This paper provides a comprehensive review of the research progress in China's energy storage technology in 2024. By reviewing and analyzing fundamental study, technical research, and integrated demonstration, the major technological advancements in China's energy storage field in 2024 are summarized. These encompass pumped hydro storage, compressed air energy storage, flywheel energy storage, lead-acid batteries, lithium-ion batteries, flow batteries, sodium-ion batteries, supercapacitors, emerging energy storage technologies, integration technologies, and fire safety technologies. A comprehensive analysis indicates that China's energy storage sector has once again experienced a year of rapid development, with significant achievements made in fundamental research, key technologies, and integrated demonstrations. China maintains its position as the most active country globally in all the three fields of fundamental research, technology development, and integrated demonstrations in energy storage. Chinese institutions and scholars rank first in the world in the number of SCI-indexed publications, WIPO international patent applications, and the newly added installed capacity in the energy storage field. Moreover, the installed power of new energy storage technologies has surpassed that of pumped hydro storage for the first time, marking a historic milestone. Overall, China's energy storage has achieved large-scale development in 2024.

All-solid-state lithium batteries (ASSLBs) are promising candidates for next-generation energy storage systems because of their wide operating temperature range, high energy density, and high power density. The sulfide solid electrolyte Li₁₀GeP₁₂S₂ (LGPS) has attracted significant attention for its exceptionally high lithium-ion conductivity (1×10^-3 S/cm). However, the thermal stability of key materials in LGPS-based ASSLBs under high energy density configurations has not been reported. This study examines the thermal runaway mechanisms of LGPS solid electrolytes combined with a LiNi_0.92Co_0.04Mn_0.04O₂ (NCM92) cathode and silicon-carbon anode. Differential scanning calorimetry and simultaneous thermal analysis-mass spectrometry were employed to analyze heat generation and gas evolution in solid electrolytes, electrodes, and their mixtures. Following thermal analysis, scanning electron microscopy combined with energy-dispersive spectroscopy, X-ray diffraction, and X-ray photoelectron spectroscopy were used to characterize the reaction products at different temperatures. At 200 ℃, the NCM92 cathode undergoes a phase transition, releasing a significant amount of oxygen that reacts mildly with the sulfide electrolyte to form P₂Sₓ and trace SO₂. At 310 ℃, the LGPS and the cathode mixture exhibited exothermic reactions, producing metal oxides, sulfides, and phosphates. This study elucidates the thermal stability of key materials in NCM92|LGPS|SiC ASSLBs, providing theoretical guidance for material selection and safety-oriented design optimization.

Thermal management systems play a significant role in the safety, comfort, and energy efficiency of electric vehicles (EVs). Effective thermal management systems for EVs require appropriate control strategies, especially when multiple conflicting control objectives are involved. Herein, we review the existing control strategies used in EV thermal management systems and propose a novel multi-objective model predictive control (MPC) strategy for the optimal operation of thermal management systems. First, we developed a comprehensive numerical model of an EV thermal management systems. Next, we established the MPC strategy enhanced by the NSGA-II algorithm to simultaneously optimize temperature control in the cabin and batteries, as well as the energy consumption. Finally, we assessed and compared the impacts of different control strategies on the performance of EV thermal management systems under various driving conditions. The results demonstrate that under different working conditions, the proposed MPC strategy can effectively control the temperatures of both the cabin and batteries, thereby reducing their fluctuations and ameliorating the effects of significant changes in driving conditions on battery temperature. In addition, the MPC strategy can effectively reduce energy consumption, achieving energy-saving rates of approximately 4%—15% and 1%—6% compared with the threshold control and PID control strategies, respectively.

Lithium manganese iron phosphate (LiMn_1-y Fe _y PO₄), which has a higher energy density than conventional lithium iron phosphate (LiFePO₄), is expected to provide enhanced endurance and higher output power for electric vehicles and portable electronics. However, the Jahn-Taylor effect associated with manganese and the consequential dissolution problems have yet to be resolved, which has severely hindered the widespread commercialization of LiMn_1-y Fe _y PO₄ as a cathode material. In this research, we introduced a core-shell structural-design strategy that resulted in the successful fabrication of two composite materials: LMFP55/C and LMFP64/C. These composites feature a unique configuration, with LiFePO₄ particles tightly and uniformly encapsulated on the surface of a LiMn_0.7Fe_0.3PO₄ core. This innovative approach effectively neutralized the negative impact of elemental Mn while simultaneously boosting the overall performance of the materials, particularly in terms of cycling stability. Notably, the LMFP64/C composite exhibited the impressive initial-discharge capacity of 154 mAh/g at 0.1 C, and it maintained the capacity-retention rate of 92% after 500 cycles at 1 C, which demonstrates its exceptional cycling stability.

In this article, porous FeNiP-NH₃ was prepared by in situ ammonia etching, and its pore structure and pore properties were controlled by adjusting the etching conditions. The pores gradually become larger as the etching time is increase4d. Utilizing porous FeNiP-NH₃ with an ammonia etching time of 60 minutes as the sulfur host material can effectively enhance the electrochemical performance of lithium-sulfur batteries. The initial-discharge specific capacities are 887.2 mAh/g, 541.7 mAh/g, 466.5 mAh/g, 402.7 mAh/g, and 318.6 mAh/g, respectively, at 0.1 C, 0.2 C, 0.5 C, 1 C, and 2 C. The specific capacity of first-cycle discharge at 0.2 ℃ can reach 632.7 mAh/g and it can still be maintained at 456.9 mAh/g after 100 cycles. This is a capacity-retention rate of 72.2%, which provides excellent cycling stability and faster REDOX reaction kinetics. The unique structural features of a porous FeNi Prussian-blue-modified sulfur anode enhances the performance of lithium-sulfur batteries. The porous structure increases the specific surface area of FeNi Prussian blue and exposes a large number of active FeNi metal sites, which can effectively capture lithium polysulfide and inhibit the shuttle effect. The open porous structure not only alleviates the volume expansion of sulfur but also facilitates the penetration by the electrolyte, which provides a convenient path for the diffusion of lithium ions and improves their diffusion rate.

