The proton exchange membrane fuel cell (PEMFC) must ensure adequate hydration of the proton exchange membrane during operation while preventing condensed liquid water from blocking the mass transfer channel. To analyze the effect of cathode relative humidity on membrane water content and PEMFC's output performance, a cathode inlet water content (CIWC) model was developed. First, this model considers the influence of temperature and water content on membrane resistance, derives a formula for calculating membrane water content, and couples the CIWC model with the computational fluid dynamics software FLUENT for computation. Second, a fuel cell test bench was constructed to perform experiments at an operating temperature of 60 ℃, 100% anode relative humidity, and 50%, 75%, and 100% cathode relative humidity, respectively. Finally, the simulated data of the CIWC model and the FLUENT built-in model were compared with experimental values. The species distribution of membrane water content, membrane conductivity, and molar water fraction in the catalytic layer on the cathode side were analyzed. The results show that at a cathode relative humidity of 50%, the CIWC model's accuracy improved by 17.67% compared to the FLUENT model at a voltage of 0.739 V. The maximum relative error between the CIWC model and experimental value was 5.66% at 100% cathode relative humidity. As the cathode's relative humidity increases, the membrane water content continuously rises at a voltage of 0.75 V and approaches saturation at 0.6 V. The membrane water content, proton conductivity, and molar water fraction in the catalytic layer gradually increase in the flow field directly from the air inlet to the outlet. At a cathode relative humidity of 75%, the fuel cell output power density reaches 272.08 mW/cm², and the membrane water content distribution becomes more uniform.

Addressing the issue of low energy storage/discharge rates in phase-change energy storage heat exchangers, this paper presents a shell-and-tube type phase-change energy storage heat exchanger using paraffin as the energy storage material and water as the heat transfer fluid (HTF). The aim is to investigate the influence of HTF on the heat exchanger's energy storage performance. An experimental platform for the shell-and-tube type phase-change energy storage heat exchanger is constructed, and a three-dimensional transient model is developed using FLUENT software. By changing the boundary conditions, a numerical simulation of energy storage is performed to examine the effect of different HTF temperatures and flow rates on the energy charge/discharge process. The results of the study show that the larger the temperature difference between the HTF and the paraffin, the faster the heat charge/discharge rate. When the temperature difference increases by 5 ℃, the maximum increase of the average energy storage rate is 91%, and the maximum increase of the average energy discharge rate is 124% but with the penalty of irreversible exergy loss. Due to the very small natural convection within the phase change material, the heat discharge rate is much lower than the heat charge rate in the solidification process. With a temperature difference of 5 ℃, the average energy discharge is 64% of the average energy storage rate. The temperature difference is the main factor influencing the heat transfer performance. As the HTF flow rate increases, the strengthening of the convective heat transfer accelerates the heat transfer and melt rate. However, the impact on the average energy storage rate of the shell-and-tube heat exchanger and the exergy loss is insignificant. With balancing the average energy storage/discharge rate and exergy loss, this study's optimal heat exchanger performance was achieved with an HTF temperature of 40 ℃ for heat charge, an HTF temperature of 10 ℃ for heat discharge, and a flow rate condition of 0.5 m/s. This study provides valuable insights into applying energy storage heat exchangers in data centers.

The solid-liquid phase change material (PCM) stores energy in the form of latent heat and is widely used for thermal energy storage. However, traditional PCMs generally exhibit low thermal conductivity. Adding a porous skeleton with high thermal conductivity can improve the thermal storage performance of these materials. To investigate the effect of the porous skeleton structure on thermal storage performance, we examined the effect of the porosity and directional growth probability on the melting process at the pore scale using an enthalpy-based lattice Boltzmann method with a double distribution function model. A dimensionless thermal storage power parameter was proposed to evaluate performance. Our results showed that as the porosity decreases, the melting rate of the composite increases, and the dimensionless heat storage power is enhanced. When the porosity is below 0.80, the dimensionless thermal storage power improves compared to pure PCM. By selecting an appropriate directional growth probability, the heat transfer rate can be effectively improved. The complete melting time for a skeleton with the main growth direction in the 1,3 direction is 13.9% shorter than for a homogeneous skeleton. This work provides a theoretical basis and data reference for designing and applying porous skeleton composite PCMs.

