Based on the filling effect between large and small particles, we designed seven grading systems using two sizes of active particles and studied the performance of the slurry systematically. The particle size distribution, surface area, particle morphology, electrical conductivity, sedimentation rate, electrochemical impedance spectrum, and charging and discharging properties of the slurry were analyzed using a laser particle size analyzer, conductivity meter, specific surface area measuring instrument, scanning electron microscope (SEM), sedimentation test, and electrochemical test. The results indicate that the particle distribution is a single peak close to the normal distribution. The particles have either macroporous or nonporous structures. The small particles are typical single-crystal structures, and the large particles comprise spherical particles with relatively larger diameters. The SEM images of 3∶7 and 0∶10 samples show that a large particle is surrounded by small particles and a conductive agent, forming a complete continuous three-dimensional conductive network. The conductivity of slurry formed by large particles is 41.80 mS/cm, and that of slurry formed by small particles can be as high as 123.39 mS/cm. The 3∶7 sample's sedimentation and settling rates are the smallest, and the particle distribution is closer to Fuller's grading curve of densely packed particles. The 0.1 C specific discharge capacity of the 3∶7 and 5∶5 samples reached 194.88 and 187.38 mAh/g, respectively, with actual specific capacities 10% and 5% higher than the commercial typical values, and the first cycle charging-discharging efficiencies were as high as 90.54% and 87.96%, respectively. Moreover, the best cycling performance of 83.63% capacity retention after 250 cycles was provided by the 3∶7 sample. In summary, the 3∶7 sample showed the most effective filling effect and best electrochemical performance. In this study, we could realize the best particle ratio grading system, providing an essential reference for particle gradation systems with excellent performance.

LiNi _x Co _y Mn_1-x-y O₂(NCM, x≥0.6) cathode materials have piqued much interest in Lithium-ion battery because of their high energy density and low-cost. However, the lithium-nickel cations mixed arrangement became serious, and thermal stability decreases as nickel content increases, resulting in deterioration of cycling stability and safety concerns. This paper successfully doped the Mg into NCM using co-precipitation method, followed by Al₂O₃ coating. The as-prepared LiNi_0.8Co_0.1Mn_0.09Mg_0.01O₂@Al₂O₃ (Mg1.0@Al) X-ray diffraction, scanning electron microscopy, and transmission electron microscopy results show that Mg doping can effectively expand the spacing in the crystalline as well as buffer cation mixing. Meanwhile, the Al₂O₃ coating protects the crystals from the cathode-electrolyte side reaction. The electrochemical measurements revealed that the synergistic effects of Mg doping and Al₂O₃ coating can help to stabilize the crystal structure and reduce interfacial polarization. The Mg1.0@Al demonstrated stable discharge voltage (ΔV=5.2 mV), low charge transfer impedance (R_ct=51.66 Ω) and excellent lithium-ion diffusion coefficient (D_Li=4.05×10^-14 cm²/s) after 100 cycles at 1 C. At the voltage range of 2.8—4.3 V, the discharge specific capacity of Mg1.0@Al cathode remains 188.58 mAh/g after 100 cycles and 147.47 mAh/g after 400 cycles, with capacity retention rates of 95.18% and 74.54%, respectively. When changed at 5 C, the discharge specific capacity increased to 146.3 mAh/g.