Ferrous sulfide (FeS) is a promising anode material for next-generation lithium-ion batteries due to its high theoretical specific capacity, safe lithium storage voltage plateau (1.5 V), abundant mineral reserves, and environmental compatibility. However, the material suffers from significant volume expansion during charge-discharge cycles, resulting in irreversible capacity loss and poor cycling performance. A coating layer can provide additional mechanical support, helping the electrode accommodate volume changes and thereby mitigating expansion effects. In this study, FeS was synthesized via a solid-phase method, and FeS@FeOOH composite materials were prepared by coating FeOOH onto the FeS surface through wet ball milling. The crystal structure and morphology were characterized using X-ray diffraction (XRD), scanning electron microscopy (SEM), X-ray photoelectron spectroscopy (XPS), and transmission electron microscopy (TEM). Results show that at a ball milling speed of 300 r/min, the coating consists of tetragonal β-FeOOH, while at 600 r/min, the coating comprises both orthorhombic α-FeOOH and tetragonal β-FeOOH phases. Electrochemical tests demonstrate that the composite prepared at 600 r/min achieves 100% capacity retention after 100 cycles at 1 A/g, owing to the synergistic effect of α-FeOOH and β-FeOOH in the coating layer. Furthermore, it exhibits a low charge transfer resistance (R_ct) of 68.84 Ω. Low-temperature discharge testing reveals that at -40 ℃ and a current density of 100 mA/g, the discharge specific capacity retention reaches 99.8% of that at 0 ℃. This study proposes a straightforward and effective FeS surface modification strategy, providing a reference for the commercial application of FeS-based anodes in energy storage systems.

Graphite is currently one of the most widely used anode materials in lithium batteries, and its electrochemical-mechanical coupling performance is crucial for the structural stability and cycling lifespan of lithium batteries. This study employed in situ measurements to investigate the evolution of the electromechanical coupling performance of composite graphite electrodes during electrochemical cycling. A physical model was also developed to analyze the evolution of the curvature, Young's modulus, strain, and partial molar volume of the composite graphite electrodes. The results show that, the elastic modulus of the graphite electrode increases progressively with the progression of lithiation, indicating a hardening behavior, while the partial molar volume undergoes phase changes that correspond to the charge state. Current collectors of different thicknesses significantly affect the stress and strain within the electrode. A thicker current collector provides more pronounced mitigation of the strain in the active layer. The peak stress and strain in the active layer of the electrode reach their maxima during the third electrochemical cycle, and the evolutionary trends of each cycle tend to converge. This work reveals the systematic mechanical-response characteristics and the evolution of the mechanical properties of graphite electrodes during the electrochemical reaction process, providing significant theoretical support for a deeper understanding of the mechanical behavior of lithium-ion battery electrodes.

A graphite dry electrode for lithium-ion batteries was fabricated using a binder fiberization technique. The effects of electrode thickness, polytetrafluoroethylene (PTFE) content, and carbon nanotube (CNT) addition on electrode performance were systematically investigated. Morphological and elemental distribution analyses were conducted using scanning electron microscopy (SEM) and energy-dispersive X-ray spectroscopy (EDS). Mechanical, electrical, and wetting properties were evaluated using a universal testing machine, a four-probe internal resistance meter, and an optical contact angle goniometer, respectively. Electrochemical performance was assessed through charge-discharge testing. The results showed that when the electrode thickness exceeded 300 μm, electrochemical performance deteriorated markedly; therefore, the thickness should be limited to ≤300 μm. Based on comprehensive evaluation, including electrochemical behavior, tensile strength, conductivity, and electrolyte wettability, the optimal formulation was identified as graphite∶PTFE∶CNT∶Super P (SP) at a mass ratio of 96∶1∶1∶2. This composition yielded a coulombic efficiency of 91.7%, a reversible capacity exceeding 330 mAh/g, a surface capacity of 11.28 mAh/cm², a tensile elongation of 9.06%, a resistance of 24.1 Ω, and a contact angle of 65.33°. SEM images confirmed the presence of abundant PTFE fibers on the surface and cross-section of the electrode, forming a branched and hierarchically structured network throughout the electrode matrix. The inclusion of CNTs enhanced both the resilience and conductivity of the electrode sheet, facilitating easier fabrication. To meet the growing demand for high energy density, thick electrode manufacturing has become a critical focus. Unlike conventional wet-coating techniques, dry electrode fabrication eliminates the need for solvents, offering a cost-effective, environmentally friendly, and scalable approach to producing thick electrodes.

This study exploits the advantages of microencapsulated phase-change material suspensions (MPCMSs), such as excellent flowability, high cooling storage density, and stable thermal behavior during cooling release. By integrating these properties into a natural stratification-based cooling storage system, a three-dimensional transient model of a cylindrical cold storage tank with a pseudo-octagonal diffuser was developed. This study investigates the cooling release and temperature distribution characteristics of MPCMSs with mass fractions of 10%, 20%, and 30% under different flow rates. The effects of the diffuser opening count and diameter on the cooling process were analyzed. An optimization strategy employing nozzle-type diffusers was proposed, and the influence of different diffuser structural parameters on the thermocline thickness, temperature fields in small-opening regions, and outflow velocity was examined.The results demonstrate that using MPCMS as the storage medium in a natural stratification-based system creates a thermocline with a steep temperature gradient at the center and smaller gradients at the sides. Higher mass fractions suppress hot-cold fluid mixing and increase the specific heat capacity in the phase-change region, enhancing the cooling release efficiency and temperature control. For 10% and 20% MPCMS mass fractions, increasing the number of openings, selecting a 10 mm diameter diffuser, and using a new nozzle-type diffuser reduce the thermocline thickness and improve cooling performance. However, at 30% mass fraction, variations in the number of openings and the use of nozzle-type diffusers compared with the original diffuser yield negligible differences in performance. This study provides valuable insights for advancing the application of MPCMS in natural stratification-based cooling systems and offers scientific support for optimizing MPCMS energy storage technology.