Carbon coating is essential for the performance of SiO anode materials; however, the intrinsic link between carbon content and electrode electrochemical performance has not been studied in depth. Herein, SiO/C anode materials with different carbon contents were prepared using chemical vapor deposition (CVD) technology. The study found that when the carbon content was about 5%, the electrochemical performance of the composite materials was the best because they had a fast lithium-ion transport channel. Further research found that when the carbon content of the CVD process continued to increase, carbon materials with disordered structures accounted for a large proportion. Although the electrical conductivity was improved, the gaps originally available for lithium-ion transport were heavily blocked, which greatly affected the kinetic reaction rate of the electrode. The prepared SiO/C-2 electrode showed excellent cycling performance, with a reversible capacity of 1350 mAh/g after 100 cycles.

Conductive polymer polyaniline (PANI) has many advantages, such as large theoretical capacitance, low preparation cost, and ease of synthesis on a large scale. However, the ion transport layer of pure PANI has a compact structure, limiting its application in practice. Herein, the nanocomposites based on inorganic hollow tubular halloysite and conductive polymer polyaniline were prepared by chemical oxidation polymerization with a simple process and low cost. Furthermore, the composites were poured on the graphene electrode to prepare a highly flexible supercapacitor electrode. In analyzing the product's microscopic morphology, it can be observed that polyaniline is uniformly coated on the wall of the halloysite tube, forming a hierarchical core-shell structure. Based on this, the effective contact area between the electrode material and the electrolyte is significantly increased. The resulting halloysite/polyaniline electrode shows an ultra-high capacitance value of 446.1 F/g at a current density of 1 A/g. Even being charged and discharged at an ultra-high current density of 10 A/g, it still has good rate performance and cycle stability and can retain 90.5% of the initial capacitance value after 1600 cycles. In addition, due to the π-π stacking effect between the graphene substrate and PANI, PANI can tightly adhere to the graphene electrode during the repeated bending process, thus obtaining ultra-high bending resistance (keeping 90.2% after 5000 bending cycles). Therefore, the electrode prepared by this method can exhibit excellent electrochemical performance, providing a reference for preparing highly flexible supercapacitors based on conductive polymers.

Hydrogen energy is a sustainable secondary clean energy. In large-scale applications, hydrogen storage and transportation technology are the key factors restricting the development of the hydrogen energy industry chain. Physical adsorption hydrogen storage technology is one of the important ways to safely apply hydrogen in the future. However, it still needs to overcome the technical problems of low hydrogen storage capacity and low absorption temperature. Focusing the research on physical adsorption hydrogen storage technology, the development history and research progress of carbon-based materials, such as activated carbon, graphene, carbon nanotubes, mesoporous carbon, and carbon aerogel, organic framework materials such as metal-organic framework materials (MOFs) and covalent organic framework materials (COFs), and hydrates such as hydrogen storage materials were summarized, and the research achievements and technical means of various materials in improving hydrogen storage capacity were introduced. Simultaneously, the hydrogen storage principle of the abovementioned physical adsorption hydrogen storage materials and their technical characteristics in hydrogen storage, transportation, and utilization were analyzed. The advantages and disadvantages of hydrogen storage materials based on different physical adsorption mechanisms were compared to provide a further application analysis basis for the application of hydrogen storage and transportation technology. Finally, the breakthrough and development direction of physical adsorption hydrogen storage technology were proposed according to the future development trend of solid hydrogen storage and the current technical limitations. Although the physical adsorption hydrogen storage technology has obvious technical limitations, it is still a necessary branch in the hydrogen storage field to combine with other hydrogen storage technologies to form a composite hydrogen storage system, which still has a good synergistic effect, helping to enhance the hydrogen storage efficiency and improve the dynamics and thermodynamic performance of hydrogen absorption and desorption processes.