Aiming to solve the problem that the phase change temperature of a single fatty acid is fixed and difficulty in matching the actual demand, we propose taking five common fatty acids—decanoic acid (CA), lauric acid (LA), tetradecanoic acid (MA), palmitic acid (PA) and stearic acid (SA)—as phase change materials and compound them in pairs. The lowest melting point and theoretical mass ratio of 10 binary composite systems are calculated using the low melting theory. A binary low eutectic composite system was prepared by melt blending, and the mass ratio was adjusted by 3%—6% above and below its low eutectic point. The phase change characteristics of the binary low eutectic composite system were measured by DSC. Experimental results show that the theoretical phase change temperature range of the binary low eutectic system is 21.58—53.90 ℃, and the theoretical phase change latent heat range is 157.64—191.85 J/g. Compared with the thermal properties of the five single fatty acids, the phase change temperature is reduced by about 10—15 ℃, and the phase change latent heat value has no obvious change. The phase change temperatures of the 10 binary low eutectic systems range from 19.94 to 56.49 ℃, with a deviation of 1.93%—14.72% from the theoretical phase change temperature. The phase change latent heat range is 125.78—181.45 J/g and the deviation from the theoretical phase change latent heat range is 0.18%—19.86%. The theoretical phase change temperature range of CA binary systems is 21.58—30.11 ℃, which is applicable to the field of building energy conservation. The theoretical phase change temperature range of LA binary systems is 35.87—41.15 ℃, which is suitable for the thermal management of electronic devices or temperature-regulating textiles. The theoretical phase change temperature range of MA and PA binary systems is 46.05—53.9 ℃, which is suitable for the temperature control field of mass concrete. By adjusting the ratio near the eutectic point, we found that the deviation between the optimal ratio and the theoretical calculation of the eutectic ratio was within 4%, verifying the accuracy and feasibility of the theoretical calculation. The results of this study can provide a technical reference for the specific application range of fatty acid binary composite phase change materials.

Transition metal phosphides (TMPs) are promising supercapacitor electrode materials due to their high electrical conductivity and large specific capacitance. In this study, the Cu-NiCoP microspheres were prepared from solvothermal and phosphating reactions, and the effect of Cu doping on the electrochemical properties of NiCoP was explored. The results of this study show that the electrochemical properties of the material are the best when the amount of Cu doping is 5% with the associated specific capacitance reaching 1500 F/g(at a current density of 1 A/g), which is much higher than that of the undocumented NiCoP microspheres (the specific capacitance is 1025 F/g at a current density of 1 A/g). The 5% Cu-NiCoP and activated carbon are assembled into asymmetric supercapacitors (ASC) with positive and negative electrodes, respectively. The assembled ASC devices demonstrate supreme cycle life stability with a capacity retention of 76% after 9200 cycles at a current density of 2 A/g. The ASC devices also show desirable multiplier performance, with a high energy density of 84 Wh/kg at a power density of 750 W/kg. Therefore, this work suggests that Cu doping can effectively improve the electrochemical properties of NiCoP electrodes, and Cu-NiCoP has the potential to be used as cathode materials in supercapacitor energy storage.

The solid-electrolyte interface (SEI) on the highly reductive negative electrode surface of lithium-ion batteries is a key component affecting the electrical performance and stability; however, the formation of SEI involves complex processes in multiscale and multiphysical fields with extremely complex components. In the "black box" environment of a battery shell, the existing technology cannot characterize SEI, and in situ technology is difficult to obtain highly precise results. Using mathematical methods to model SEI is expected to decouple the complex physical fields and accurately describe the mechanisms and processes of SEI formation and evolution, which is a research hotspot in the field of battery. In this study, first, we describe the main methods and progress of SEI modeling from the atomic scale to mesoscale, including first-principles classical molecular dynamics, reactive molecular dynamics, classical molecular dynamics, Monte Carlo simulations, and macroscopic models. We also summarize some modeling applications in guiding electrode material synthesis and electrolyte modification, focusing on the difficulties and shortcomings of multiscale modeling. Next, we propose a force field algorithm platform based on the electrochemical potential field characteristics of SEI, expanding the modeling to tens of thousands or even hundreds of millions of atoms using the kinetic Monte Carlo simulations and machine learning assistance. Then, calculations are performed step by step, combining experimental verification and expert evaluation to promote convergence. Finally, we obtain an SEI model with quantum mechanical accuracy and electrochemical potential field, which is expected to realize SEI modeling at various lengths and timescales.