Supercapacitors are important energy storage devices, and their electrochemical performance is intrinsically linked to the electrode materials. The storage capacity of supercapacitors can be enhanced by combining graphene and polyaniline to form a composite material. This is achieved using dual storage modes: bilayer capacitance and Faraday capacitance. In this study, polyaniline (PANI) nanowires were deposited onto graphene sheets via in situ polymerization. The morphology and composition of the doped PANI/graphene composite were characterized using scanning electron microscopy, Fourier-infrared spectroscopy, and X-ray diffractometry. The composite slurry was coated onto a nickel foam plate to prepare graphene composite electrodes, which were then assembled into supercapacitors. The electrochemical performance of the assembled capacitors was evaluated using cyclic voltammetry, galvanostatic charge-discharge, and electrochemical impedance spectroscopy. The PANI/graphene composite exhibits good capacitance, cycling stability, and adaptability to high current densities. The mass-specific capacitance of the PANI/graphene composite was 336 F/g at a current density of 1 A/g. The specific capacitances of the graphene and graphite electrodes were 44% and 560.1% higher, respectively. The in situ polymerization method ensured strong interfacial bonding between PANI nanowires and graphene, enhanced the pseudocapacitance contributions, and significantly improved the specific capacitance of graphene supercapacitors. These findings promote the application of graphene in supercapacitors.

The development of lithium-ion batteries (LIBs) with high energy density, long cycle life, high power density, and wide operating temperature range is urgently needed to promote the development of high-end portable electronic devices. Lithium cobalt oxide (LiCoO₂, LCO), the most effective cathode material in portable applications, faces critical challenges such as limited charging cutoff voltage, low specific capacity, unsatisfied fast charging capability, and wide temperature range performance. In this study, we systematically examine the failure mechanisms and challenges of high-voltage LCO cathodes, evaluate recent advances in modification strategies, and outline future research directions. First, we examine the key failure mechanisms of high-voltage LCO cathodes and basic crystal and band structures, such as bulk structure failure process (e.g., complex phase transition, irreversible interlayer slippage, and crack initiation/propagation), interface failure process (e.g., cobalt migration and dissolution, oxygen release, electrolyte catalytic decomposition, HF attack, and cathode-electrolyte interphase degradation), and failure mechanisms under complex working conditions (e.g., high-voltage and fast charging, high-voltage and high-temperatures). Second, we review representative modification strategies and mechanisms, including the improvement of lithium-ion diffusivity and bulk-phase stability through bulk element doping, e.g., lithium, cobalt, oxygen, and multisite doping. The enhancement of structural stability and ionic/electronic conductivity of the surface-interface through chemical manipulation, including surface coating (e.g., ionic conductors, electronic conductors, ionic/electronic insulator materials), in-situ surface-interface structural conversion using wet-chemical and thermochemical methods, electrolyte manipulation through modified additives, and in-situ electrochemical surface-interface conversion process. The optimization of ion-electron transport in electrodes by improving adhesives, conductive agents, and electrode structures. Finally, we outline forward-looking research directions for high-voltage LCO cathodes, including (1) structural design of high-voltage LCO (>4.6 V); (2) design and control of high-voltage (>4.6 V) LCO-electrolyte interface; (3) synthesis of LCO for high-voltage fast charging (>4.6 V, > 50 C) and high-voltage wide operating temperature range (>4.6 V, -60—70 ℃); (4) optimization mechanism of modified LCO via advanced in-situ characterization and simulation; and (5) system design and cell construction of high-specific energy fast charging and high-specific energy wide temperature batteries. This review provides comprehensive insights and theoretical guidance for designing high-voltage LCO cathodes and other layered cathode materials for next-generation LIBs.

With the implementation of the national "carbon peak" and "carbon neutrality" policies, the development of new electrochemical energy-storage technologies has become an important aspect of support for the new power system and for energy transformation. Among various energy-storage devices, aqueous zinc-ion batteries (AZIBs) are attracting widespread attention due to their abundant resources, high theoretical specific capacity, high safety, and cost effectiveness. However, their rapid development urgently requires breakthroughs in the technical challenges of electrode materials. At present, there are three common problems with positive-electrode materials for AZIBs, which make it difficult to apply them with high-performance and long endurance under complex service conditions. In this article, starting from the development history of AZIBs, we systematically elaborate on four common energy-storage mechanisms of positive-electrode materials for AZIBs through the exploration of recent relevant literature. We then summarize the inherently low conductivity, slow ion-transport speed, and poor structural stability of three common positive-electrode materials: manganese-based materials, vanadium-based materials, and organic materials. We also focus on four improvement strategies and discuss the corresponding progress, including the construction of novel microstructures, regulation of the oxygen-vacancy concentration, regulation of the interlayer structure, and increasing the material hydrophobicity. Finally, we discuss the research prospects for, and specific directions in, composite materials, expansion of the research field, and characterization-testing technology, providing references and inspiration for the design and development of high-performance AZIBs.

With the rapid development of fresh agricultural products and e-commerce channels, as well as consumers' increasing demand for food freshness, cold chain logistics related industry technologies play an important role. The innovative application of energy storage technology as a new type of energy control technology in cold chain logistics has effectively improved the efficiency of cold chain logistics. In this regard, the article explores in detail the development process of phase change energy storage technology and cold chain logistics technology, with a focus on analyzing the application of multiple types of energy storage technology in cold chain logistics. Finally, it elaborates on the current development prospects of energy storage technology in fresh logistics, including the impact of various factors such as policies, markets, and technology on the future application of energy storage technology and cold chain transportation. It can be foreseen that energy storage technology will also be an important technical support in the field of fresh cold chain logistics in the future.