Polymer-based solid-state lithium batteries have become a promising energy storage device that can meet the high energy density and high safety requirements due to the high safety, flexibility, interface compatibility with electrodes, and easy processing of solid polymer electrolytes. Organoboron compounds have received significant attention in the research on polymer electrolytes for lithium-ion batteries because of their wide range of design possibilities, excellent thermal stability, and the prospect of increasing the Li⁺ transference number of electrolytes. This paper summarizes the latest research progress of polymer electrolytes, containing organoboron for lithium-ion batteries. First, the advantages, composition, and classification of polymer electrolytes for lithium-ion batteries are briefly introduced. Then, the application of anionic borate-based single-ion conducting and borate ester-based polymer electrolyte, borate lithium salts, boron ester, and borane electrolyte additives in polymer lithium-ion batteries are introduced in detail. The comprehensive analysis shows that boron-containing groups, covalently linked to the polymer electrolyte, can increase the Li⁺ transference number and suppress the growth of lithium dendrites. Using organoboron electrolyte additives can improve the interface contact and construct a stable SEI between the electrode and electrolyte. Finally, the challenges facing the practical application of polymer electrolytes containing organoboron are pointed out, and future research directions are prospected. This review aims to highlight the potential application of boron in polymer electrolytes and provide new insights for the research and development of polymer-based solid-state lithium batteries.

Catapult launching can effectively reduce the energy consumption of UAVs in the take-off stage, increase the payload, and improve cruising mileage. This study investigated the compressed air ejection system for fixed-wing UAVs through experiment and simulation. Based on the overall thermodynamic design of MATLAB, the compressed air catapult prototype was established successfully, and detailed experimental tests were conducted to evaluate its performance. A dynamic simulation model was further established to grasp its aerodynamic action process. The variation curves of pressure and motion parameters such as velocity, acceleration, and displacement in the launching process of UAVs were obtained. The different parameters, like the effects of different working pressures on the performance of the catapult launch prototype, concrete working process, ejection performance of the catapult launch prototype, and the flow characteristics of compressed air and its effect on the piston were analyzed in detail. The experimental test results show that the catapult prototype can realize the catapult take-off of the 50 kg UAVs at a speed of 25.11 m/s. Similarly, its aerodynamic mechanism is obtained through simulation research.

Renewable energy is aimed to be the main part of a new electrical system to support the strategic goal of "Carbon Peak, Carbon Neutrality"; however, due to the drawbacks of intermittence, fluctuation, and periodicity of renewable energy, large-scale, long-duration energy storage systems urgently need to improve the quality and flexibility of renewable energy. Compressed air energy storage (CAES) is considered one of the most promising large-scale long-duration energy storage technologies with high efficiency, low cost, and environment-friendly merits. Generally, the CAES system utilizes constant-volume storage caverns. Thus, the charging and discharging processes are under dynamic conditions, especially the storage pressure. Various CAES operation modes, including dynamic component features, are investigated due to the dynamic pressure conditions and system modeling. Similarly, the operation characteristics and performance of both component-level and system-level are analyzed. The results show that the combination of constant pressure and sliding pressure mode in the discharging process, and enlarging the pressure range of the air chamber, can improve the round-trip efficiency and energy density of the TS-CAES system, which are 73.98% and 26.49 MJ/m³, respectively, at the discharging time of 6 hours.

A compressed air energy storage (CAES) system has gained attention due to its advantages of long life, low cost, and low environmental pollution. However, the CAES system is faced with low energy density and efficiency. Herein, the parallel operation mode of pneumatic motors is proposed to improve the power output of the CAES system. The output performance, economic performance, and energy conversion efficiency of CAES with the key parameters are compared and analyzed when the pneumatic motor works in parallel or alone mode. The purpose of studying the forward and reverse performance of the pneumatic motor is to integrate the expander and compressor of the micro CAES system. It was observed that the pneumatic motor could operate in two modes to improve energy utilization efficiency. In the application scenario, combined with renewable energy power generation, the off-design operation is another critical problem faced by the CAES system. The changes in air tank pressure and load demand fluctuation represent various off-design conditions. Herein, the influence of key parameters on the performance of the CAES system is investigated via experiments under off-design conditions and improved by adopting the parallel operation mode of pneumatic motors. When the intake pressure is 10.5 bar, the maximum power output of the pneumatic motor and generator is approximately 660 and 380 W, respectively.

