermediate variables. Subsequently, model parameters influenced by environmental temperature are identified from open-circuit voltage and dynamic stress test data under various constant-temperature environments. Moreover, the traditional CKF is optimized based on principles of matrix diagonalization and adaptive covariance matrix, bolstering overall stability and the ability of the proposed SOC estimation method to handle random sampling noise. Finally, experimental validation under six diverse battery operating conditions in rapidly temperature-varying environments demonstrates the accuracy of the established improved battery model and the effectiveness of the proposed SOC estimation method, even under random sampling noise. The results demonstrate the versatility of the proposed SOC estimation method across various battery operating conditions in rapidly temperature-varying environments, with an estimated root mean square error of approximately 1.3% under random sampling noise.

The prediction of state-of-charge (SOC) in batteries in data-driven models relies on high-quality experimental data. However, when considering lithium battery packs with diverse distributions in real-world scenarios, the accuracy of prediction of SOC in these packs becomes unstable, leading to poor generalization ability and limiting the practical application of the models. Therefore, it is of great significance to investigate the impact of the diverse distribution of large-scale datasets from actual operating scenarios on the generalization ability of SOC prediction models. Consequently, in this study, we conducted research on 32 datasets of operational data associated with lithium battery packs. Classic algorithms were combined with a multi-input multi-output (MIMO) strategy to predict multi-step SOC. Separate models were established for each dataset to analyze the application effects of different algorithms and the influence of data distribution diversity on model generalization ability. The results demonstrated that for large-scale lithium battery pack datasets, the LR-MIMO model generally exhibited higher training accuracy compared to the RF-MIMO, KNN-MIMO, and LSTM-MIMO models. The LR-MIMO model achieved R² values above 0.98 and MAPE values below 0.05 for predicting SOC in the next half hour. Compared to other models, the LR-MIMO model demonstrated excellent predictive performance, with R² values above 0.95 for predicting other datasets. The prediction accuracy of the KNN-MIMO model is comparable to that of the RF-MIMO model, with R² values roughly above 0.7, while the prediction performance of the LSTM-MIMO model differs significantly compared to other models due to the use of different datasets. The accuracy of the model can be improved when the data satisfies certain conditions, such as a correlation coefficient between SOC and voltage exceeding 0.9, a wide range of SOC and voltage distributions, a left-skewed kernel density curve, and a relatively uniform distribution.

Wearable devices (WDs) with small sizes and long working time are widely used in industrial monitoring and other fields. Lithium-ion batteries provide energy for electronics used in WDs, and their online accurate estimation of state of energy (SOE) critically impacts the real-time power management and life extension of WDs. Traditional model-based estimation methods must obtain the offline relationship between SOE and open circuit voltage (OCV). However, this requires a large amount of test time and is challenging to adapt to actual working conditions, thus hindering its online application. This paper proposes an SOE estimation method based on the online identification of OCV for lithium-ion batteries used in WDs. Based on the first-order RC model of the battery, the forgetting factor recursive least squares is used to identify the OCV online and other parameters of the lithium-ion battery. After analyzing the characteristics of load changes in the WD operation, the working condition and parameter identification condition are constructed, and the experiments are conducted on the test bench. Combined with the workload characteristics of WDs, the relationship between OCV and terminal voltage is discussed, and the relationship curves between OCV and SOE are obtained online. The adaptive unscented Kalman filter is used to estimate the SOE online and is compared with the traditional method based on the offline OCV-SOE relationship. The results show that the proposed SOE estimation method based on OCV online identification has good accuracy and robustness against different initial values.