Slot-die coating improves the production efficiency of electrode sheets, reduces material waste during coating, enhances material utilization, and lowers production costs. Wide-width coating requires efficient die heads, and the structure of the slot-die head directly affects the properties of the electrode sheets. In this study, we designed a slot-die head with a cavity length of 1520 mm and a total coating width of 1240 mm. The coating quality was evaluated using the thickness consistency of the 150 μm wet-coated layer as an indicator. The slot-die head structure was optimized in three aspects: cavity optimization, adjustment of the feed port position, and shim chamfering design. The feasibility of the optimization design was verified by tracking the start-up process of the external flow field. During the coating process with a single cavity and single-feed port (the inlet was located in the middle of the die head), the wet-coated layer was thicker in the center area and thinner in the edge area. With increasing coating width, the thickness consistency decreased significantly. However, after adding a subcavity with a coating width of 1240 mm, the wet-layer thickness consistency of slurry 1 improved from 26.88% to 9.79%. By further adjusting the single-feed inlet design to a dual-feed inlet design, the consistency of the wet-layer thickness of slurry 1 increased to 0.33%. In addition, the wall shear stress in the fluid domain at the edge of the outlet was optimized by adjusting the shim chamfering, which improved the quality of the coating edge. With a coating speed of 60 m/min and a coating width of 1240 mm, the consistency of the wet-layer thickness of slurry 1 was improved to 0.28%. The comparison of numerical simulation and experimental results showed an average relative error of only 1.35%, indicating that the slot-die structural model exhibits high accuracy. Numerical simulations of the other three viscosity slurries revealed wet-film thickness consistency in the range of 0.38%—0.58%, indicating the strong applicability of the designed model. These results serve as a reference for optimizing the design of long slot-die heads.

High-pressure hydrothermal energy storage systems have gained widespread deployment in industrial heat storage and peak load balancing for grids owing to their high energy storage density, long thermal storage time, low cost, and the possibility of modularity. In this study, we developed a self-pressurized, ultrahigh-pressure hydrothermal energy storage system. In addition, we performed numerical simulations to understand the experimentally observed insufficient natural convection under electric heaters and lower heat storage efficiency due to the temperature gradient. The simulation results show that the temperature gradient can be significantly improved by including an external circulation pump, thereby improving thermal storage efficiency. The heat storage efficiency was improved by approximately 12%, and the maximum temperature difference in the gravity direction was reduced from 45.93 ℃ to 0.93 ℃ after heating for 60 min. Furthermore, we evaluated the effect of external circulation pumps on the thermal storage efficiency of the system using two indicators: dimensionless constants λ and δ, which describe the ratio of the circulating pump installation position to the storage tank size and the ratio of the circulating volume to the tank volume, respectively. With λ≈0.5, the flow within the storage tank was more consistent, the temperature gradient was relatively small, and the thermal efficiency of the storage system improved. As δ increased to 4.42, the uniformity of the internal temperature in the storage tank was optimal, the response time to thermal changes decreased, and the efficiency of thermal storage increased. This study provides conceptual insights into the thermal storage performance of horizontally oriented, high-temperature, self-pressurized hot water reservoirs, thereby facilitating the implementation of high-pressure hydrothermal energy storage systems in industrial applications.

The wide application of lithium-ion batteries in electrochemical energy-storage stations (EESSs) has led to frequent fire and explosion accidents. In order to study deeply the causal factors responsible for such accidents, we examined the 90 accidents caused by lithium-ion batteries that occurred in EESSs around the world from November 2017 to September 2024. These accidents were analyzed based on four aspects: the type of batteries, the countries where the accidents occurred, the states of the EESSs, and the factors that caused the accidents. Fifteen risk factors,including equipment risk, human risk, and environmental risk,were evaluated systematically using the Delphi method and the risk-matrix method. The results show that the number of accidents caused by lithium ternary batteries is more than 2.5 times the number of accidents caused by lithium-iron-phosphate batteries. Republic of Korea experienced the highest number of accidents—34—which accounted for 37.8% of the total number of accidents. Seventy-two EESSs accidents occurred during operation, accounting for 80.0% of the total number of accidents. Human factors accounted for the largest proportion of the total numbers of accidents, which was 43.3%. The main factors responsible for causing these accidents were cooling-system failure, battery overcharging, inadequate fire-protection facilities, failure of the battery-management system (BMS)/power-conversion system (PCS)/energy-management system (EMS), and high and low ambient temperature. To reduce the risk due to these factors, preventive and control measures were proposed to enhance the system safety of EESSs.

As the best storage medium for electric energy, energy storage power station provides support for the integration of large-scale new energy connected into the power system. However, due to the insufficient technology and management in energy storage power stations, there may be safety risks such as fire and explosion. Especially in recent years, the frequent safety accidents in energy storage power stations has further limited the promotion and application of energy storage power stations. This paper sorts out the significance of fire safety management for energy storage power stations, analyzes the potential safety risk factors in energy storage power stations, and provides specific measures for fire safety management of energy storage power stations, in order to provide effective reference for the safety of energy storage power stations.

The rapid development of electrochemical energy storage technology has brought about energy convenience, but the safety issues of energy storage systems are also becoming increasingly prominent. The shortcoming effect of conventional battery packs is the core of the safety issues of energy storage systems. The emergence of reconfigurable batteries can effectively solve the safety problems of traditional energy storage systems. Therefore, research is conducted on the design concept and corresponding mechanism of energy storage safety systems based on reconfigurable batteries. The article first elaborates on the inherent application advantages of reconfigurable batteries and the research progress of related industry technologies, and then elaborates in detail on the mechanism optimization of energy storage safety systems. Including the digital concept of safe energy and dynamic restructuring, the fault diagnosis process of the energy storage system was finally analyzed to further ensure the safety of the energy storage system. It has been proven that dynamic reconfigurable battery technology can greatly improve the safety and energy efficiency of battery energy storage systems, and also provide a new path for the design of large-scale, long-life, low-cost batteries and energy storage systems in the future.