It is required to exert thermal mitigation measures on batteries to delay the thermal runaway (TR) process and prevent fire and explosion accidents in lithium-ion battery packs, caused by TR propagation. Therefore, this study selected the 40 Ah square ternary lithium battery packs as the experimental object, in which the influence of inserting different heat mitigation plates between batteries on TR propagation and heat transfer characteristics was experimentally investigated. The experimental results show that the TR behavior and heat transfer rates of the batteries were severe and rapid, without any plates. The temperature of the left side of the downstream battery reached 364.89 ℃ after triggering the TR of the upstream battery for 30 s. The average interval time of TR propagation between adjacent batteries was 99.33 s. After inserting 6 mm thick cotton plates and 3 mm thick aerogel plates as mitigation interlayers, the average interval time was extended to 644.33 s and 1282.33 s, respectively. The TR process was entirely suppressed by inserting the 6 mm thick aerogel plates. Considering the battery pack's thermal mitigation performance and energy density, a 3 mm thick aerogel plate a suitable thermal mitigation material for this study. The calculation model of heat transfer in the TR process was established. The analysis showed that 81.7% of the TR-inducing heat came from upstream batteries where TR happened, whereas only 18.3% of the heat came from self-generation. The results of this study can provide theoretical guidance for the safe design of lithium-ion battery modules and the selection of TR mitigation materials.

The auto-disturbance rejection control (ADRC) system has the advantages of small overshoot and stability errors, fast response speeds, excellent anti-interference ability, and robustness. Aiming at the shortcomings of electromagnetic bearing proportion integration differentiation (PID) control in terms of immunity, this study investigated a 10 kWh flywheel energy storage system and designed a double closed-loop ADRC system. A control system of single-degree-of-freedom magnetic bearings was built with Simulink. A random noise signal was applied in different positions, and the control capability of ADRC was verified in active magnetic bearing (AMB) control applications with the mechanical error interference ignored. Moreover, the anti-interference abilities of the ADRC and PID control were compared. The anti-interference control system's response overshoot and steady-state error can be controlled within ±5%, indicating better anti-interference ability. The results provide a good reference for the design of the auto-disturbance rejection controller of the magnetic bearing.

A new secondary flow serpentine liquid cold plate is proposed to address the issues of high-pressure drop and significant power consumption associated with traditional serpentine channels combined with secondary flow structures. This study establishes a simulation model to compare the performance of traditional serpentine liquid cold plates and secondary flow serpentine liquid cold plates. Furthermore, the effects of factors such as channel numbers, width, angle, and distance on the secondary flow serpentine flow channel's heat transfer and pressure drop characteristics under various flow velocities are investigated. The results show that incorporating the secondary flow structure into the traditional serpentine liquid cold plate reduces the pressure drop at the inlet and outlet by 90.69%, largely resolving the issue present in traditional serpentine channels.As the flow rate increases, the cooling effect of the liquid cold plate with different structural parameters improves; once the flow rate exceeds 0.4 m/s, the maximum temperature stabilizes around 303 K, and the maximum temperature difference remains at approximately 4.5 K. Pressure drop and pump work also increase with the flow rate. Each structural parameter has an optimal value; when the number of channels is 7, the channel width is 4 mm, the channel angle is 75°, and the channel distance is 8 mm, the system's pressure drop is significantly reduced, leading to substantial pump work savings.

This study proposed an optimal operation method for energy storage systems in the data center, based on the Markov decision process, and an alternating direction multiplier method to alleviate the problems of the high comprehensive operating cost of the data center and large load fluctuations on the grid side. First, the basic structure, the power consumption characteristics of the primary devices, and the load time-shifting characteristics of the data center are analyzed. Second, considering the power supply reliability of the data center, the charge and discharge power limit, and the loss cost of the energy storage system, an optimal operation model is established to minimize the peak-to-valley load difference and comprehensive operating cost. Then, given the multi-time coupling characteristic, the Markov decision process is used to reconstruct the model, and the alternating direction multiplier method is used to solve the reconstructed problem. Finally, the simulation analysis of a large-scale data center is conducted in MATLAB, and the obtained results verify the rationality and effectiveness of the proposed optimal operation method.