Rechargeable Zn-air battery is considered a promising green energy device due to the advantages of high specific energy, stable working voltage, good safety, and no environmental pollution. However, the air electrode of rechargeable Zn-air batteries requires reversible oxygen reduction and evolution reactions during the discharging and charging process. Because these reactions involve a solid-liquid-gas three-phase interface, the kinetic process is very slow. Therefore, it is essential to design air electrodes with efficient catalytic action. In this study, first, we describe the principle of rechargeable Zn-air batteries and the structure of air electrodes. Second, we present the structural characteristics and performance differences between traditional air electrodes and integrated air electrodes. By surveying the recent literature related to the integrated air electrodes of rechargeable Zn-air batteries, the preparation of integrated air electrodes comprising different conductive substrates and catalysts and the research progress of their Zn-air battery performance are reviewed. Further, the advantages, disadvantages, and existing problems of carbon-and metal-based integrated electrodes are summarized, and the future optimization and improvement directions of these two electrodes are proposed. The relevance and improvement strategies of the three-phase interface structure of air electrodes are also presented; the analyses prove that constructing reasonable three-phase interfaces can effectively improve the transport kinetics of electrochemical reactions. Finally, the difficulties in the practical application of rechargeable Zn-air batteries are highlighted.

Faced with the demand for steam heating in the industrial field, we will vigorously develop high-temperature phase change heat storage technology, effectively adjust the peak and valley loads of power grids, effectively promote the replacement of electric energy, and help achieve the goal of "carbon peak and carbon neutrality." This article comprises a literature review of the principles and methods of phase change material optimization. High-temperature phase change materials are also classified. The latest research trends of high-temperature composite phase change materials are emphatically described, including metal foam/inorganic salt, graphite foam/inorganic salt, expanded graphite/inorganic salt, porous ceramic/inorganic salt, and porous clay mineral/inorganic salt composite phase change materials. Notably, high-temperature composite phase change materials can improve the low thermal conductivity and stability of inorganic salts and corrosion of sealing materials. Next, the preparation methods of high-temperature phase change materials are summarized; the advantages and disadvantages of the infiltration method, sol-gel method, and cold pressing sintering method in practical application are highlighted. In comparison, the cold pressing sintering method is the most cost-effective for preparing salt matrix composites. Finally, the application statuses of high-temperature composite phase change materials in industrial waste heat recovery, power peak regulation, and solar thermal power generation are discussed, providing a basis for studying the capacity allocation and economic evaluation methods of high-temperature phase change heat storage systems of the steam type under different scenarios. This review has a certain reference value for the development of high-temperature phase change heat storage technology.

Phase change thermal energy storage is one of the energy storage technologies with a wide range of applications due to its advantages of high heat storage density and stable phase transition temperature, but the low coefficient of thermal conductivity of phase change materials (PCM) has hampered the further development of this technology. It is an efficient method of increasing the thermal conductivity of PCM as well as the heat transfer rate of the thermal storage device. There have been a large number of review articles published on the research progress of thermal conductivity enhancement of PCM, but there have been fewer summaries for heat transfer enhancement of the heat storage device. This paper reviews the research progress of heat storage devices and their heat transfer enhancement over the last decade. To meet various application needs, different types of phase change heat storage devices emerges. Based on its working mode and structure, it is classified into four types: shell and tube type, filled bed type, plate type, and heat pipe type. The working principle, advantages and disadvantages of the four types of heat storage devices, and the progress of heat transfer enhancement research are systematically summarized, majorly comparing the heat transfer rate and charge/discharge performance of conventional heat storage devices and those after structural optimization. The results show that improving the internal structure of the heat storage and expanding the external structure can effectively increase the heat storage capacity and charge/discharge rate, which improves system capacity. The finding demonstrate that future research should focus on clarifying the multi-phase coupling heat transfer mechanism inside the heat storage device, improving the heat storage device's adaptability to dynamic working conditions, and broadening the application range.

Due to the low thermal conductivity of the phase change materials (PCM) in the cooling battery, the copper tree fins embedded in the envelope structure of the PCM are proposed to release heat in time. A mathematical model was established to describe the dynamic melting and heat transfer process of the tree-finned PCM using the enthalpy-porosity method. The influence of the envelope with the tree-finned PCM of different structures on the cell temperature was numerically analyzed at discharge rates of 1 C, 2 C, and 3 C. Compared with using paraffins as the envelopes, the cell temperature can be reduced by 1.4 K with straight fins. When embedded with tree fins, however, the cell temperature can be reduced by 1.4 K compared to that with straight fins. When the ratio of the branch length to trunk length is 1.2, the PCM envelope embedded with tree fins can obtain better heat transfer characteristics for battery thermal management systems.