To investigate the performance variation of piston gravity energy storage systems (PGESSs) under different design parameters, a modular modeling approach was adopted to develop submodels for piston motion, confined pipe chamber pressure, and pump-turbine power. These submodels were used to simulate the dynamic motion and energy transfer characteristics of the PGESS. The charging and discharging processes were analyzed to derive expressions for total storage energy, charge-discharge efficiency, and energy density. The influence of key parameters namely, the height of the confined pipeline, the piston height, and the piston diameter was examined individually and in terms of their ratios. Results indicate that increasing the height of the confined pipe enhances both total storage energy and energy density; increasing piston height improves charge-discharge efficiency; and increasing piston diameter significantly boosts total storage energy. When the piston height-to-pipe height ratio increases, charge-discharge efficiency improves notably, while energy density and total storage energy initially rise and then decline. As the piston diameter-to-height ratio increases, both charge-discharge efficiency and energy density first increase and then stabilize. A similar trend is observed when both piston diameter and height increase. These findings provide a theoretical basis for optimal design parameter selection in PGESSs, supporting their further development and application.

To improve the energy storage density of compressed energy storage systems and address challenges in CO₂ condensation, this study proposes a liquid-phase CO₂-based compressed energy storage system, where CO₂ mixtures are stored in liquid form on both high- and low-pressure sides. A steady-state thermal-economic model was developed to assess the system adaptability of seven CO₂-based mixtures (CO₂/R32, CO₂/R41, CO₂/R22, CO₂/R125, CO₂/R143a, CO₂/R601, and CO₂/R601a). In addition, the optimal CO₂-based mixtures and mixing ratios were selected to reveal the impact of the key operating parameters on the system performance. The results demonstrate that with an increase in the CO₂ mass fraction, the round-trip efficiency (RTE) and exergy efficiency (ηex) of the system are improved. CO₂/R41, CO₂/R32, and CO₂/R22 exhibit superior performance and lower sensitivity to CO₂ mass fraction. Among the seven mixed working fluids, CO₂/R41 (0.65/0.35) demonstrated the best thermal-economic performance under design conditions, with RTE and ηex of 59.12% and 53.11%, respectively. The high- and low-pressure storage tanks require volumes of 5217.65 m³ and 2787.39 m³, respectively, achieving an energy storage density of 9.36 kWh/m³. The total capital cost (TCC) is 134.06×10⁶ CNY, with a payback period (PBP) of 6.59 years. For the compressed CO₂/R41 (0.65/0.35) energy storage system, increasing the high-pressure storage tank temperature linearly increases RTE and η_ex, whereas TCC initially decreases before increasing. Conversely, an increase in the temperature of the low-pressure storage tank decreased RTE and ηex while increasing both TCC and PBP.

With the rapid development of renewable energy technologies, energy-storage batteries have gained widespread application in power systems. Accurately estimating the state of charge (SOC) of batteries is critical for ensuring their performance and safe operation and extending their lifespan. To improve the accuracy of SOC estimation for grid energy-storage batteries under varying power demands, we propose a method based on dynamic capacity correction. First, the error-generation mechanism of traditional SOC estimation methods under complex operating conditions was analyzed, and a general improvement strategy was proposed. Second, the capacity variation characteristics of batteries at different discharge rates were analyzed, and a quantitative model that characterizes the relationship between discharge rate and capacity was established, providing a theoretical foundation for accurate SOC estimation. Next, a hybrid estimation algorithm, CLA-EKF, was developed by integrating deep neural networks with the extended Kalman filter (EKF). This approach leverages the advantages of both methods in handling complex nonlinear relationships and resisting disturbances. Furthermore, an SOC estimation method with adaptive capacity correction based on discharge rate was developed. The experimental results demonstrate that the proposed capacity-correction-based CLA-EKF method significantly improves the accuracy of SOC estimation under various fluctuating power conditions, outperforming conventional methods. This study provides an effective solution for SOC estimation in grid energy-storage systems with high practical application value.

As the proportion of new forms of energy in the power grid and the usage of electric vehicles increase, the energy fluctuations and the uncertain charging loads die to electric vehicles have brought challenges to the flexible operation of power system. To meet users' travel needs, electric vehicles are parked most of the day, and with the development of vehicle-network interaction technology, electric vehicles have certain energy storage characteristics. In this paper, a charging-and-discharging strategy for power-system scheduling is proposed that takes into account the uncertainty of electric vehicle users' willingness to participate, and the flexibility of this strategy is analyzed. First, in order to solve the problem that the scheduling of charging-and-discharging existing electric vehicles does not consider a user's willingness to participate, an optimistic and a pessimistic strategy for the orderly charging-and-discharging of electric vehicles is proposed based on the logistic function, which reflects the uncertainty of a user's willingness to participate in orderly scheduling of charging-and-discharging. The influence of different charging-and-discharging modes for electric vehicles on the multi-timescale dispatching of the power system is then compared and analyzed, with intra-day dispatching carried out on the basis of the previous day, with the minimum comprehensive cost of power system dispatching as the objective function. Finally, the simulation results show that considering the energy-storage characteristics of electric vehicles, the charging-and-discharging strategy for electric vehicles proposed in this paper, which takes into account users' willingness to participate, not only cuts off peaks and fills valleys effectively but also promotes the flexible operation of the power system at less cost.

Inspired by the thermodynamic concept of Clausius entropy, power entropy can be used to assess the adjustment ability of energy systems. Power entropy exhibits a consistent positive correlation with key energy storage indicators, such as power, duration, and capacity. Building on previous research, this study proposes a calibration method for evaluating system adjustment ability within a power entropy framework and discusses its application in optimizing energy storage configurations under specific conditions. The results demonstrate that under different energy storage configurations, the system power entropy decreased by 33%. The system optimization progresses as the power entropy increase introduced by the energy storage decreases, which corresponds to reduced redundancy in the energy storage regulation performance. The power entropy concept provides a new idea for the optimization of energy storage configurations in future power systems and the optimization of multiple energy forms in integrated energy systems.