Configuring energy storage with suitable capacity for renewable energy is crucial for promoting new energy consumption and achieving emission reduction and decarbonization. This paper proposes a joint planning method for renewable energy and energy storage aimed at reducing carbon emissions and improving the load-carrying capacity of the power grid, considering the aggregate control of temperature-controlled loads. First, a temperature-controlled load aggregation model was developed, considering factors such as equivalent heat capacity and equivalent thermal resistance, and other thermal parameters. The influence of the collaborative operation of temperature-controlled load and energy storage on the carrying capacity of the distribution network was further analyzed. Next, the equilibrium index of power flow distribution, representing the distribution network's bearing capacity, and the carbon emission index, representing the distribution network's carbon footprint, were used as an objective functions. The economic profit opportunity constraint is embedded to construct a two-layer programming model. The outer layer is the location problem of landscape storage, and the inner layer is the constant volume problem. After applying second-order cone relaxation, a hybrid algorithm combining the sparrow search algorithm and solver was used to solve the model efficiently. Finally, the example shows that the coordinated operation of energy storage and temperature-controlled loads contributes to a uniform spatiotemporal distribution of power flow in the distribution network. This coordination improves the permeability of renewable energy and reduces the system's carbon emissions.

Aiming at the problem that the new energies lead to an increase in the peak-valley difference of the power grid and considering the influence of time-of-use electricity price and carbon income, this study proposes an electric vehicle peak-shaving strategy based on the Improved Dingo Optimization Algorithm (IDOA). First, the IDOA dynamically selected by the execution strategy is designed to improve the optimization accuracy and speed of the original Dingo optimization algorithm. Second, an optimal scheduling model of electric vehicles participating in peak load regulation is established, considering the peak-valley difference in load, charging cost, discharging income, and sales carbon quota income. The constraint conditions are introduced into the optimal scheduling model as a penalty term to form the optimization value function that IDOA solves. Finally, the proposed IDOA and optimal scheduling model are simulated and verified. The results show that IDOA has good results in optimization speed, accuracy, and robustness compared to the other four algorithms. The peaking model solved by IDOA reduces the peak-valley difference of power grid load and lowers car owners' costs.

After the "double carbon" target is put forward, renewable energy power generation continues to increase, the proportion of traditional thermal power units decreases, the inertia of the power system decreases, and frequency security faces challenges. The flywheel energy storage virtual synchronous generator (VSG) has the ability to provide fast response and inertia support to improve the frequency characteristics of the power system. This study first establishes a VSG model of flywheel energy storage, and the dynamic response characteristics under different damping states are analyzed. Similarly, flywheel energy storage VSG's inertia and fast response advantages were verified, and the optimal control parameters of the 2.5 MW/0.5 MWh flywheel energy storage array VSG were determined. Furthermore, a frequency response expansion model of a power system with flywheel energy storage VSG is established. The inertia response and frequency modulation ability of flywheel energy storage VSG are expounded. Additionally, the simulation analysis of a regional power system in the Matlab/Simulink environment is performed to verify that the frequency modulation auxiliary and inertia support functions of flywheel energy storage VSG can suppress the frequency drop of the power system during power disturbance, raise the lowest frequency point, and improve the frequency deterioration of the power system.

Compressed carbon dioxide energy storage (CCES), a new type of compressed gas energy storage technology, has the advantages of high energy storage density, low economic cost, long operation life, negative carbon emissions, etc. It is suitable for large-scale, long-term energy storage systems for construction and sustainable development in China and has a broad development prospect. This paper intuitively shows the advantages of a CCES system compared with a compressed air energy storage system. It introduces the operation principle, system performance, and applicable scenarios of cross-critical, supercritical, and liquid CCES system. Similarly, this paper also expounds on the influence of key operating parameters on the system performance and its improvement method, and further introduces the improved system and improvement effect of CCES coupled to other external energy systems. Finally, the advantages and development direction of the CCES system is analyzed. This paper aims to summarize the current research results of CCES technology, point out its advantages and disadvantages, guide subsequent scholars to study the CCES systems, and provide reference for the experiment and demonstration of CCES systems.