Calendering is a crucial step in the electrode preparation process for lithium-ion batteries, significantly impacting the battery's consistency and safety. Given that the electrode is a composite material comprising a current collector (metal) and a coating (nonmetal), the complexity of the calendering deformation mechanism is heightened. In this study, we employed the discrete element method (DEM) model to characterize the coating component of the cathode electrode in a lithium battery, simulating the electrode calendering process numerically. Cathode electrodes were prepared, and experiments with varying calendering degrees were conducted. The congruence between the numerical simulations and experimental outcomes validates the model's accuracy. This research delves into the electrode's morphological evolution throughout the calendering process, unveiling the fundamental nature of calendering deformation. It also quantifies the load at the interface between the coating and the current collector during calendering, providing an in-depth interface analysis. The findings indicate that the DEM model can effectively simulate the microstructural evolution and actual mechanical behavior of the electrode. As the rolling reduction increases, the maximum stress on the current collector exhibits a linear rising trend. The enhanced compactness among the active particles in the coating is identified as the primary cause of calendering deformation. Some active particles in the coating become embedded in the current collector's surface, causing noticeable plastic deformation and stress concentration. This investigation offers innovative research perspectives and valuable insights for further exploration of the electrode calendering process.

To improve the efficiency of compressed air energy storage systems and achieve efficient utilization of energy and gas storage resources, a mathematical model for thermodynamic improvement of compressed air energy storage gas storage is proposed. This model is based on the fundamental principles of thermodynamics, describing the behavior of air compression, gas storage heating, heat storage, and heat release in gas storage, and considering various possible thermodynamic improvement methods. Through simulation and analysis, the model reveals the thermodynamic characteristics and efficiency issues of compressed air energy storage systems, providing theoretical support for the design and improvement of actual systems.

Solid oxide fuel cells (SOFCs) represent an efficient power generation system capable of directly converting chemical energy into electricity. However, ensuring the high efficiency and stable operation of SOFCs is a pressing issue. This study establishes a three-dimensional model for a single-piece planar SOFC, involving the coupling of various physical fields such as electrochemistry, mass transfer, and heat transfer. The model is subjected to numerical calculations and validation using the COMSOL Multiphysics finite element simulation software, with the I-V curve indicating a data error of less than 6%. Building on this model, a three-dimensional model of an SOFC based on cathode and electrolyte support is developed. This study further investigates the impact of different operational parameters and support structures on factors such as output power and temperature of SOFCs. The effects on the cell are reflected through polarization and power curves. The simulation results show that connecting rib plates can influence the diffusion of gases within the cell, revealing a close correlation between current density and material distribution. Further research indicates that factors such as support layer thickness, pressure, and input fuel flow rate can affect the cell's output power and temperature. Increasing the operational pressure and fuel input flow rate can enhance the output power; however, this also leads to an increase in the internal temperature of the cell, with the output power being inversely proportional to the thickness of the cell support layer. Under identical conditions, the output power of cathode-supported SOFCs is greater than that of electrolyte-supported SOFCs. Hence, this study provides some guidance regarding the structural design and experimental work related to SOFCs.

The bionic spider cooling structure is widely employed in dissipating heat from high heat flux chips, yet it faces challenges with uneven temperature distribution. To enhance the temperature uniformity within the spider web's thermal structure, this study introduces variable density topology optimization to refine the design. The optimization process aims to minimize temperature variance across the design domain, utilizing Holmz density filtering for numerical stability and hyperbolic tangential projection to define clear flow paths. Analysis of the heat transfer efficiency in topologically optimized flow paths, considering various inlet and outlet configurations and shapes across different design domains, revealed that a staggered multi-inlet and multi-outlet arrangement significantly enhances temperature equalization. Additionally, it was observed that the average temperature of the topological channel increases when the design domain comprises fewer than 10 edges. Comparative simulations using the finite element analysis method were conducted on a traditional structure (M1) and three-dimensional topological reconstructions (M2 and M3) with varying edge numbers in the design domain. The results demonstrated superior temperature uniformity in M3, which has 10 edges, compared to M1 and M2 with 6 edges. At a Reynolds number (Re) of 1800, M3 exhibited an 18.48% reduction in thermal resistance and a 25% decrease in the heat source surface temperature difference compared to M1, achieving a performance evaluation criteria value of 1.22. This study not only validates the effectiveness of the topology optimization method in enhancing the thermal structure's temperature equalization but also advocates for its broader application in thermal management systems.