With the proposal of the "3060 Plan" and the introduction of a series of reform schemes for the power system, the development of new energy grid connection technology has been vigorously promoted. However, due to the randomness of photovoltaic power generation, accurately predicting photovoltaic power generation has become quite challenging. The large-scale connection of photovoltaic power plants to the power system poses severe challenges to the power system's power flow distribution and scheduling operations. This paper proposes an optimization scheduling method based on deep reinforcement learning, emphasizing its intelligence, self-regulation, and dynamic adjustment characteristics. It also attempts to explore multi-objective optimization and multi-level scheduling strategies, providing theoretical support and guidance for the efficient and sustainable development of energy storage systems.

The traditional droop control strategy for energy storage systems does not consider the state of health (SOH); thus, it does not ensure SOH balance and may intensify disparities among modules. To ensure the stable operation of DC microgrids and maintain power balance in energy storage systems, there is a need for a control strategy for parallel energy storage systems that incorporates the SOH of the storage modules. Herein, we propose an improved droop control method that considers SOH as a control strategy for parallel energy storage systems. The proposed method employs a secondary compensation mechanism to reduce bus voltage drop. Furthermore, we developed an improved droop control model. We evaluated the effectiveness of the secondary compensation mechanism in maintaining system voltage stability and examined the impact of the exponential factor n on the speed of power balancing. The proposed strategy was validated through MATLAB/Simulink simulations, demonstrating its effectiveness in vanadium flow battery (VFB) parallel energy storage systems. This study provides a promising approach for improving the accuracy and efficiency of energy storage system management in DC microgrids.

Grid-forming energy storage systems (GFESS) are a promising solution for enhancing power system stability under weak grid conditions. However, the modeling of short-circuit current behavior for GFESS remains underexplored. Existing studies typically adopt electromechanical models, which inadequately capture the transient characteristics induced by inductive and capacitive elements, thereby complicating protection parameter configuration and control parameter optimization. This paper presents an equivalent model of a GFESS connected to the power grid on the electromagnetic time scale. Considering the step changes in voltage amplitude and phase angle during grid faults, a time-domain dynamic response model of the GFESS electromotive force is developed. The relationship between GFESS control parameters and the electromotive force, specifically response time and overshoot, is quantitatively analyzed. Based on the damping behavior under various control settings, a systematic method for calculating the short-circuit current of GFESS, accounting for variations in phase angle and amplitude, is proposed. The method is validated through simulations under both symmetrical and asymmetrical fault conditions. Variance analysis confirms that the proposed model accurately quantifies the short-circuit current capacity of GFESS, offering a reliable reference for grid operators and dispatchers.

To meet the grid stability requirements associated with large-scale distributed new energy (DNE) integration into distribution networks, shared energy storage (SES) offers an effective solution for mitigating uncertainty and minimizing deviation penalty costs. However, the multifaceted contributions of DNEs—both economic and operational—when jointly utilizing SES remain insufficiently characterized, and current revenue allocation mechanisms lack fairness and rationality. This study proposes a contribution-based revenue allocation strategy for a cooperative alliance of DNEs and SES. A cooperative model is developed wherein multiple DNE participants co-deploy and share energy storage resources. An optimization-based scheduling model is established to maximize the alliance's net revenue by accounting for electricity sales, operating expenses, and deviation penalties, with opportunity constraints introduced to reflect renewable energy uncertainty. Three contribution indices are formulated—stability contribution, deviation adjustment contribution, and economic contribution—to quantify each DNE's role in alliance performance. These indices are used to define a correction factor that adjusts the classical Shapley value method, resulting in an improved revenue allocation strategy. Case study results demonstrate that the proposed alliance structure reduces deviation penalties and improves net economic benefits by 6.64%. Furthermore, the revised allocation method appropriately reflects the overall contributions of individual DNEs: members with higher stability and deviation adjustment contributions experience revenue increases of 82.73%, while those with stronger economic contributions gain 8.81%. The strategy thus ensures fairness and incentivizes active participation in cooperative storage-sharing schemes.

The importance of modern power grid risk analysis technology is constantly increasing. This study first analyzes the risk factors and impacts of energy storage systems from their sources, including energy storage capacity, charging and discharging power, and external environment. Then, the construction method of modern power grid risk assessment model was elaborated, and core parameter risk indicators were introduced to achieve quantitative analysis and evaluation of the power grid, seeking good operation strategies for the power grid under multi-element uncertainty conditions. Finally, this article discusses in detail the combination of power grid risk assessment and intelligent operation and maintenance, especially the optimization role in the field of remote uninterrupted transmission of switches, providing strong support for the stable operation of modern power systems.

To overcome the limitations of traditional state of health (SOH) estimation methods—such as inadequate feature extraction, nonlinear complexity, and difficulty in model parameter optimization—this study proposes a novel SOH estimation approach based on incremental capacity analysis combined with variational mode decomposition (VMD), grey wolf optimization (GWO), and kernel extreme learning machine (KELM). First, an improved voltage-capacity model based on the Lorentz function is employed to fit voltage-capacity data during the constant-current charging process, enabling the extraction of health indicators such as peak voltage, peak value, and peak area. Model parameters are optimized using the GWO algorithm, thereby improving feature extraction accuracy and robustness. Next, VMD is applied to decompose SOH-related signals into multiple intrinsic mode functions. These components serve as inputs to individual sub-models, effectively capturing signal characteristics across distinct frequency domains while mitigating noise and mode mixing. Subsequently, the GWO algorithm is used to optimize the key parameters of the KELM model, significantly enhancing its nonlinear regression capability and estimation accuracy. The proposed method is evaluated through comparative analyses across different training data sizes, estimation models, and datasets from multiple batteries. Experimental results demonstrate that the proposed method achieves high-accuracy SOH estimation using only 100 cycles of data, with a mean absolute error of 0.9751% and a maximum error of 1.9340%. The model also exhibits strong robustness and generalization performance.