Lithium-ion battery equalization systems are primarily used to address inconsistencies during battery pack operation. However, existing studies lack a theoretical basis for selecting the equalization threshold when considering multiple equalization metrics. To address this problem, the paper proposes a computational framework based on the Non-dominated Sorting Genetic Algorithm-II (NSGA-II) to optimize the equalization metrics of the Li-ion battery equalization system. First, the equalization threshold (Δ_V ) is used as the problem parameter, and the equalization speed, the number of switching actions, and SOCconsistency are considered as multiple equalization indicators to establish the objective function. A method for determining the relationship between the threshold and equalization indicators is given to establish the problem model for optimizing Li-ion battery equalization indicators. Then, the NSGA-II algorithm is used to optimize the multiple equalization indicators and design the corresponding decision strategy. Finally, the effectiveness of the proposed algorithm is verified under New European Driving Cycle (NEDC) and Highway Fuel Economy Test (HWFET) conditions. The results show that the switching frequency of the optimal threshold Δ_V = 0.0232 is 42% of the empirical threshold Δ_V =0.01 for the NEDC condition, with similar battery pack consistency and equalization speed. Similarly, the switching frequency of the optimal threshold Δ_V =0.0156 is 43.6% of the empirical threshold Δ_V =0.01 for the HWFET condition. The proposed method in this paper addresses the challenge of determining the equalization threshold and enables a more scientific and effective design of equalization systems.

The moisture content in electrode sheets significantly affects the performance and safety of lithium-ion batteries, necessitating strict control through vacuum drying during production. Currently, vacuum drying research mainly relies on time-consuming experiments that utilize substantial energy, materials, and human resources. To address these issues, we developed a two-dimensional rotating diffusion-flow-heat conduction coupling model using an 18650 battery as the research model, which accurately predicted the change in the moisture content of the cell during the vacuum drying process. The results show that the temperature, water vapor partial pressure, and water content at different core positions are relatively uniform, and the homogeneous zero-dimensional model can yield accurate results. The core material's particle size, porosity, and initial moisture content significantly impact the water evaporation rate, with drying time varying by several hours to reach the same dryness level. The equilibrium moisture content depends on temperature and air humidity which are the main factors affecting the final moisture content. Enhancing the cell's temperature or reducing the oven's outlet pressure can improve vacuum drying efficiency and yield products with lower water content. Heating the core more rapidly and transitioning to the vacuum stage earlier can significantly improve early-stage drying efficiency; after entering the vacuum stage, periodic air exchange in the oven can lower the core's water vapor partial pressure, thereby enhancing the drying rate and reducing the final moisture content, saving both time and cost. The proposed prediction model provides a fast and convenient method for studying the impact of various process parameters and has application value in optimizing lithium-ion battery vacuum drying process parameters.

In this research, the finite element method is used to optimize the gradient pore density of the metal skeleton by using three different pore densities; five metal skeletons with different directions and gradients are prepared, and the heat transfer characteristics of the gradient pore density arrangement in different directions under the heating of the side wall are studied. The results show that the longitudinal negative gradient pore density skeleton has the best heat transfer effect; on this basis, the gradient distribution is optimized and the best working condition is obtained when the three-stage gradient is adopted, i.e., the longitudinal negative gradient two-time working condition. The longitudinal negative gradient structure considers the influence of skeleton heat conduction and flow heat transfer, thereby enhancing the heat transfer effect of the melting dead zone at the square cavity corner and improving temperature uniformity. Compared with the uniform skeleton, the complete melting time of the longitudinal negative gradient two-skeleton is shortened by 8.2%, especially after the liquid phase rate reached 0.9; the melting rate is increased by 29% and 14% compared to the transverse-negative gradient and the uniform square cavity, respectively. In comparison to the transverse and positive gradients, the phase change heat storage rates under the longitudinal negative gradient conditions are increased by 17% and 11.6%. In this study, it is proposed that the preparation of composite phase change materials by multi-stage gradient pore density metal framework is helpful to strengthen the energy storage efficiency of composite phase change materials without increasing the skeleton volume and provide a theoretical basis for better energy utilization.