The high penetration of renewable energy sources such as wind and solar will enhance the randomness and volatility of power output, leading to frequent occurrences of wind/solar curtailment. Hydrogen production from wind/solar curtailment is an effective means to handle the deep consumption of renewable energy. In this study, we propose a gated recurrent unit algorithm based on Adam optimization for predicting wind/solar curtailment using Latin hypercube and synchronous backpropagation reduction algorithms to generate typical wind/light generation uncertainty scenarios. A double-layer objective function is constructed to minimize system construction and operating costs. The first layer determines the capacity configuration of the alkaline electrolysis cell (AWE) and battery energy storage system (BESS), and the second layer ensures the system's optimal operation in the generated scenario. In addition, a penalty term is introduced to maximize the abandoned power use. Considering the wind/solar curtailment data from a certain region in northwest China as an example, different structures of neural network algorithms were used to predict the amount of wind/light curtailment. The accuracy of the proposed algorithm was verified by comparing the root mean square errors of the various algorithms. Finally, an optimization configuration analysis was performed on three energy storage schemes. The results showed that using only the BESS system resulted in negative annual profits and the highest annual investment cost. Using the BESS-AWE hybrid energy storage system increased the annual investment cost by 15.66% compared with using only the AWE system, but the annual profit increased by 255%, and the abandoned power usage rate was 92%, effectively improving the system's economy.

This article mainly studies the optimization configuration problem of distribution network operation with the participation of photovoltaic energy storage coupling in peak shaving. With the widespread application of photovoltaic power generation in distribution networks, how to effectively utilize photovoltaic power generation and solve its randomness and intermittency problems has become crucial. By introducing energy storage systems, the optimal configuration of photovoltaic power generation and energy storage can be achieved, improving the stability and economy of the distribution network. Firstly, the energy supply relationship of the "photovoltaic energy storage" distribution network is analyzed, and then the implementation effect of the peak shaving effect of the distribution network coupling is determined. Finally, the relevant electric energy storage equipment is adjusted to achieve the operation optimization of the "photovoltaic energy storage" distribution network.

The legal governance measures for fire safety in electrochemical energy storage power stations aim to ensure the fire safety of the power station through legal means, in order to prevent the occurrence of various unsafe events. Firstly, conduct research on electrochemical energy storage laws, related legal issues, and various energy storage policies; Secondly, analyze the basic principles of electrochemical energy storage, and determine the energy supply performance of electrochemical energy storage stations based on the cyclic conversion mode of electrical and chemical energy in energy storage stations, in order to carry out targeted fire safety supervision; Finally, identify the loopholes in fire safety laws and propose corresponding governance and rectification suggestions. On the one hand, ensure the integrity of the legal system, and on the other hand, ensure the fire safety of electrochemical energy storage power stations.

The development of new energy vehicles in China is a strategic move to transform the automotive industry, enhancing its resilience and vitality. With policy and market incentives, the development of new energy vehicles in China is shifting towards a product-driven approach. The focus is on gaining technological advantages in core areas such as motor, battery, and electronic control, and driving the development of new energy vehicles towards intelligent and connected vehicles. This article outlines the role and application space of cloud computing in the manufacturing of new energy vehicles, providing technical references for the intelligent upgrade of whole vehicle based on energy consumption reduction and safety optimization, and building a solid foundation for the high-quality development of new energy vehicles.

In the next twenty years, it is crucial for the development of China's new energy vehicle power battery industry. Research on breakthroughs in key battery technologies should be carried out, and breakthroughs in battery system shortcomings should be implemented to create a feasible path for the scale, digitization, and intelligence of the battery industry. This article analyzes the background and requirements of intelligent manufacturing of power batteries, outlines the battery manufacturing process goals with high strength, high safety, lightweight, and low cost as the goals, and proposes ideas and strategies for intelligent manufacturing of new energy vehicle batteries from the perspectives of closed-loop control architecture, intelligent digital dictionary, knowledge management and decision-making. The aim is to optimize the process, achieve more efficient production control, and higher quality processing technology, further promote innovative production of power batteries.