Swelling force is an important parameter for evaluating the performance and safety of LiFePO₄ (LFP) energy storage batteries, which is affected by the state of charge (SOC) and state of health (SOH) of the battery. However, the evolution and mechanisms of swelling forces over the full lifecycle of high-capacity LFP batteries are not well understood. In this study, we investigated the swelling force variation in a 280 Ah LFP battery under constrained conditions, which was assembled into modules with different string numbers. In addition, we analyze the behavior of the swelling force under full SOC and lifecycle. The results demonstrate that the swelling force changes with the SOC of the battery during a single cycle. Because of the structural characteristics of graphite and lithium iron phosphate materials, during charging, two swelling peaks emerged at approximately 30% and 100%SOC, and two peaks emerged at 100% and 30%SOC during discharging. These peaks evolved with the battery degradation. At 100%SOC, the force gradually changes from maximum to minimum value of the battery ages, whereas at 30%SOC, the force gradually becomes the largest. In addition, after the SOH dropped to approximately 90%, the expansion force exhibited a linear correlation with the SOH. The swelling force growth trend was maintained as the series count increased, and the maximum expansion force of the 1P12S module reached 2365 kgf at 70% SOH. Based on the measured data, the simulation analysis of the swelling force of the module indicates that the design of the module's main components meets the structural safety requirements throughout its lifecycle. This study preliminarily explores the swelling force characteristics of the LFP battery modules, providing a reference for swelling force simulations at the module level. Furthermore, this study provides support for the safe design and development of LFP battery modules in energy storage systems.

Accurate estimation of the state of health (SOH) of lithium-ion batteries is essential for ensuring the safety and reliability of battery systems and is a critical function of battery management systems. To address the limitations of existing data-driven SOH estimation methods—such as inadequate representation of uncertainty and insufficient decoupling of training and testing data—this study proposes a novel approach based on Gaussian process regression (GPR) optimized by the egret swarm optimization algorithm (ESOA). Health features related to battery aging are extracted from the charging voltage, current, and relaxation voltage data of similar batteries, and features with high correlation to capacity are selected using Pearson correlation analysis. A GPR model employing a squared exponential kernel is then used for SOH estimation, with its hyperparameters optimized via ESOA. The proposed method is validated using NCA and NCM battery datasets from Tongji University. Experimental results show that the method significantly improves estimation accuracy and robustness. For the tested batteries, the maximum root mean square error (RMSE) and mean absolute error (MAE) are 0.0028 and 0.22%, respectively, representing improvements of 58.82% and 57.69% over conventional GPR models. Additionally, the method enables SOH interval estimation, reducing the risk of safety hazards from overestimation.

With the increasing level of lithium-ion battery industrial manufacturing, the requirements for the detection speed and accuracy of battery components are also increasing. As a key component of lithium-ion batteries, the pole piece has a wide variety of surface defects and a small area, and the traditional visual inspection has been difficult to meet the current production needs of the pole piece. The surface defect detection technology of lithium battery based on image processing can solve the above problems well, and the research of this technology is analyzed and reviewed in this paper. Firstly, the development of lithium battery surface defect detection technology is introduced, and then the lithium battery surface defect detection process under electronic image processing technology is explained in detail from each link, including image acquisition, preprocessing, feature extraction and recognition. Through practical research, it can be confirmed that through high-quality image processing and analysis algorithms, lithium battery defects can be detected more accurately, which is suitable for the current industrial production needs.

The eutectic properties of molten salts are key indicators for assessing their potential in thermal storage applications, with broad relevance in efficient thermal storage technologies. To quickly and accurately predict whether a eutectic can form between different components of mixed nitrate salts, and to further determine their eutectic composition and lowest melting point, this study employs FactSage thermodynamic simulation software to simulate five common nitrate salts—lithium nitrate (LiNO₃), potassium nitrate (KNO₃), sodium nitrate (NaNO₃), potassium nitrite (KNO₂), and sodium nitrite (NaNO₂). Phase diagrams of their binary and ternary systems were predicted thermodynamically. Based on the simulation results, the effects of anions and cations on the eutectic properties of binary and ternary nitro salt systems were further analyzed. It was found that different combinations of anions and cations significantly influence the eutectic formation temperature and eutectic composition. To verify the accuracy of the phase diagram predictions, binary salt (Li-K nitrate) and ternary salt (Li-Na-K nitrate) systems with favorable eutectic behavior were experimentally validated by determining their melting points and eutectic compositions. The experimental results showed consistency with the predicted values, further confirming the validity and accuracy of FactSage software in predicting the phase diagrams of molten salt systems. In addition, this study predicts binary and ternary phase diagrams for several common nitro salts to analyze the mechanism by which cations and anions influence mixed molten salt energy storage media. Through this work, new approaches are proposed for evaluating the eutectic behavior of mixed nitrate salts, providing essential theoretical support for optimizing and applying molten salts in future thermal storage technologies.

Reliability design is an important aspect of the development of vanadium-battery stacks, and it plays a decisive role in the safety and economy of energy-storage systems. Taking the liquid-flow frame and bipolar plate as the research objects, this study establishes reliability models for the core components of vanadium batteries based on the lognormal distribution and on two-parameter and three-parameter Weibull-distribution models. Parameter-estimation in this study is based on the maximum-likelihood estimation method and the GM (1,1) method. The K-S test and the root-mean-square error (RMSE) were adopted as evaluation indicators for exploring the influence of the distribution model and the parameter-estimation method on the reliability of the predictions. The results show that the minimum goodness-of-fit for the liquid-flow-frame reliability model is 0.89, indicating that the reliability models obtained using the two-parameter-estimation methods all pass the goodness-of-fit test. Further, the minimum RMSE of the reliability model is 0.22, which indicates that the two-parameter distribution model based on the GM (1,1) method is the most suitable reliability model for the liquid-flow frame. The minimum goodness-of-fit of the bipolar plate reliability model is 0.03, which indicates that the maximum-likelihood estimation method is not suitable for estimating the parameters of the three-parameter Weibull-distribution. Further, the minimum RMSE of the reliability model is 0.16, which indicates that the two-parameter distribution model based on the GM (1,1) method is the most accurate reliability model for the bipolar plate. These research results have theoretical guiding significance for the reliability design and optimization of vanadium-battery core components and systems.