Lithium-ion batteries are the mainstream energy storage component for electric vehicles. The reduced reliability of lithium-ion batteries leads to abnormal performance degradation or frequent failures for electric vehicles, resulting in accidents that threaten safety. The study of battery fault diagnosis and the state of health estimation technology has become a research hotspot in the field of lithium-ion battery reliability. The deep integration of big data and electric vehicles has provided new insights into the development of key technologies for improving the reliability of lithium-ion batteries. Herein, the data characteristics of the big data platform for new energy vehicles and the data cleaning methods they utilize are first introduced. The application of key reliability technologies based on the findings from big data in electric vehicles and big data platforms is briefly reviewed. Furthermore, the previous research on battery fault diagnosis and state of health estimation analyzing the reliability of lithium-ion batteries is reviewed. Considering a data-driven model as the core method of inquiry, the research status and methods used to analyze big data pertaining to the fault diagnosis and state of health estimation of lithium-ion batteries are discussed. The advantages and disadvantages of machine learning, statistics, signaling, and fusion models in battery fault diagnosis are discussed. The theoretical basis for extracting features based on historical operating data and incremental capacity analysis is reviewed, and the battery state of health estimation models are sorted appropriately. Finally, the limitations and challenges of the current research in data cleaning, fault diagnosis, and health status prediction of lithium-ion batteries are summarized. Thus, this paper provides the future direction for the development of key reliability technologies for estimating the reliability of lithium-ion batteries.

Lithium-ion batteries are extensively used in electric energy storage and new vehicles due to their high energy density and long cycle life. Accurate estimation of the battery's state of charge(SOC) is crucial for improving its service life and utilization efficiency. However, lithium batteries are a highly complex, time-varying, and nonlinear electrochemical system. Thus, an online SOC estimation method with high accuracy is vital for the practical application of lithium batteries. In recent years, model-based SOC estimation methods have gained widespread attention and research because of their closed-loop control and ease of implementation. This paper reviews model-based SOC estimation methods from the aspects of model classification, model parameter identification algorithm, SOC estimation algorithms, and factors influencing SOC estimation. First, various common lithium-ion battery models are summarized, primarily focusing on introducing and comparing common electrochemical and equivalent circuit models. Then, the model establishment methods and SOC state estimation algorithms are examined and compared; various model parameter identification methods and SOC estimation calculation methods are introduced and contrasted. After that, the influencing factors and solutions of the model-based SOC estimation method are analyzed and summarized, mainly addressing the impact of temperature, aging, and battery pack factors on battery SOC estimation. Finally, potential future research directions are discussed and explored.

In energy transformation, national policies require energy storage to achieve marketization and scalable transformation. However, due to inaccurate perceptions of users' needs, manual research and selection by suppliers; user selection is inaccurate, subjective, and inefficient, making the development of energy storage business challenging. To solve these issues, this paper proposes an active identification method for target users of the integrated energy service provider (IESP) user-side energy storage business. First, based on multi-source data, a user-side energy storage target user active identification feature library is constructed, considering users' multiple power demands and service value characteristics. This feature can reflect users' information demand, such as energy-saving, efficiency-enhancing demands and power quality demand, as well as supply information such as whether users are worth serving by the supplier. Secondly, we developed an improved GRA-TOPSIS user feature quantification model to address the limitation of traditional quantification methods, where the influence of the deterioration index is easily compensated by the advantage index, resulting in inaccurate recognition results. Thirdly, according to the quantitative results, we establish a target user activity identification coordinate system to visualize a target user activity identification results, which provides support for implementing energy storage services, and help IESP in intuitively identifying target user. Finally, the feasibility and effectiveness of the proposed method are verified through a case study.

After putting forward the two-carbon goal, the country has conducted planning and layout for energy development and transformation. The plan points out that the new power system is an inevitable choice for our country to achieve its double carbon goal. With a large amount of renewable energy connected to the grid and several electronic power devices connected, it will significantly challenge grid stability and power quality. In such a context, new energy storage is a key technology to overcome this challenge. Under the guidance of various policies of the state, provinces, and cities, the new energy storage industry has entered the rapid commercialization phase from the exploration and development stages. It needs more support from relevant policies and compensation mechanisms. Therefore, it is considerable to study and analyze the current domestic policies and effectively rectify their imbalance and irrationality. This study introduces a specific scale of the current domestic new energy storage and the future planning layout, starting with the development status of new energy storage. Second, it combs through the relevant national policies and the compensation means of each province and points out the rationality and reference of some provinces' compensation mechanisms. Furthermore, according to different application scenarios, the new energy storage business model is analyzed in detail and further elaborated by referring to the actual project. Finally, combining the actual policies and specific applications, the shortcomings of policy formulation are found, and suggestions are put forward for the current commercialization process of new energy storage, which has specific reference values for improving the policy system.