In this study, we explore the factors that influence and the inherent laws that govern the heat storage and release performance of phase-change solar greenhouses in cold regions. First, a full-scale model of a phase-change solar greenhouse was constructed using TRNSYS software, and its reliability and accuracy were verified through experiments. Subsequently, this model was used to investigate the effects of the structural parameters of the enclosure—such as the span of the greenhouse, the north-wall height, and the heat-transfer coefficient—as well as the parameters of the phase-change material—such as the phase-change temperature, latent heat, and phase-change material dosage—on the average air temperature and the effective accumulated temperature of the phase-change greenhouse. Finally, a nonlinear regression equation was established between the effective accumulated temperature and the aforementioned parameters using multiple regression methods within the SPSS software. The following conclusions were obtained by analyzing the data from these experiments: The effective accumulated temperature and the average air temperature both decrease as the span of the greenhouse and the north-wall heat-transfer coefficient increase, a negative correlation, and they both increase as the north-wall height, latent heat of the phase change, and phase-change material usage increase, a positive correlation. The effective accumulated temperature and the average air temperature reach their maximum values—16.63 ℃ and 36.29 ℃·d, respectively—at a phase transition temperature of 23 ℃. A nonlinear regression equation was constructed for the effective accumulated temperature in terms of the heat-transfer coefficient of the north wall, the span of the solar greenhouse, the north-wall height, material usage, latent heat of the phase change, and phase-change temperature. This study thus provides a convenient means for the design and optimization of phase-change solar greenhouses in severely cold areas.

The vanadium redox flow battery (VRFB) offers several advantages, including long service life, high safety, straightforward energy management, and the independent scalability of power and capacity. These characteristics make it well-suited for applications such as mitigating fluctuations in renewable energy generation, peak shaving and filling, and voltage and frequency regulation in modern power systems. In recent years, VRFBs have seen large-scale deployment. However, due to the intrinsic properties of core components—such as membranes, stack and pipeline configurations, and electrolyte composition—capacity decay remains a significant challenge during operation. To extend service life, improve energy efficiency, and reduce the frequency of maintenance tasks such as electrolyte rebalancing and ion concentration adjustment, extensive research has been conducted globally on mitigating capacity fade in VRFBs. This paper analyzes the causes of capacity decay from both mechanistic and technical perspectives, summarizing the state of research on the impacts of water and vanadium ion migration, self-discharge, side reactions, temperature, and concentration under various application conditions. It emphasizes the roles of membranes and electrolyte-related materials in influencing capacity retention, along with strategies that adjust flow rate, charge-discharge protocols, and active species concentrations to suppress capacity loss. Moreover, it reviews emerging technologies tailored to specific application scenarios that aim to inhibit capacity degradation. The insights presented herein provide guidance for maintaining electrolyte performance and overall battery capacity during long-term VRFB operation.

With the rapid development of renewable energy and the continuous advancement of smart grid technology, grid energy storage joint scheduling has become an important means to improve the operational efficiency and reliability of the power system. This article proposes a deep learning based power grid energy storage joint scheduling strategy, which achieves intelligent scheduling of the power grid and energy storage system through data preprocessing and feature extraction, deep learning model construction and optimization, scheduling strategy formulation and implementation, and other steps. The experimental results show that this strategy can significantly improve the energy utilization efficiency of the power system, reduce operating costs, and provide strong support for the sustainable development of the power system.

Hydrogen and ammonia production from wind power offers a practical solution for addressing the on-site consumption challenges of wind energy in China's "Three North" region. To enhance the economic performance and operational stability of wind-hydrogen-ammonia synthesis systems, this study proposes an optimal allocation strategy that considers multi-factor constraints in source-storage-load coordination. First, a system model for wind turbine-driven hydrogen and ammonia production is established, incorporating variables such as hydrogen production equipment power, energy storage configuration ratio, hydrogen storage tank capacity, grid electricity consumption, annual shutdown frequency, hydrogen and ammonia outputs, and liquid ammonia costs under different operational conditions. Second, an economic objective model is formulated as a boundary condition to guide a source-storage-load collaborative optimization strategy. This strategy identifies optimal output levels for grid electricity extraction, hydrogen storage, and battery energy storage to achieve cost-effective configurations across different scenarios. Finally, using typical annual wind speed data from Urad Middle Banner as a benchmark, an economic comparison is conducted across four working scenarios. Results demonstrate that the proposed control strategy effectively meets field demands, achieving the lowest unit ammonia cost, maximum system output, minimal downtime, and the shortest payback period. This study enhances the local consumption and self-balancing capabilities of wind power systems and offers engineering insights for improving the economic viability of hydrogen and ammonia production based on coordinated source-storage-load operation.

To address voltage fluctuations caused by the high proportion of intermittent renewable energy access to the power grid and enhance the acceptance capacity of the distribution network for clean energy, this study proposes an energy storage optimization strategy for distribution systems using an improved artificial colony (ABC) algorithm. First, the structure of the energy storage system is analyzed, and mathematical models are constructed for the energy conversion efficiency, power-capacity relationships, and state of charge. Second, a two-layer optimal control model was proposed for a distributed energy storage system. The upper control layer prioritizes minimizing voltage deviation and maximizing system stability, while the lower layer optimizes renewable energy consumption and reduces system active power network loss by coordinating distributed energy storage power allocation. The ABC algorithm was introduced to solve the model, integrating Cauchy variation to enhance the global search ability of the algorithm, and a dynamic inertia weight mechanism was combined to accelerate convergence and improve solution accuracy. Finally, the proposed strategy was validated on a typical IEEE33-node distribution network, demonstrating its effectiveness in enhancing renewable energy utilization, maximizing the regulatory role of energy storage systems, and ensuring flexible, reliable distribution network operation.