First, a pumped storage unit with a capacity of 300 MW and a flywheel storage array with a capacity of 25 MW are modeled and their corresponding charging and discharging characteristics are analyzed. Then, a coordinated control strategy is proposed for the pumped and flywheel hybrid energy storage system, based on the ramp input control strategy for the hydroelectric unit and the state of charge (SOC) segmentation control strategy for the flywheel energy storage array. The proposed method is aimed to improve the integrated frequency regulation index of the pumped storage unit by a factor of 2 and reduce the wear and tear of the unit for reduced loss cost. Finally, the proposed coordinated control strategy for the hybrid energy storage system is verified by numerical experiments using the historical operation data of a pumped storage unit with a rated power of 300 MW. In the process of secondary frequency regulation of the power grid, the results show that the proposed coordinated control strategy of the hybrid storage system can improve the performance index of the integrated frequency regulation by more than 2.29 times for the pumped storage unit. In addition, the frequent output adjustment is significantly reduced during the smooth operation of the pumped storage unit, leading to reduced loss cost and improved steady-state operation characteristics for the pumped storage unit. Moreover, the SOC of the flywheel energy storage array is also maintained at a reasonable level.

With the continuous advances in carbon peaking and carbon neutrality goals, the installed capacity of renewable energy will continue to grow. However, renewable energy power generation is unstable. Therefore, it is essential to analyze the impact of changes in the installed capacity and energy storage system capacity of renewable energy on power grid performance. Considering wind power, photovoltaic power, thermal power, and energy storage, a power system dispatch model of one province grid is proposed in this study, and information gap decision theory and the Monte Carlo method are used to simulate the uncertainty of user load and renewable energy power generation. The simulation results show that, when the installed capacity of renewable energy is doubled, the proportion of renewable energy power generation increases by 62.6%; also, the operating cost increases by 18.7%. When the energy storage system capacity is increased from 0 to 15 GW, the operating cost decreases by 14.4%, and the proportion of new energy power generation increases by 22%. In addition, when the new energy consumption rate is lower than 95%, an increase in energy storage capacity will mainly increase the new energy consumption; when it is higher than 95%, an increase in energy storage capacity will significantly reduce the power grid volatility. This study provides certain guiding significance for configuring the installed capacity of renewable energy and energy storage systems for constructing low-carbon energy and smart grids in the future.

Multiheterogeneous energy storage technology plays an active role in the flexible operation and resource coordination of integrated energy systems. In this study, we propose a day-ahead game-robust optimization operation method for integrated energy systems considering flexible economic regulation of electric/thermal energy storage. This method taps the economic potential of electric/thermal heterogeneous energy storage in energy trading and the flexible potential under uncertain factors. First, an integrated energy system scheduling model and energy storage operation model are established considering the security constraints of state quantities such as node voltage and pipeline temperature. Then, under the framework of electric heating energy trading, the Stackelberg model and the MPEC form of the game between the integrated energy service provider and the prosumers are established considering the economic operation of energy storage. Next, based on the Stackelberg model, a two-stage robust optimization model is proposed considering the flexible intraday adjustment of battery energy storage and is solved by an improved column constraint generation algorithm. Finally, an example is given to verify the effectiveness of the proposed method. The flexible and economic regulation of energy storage can reduce the system operation cost and ensure the balance of intraday power supply and demand.

The new power grids with the high penetration of new energies are more prone to load imbalance between the generation side and the user side, resulting in fluctuations in the grid frequency, which brings great challenges to frequency safety. Improving the active frequency modulation support is an effective method for the generation side. The energy storage technology, which assists the thermal power units participating in the primary frequency regulation, can not only improve the safety of power grids, but can also reduce the wear of the units and for more economic unit operations. The existing configuration method of the primary frequency modulation energy storage capacity is relatively simple. Hence, a configuration method is proposed for the hybrid energy storage system to assist the thermal power frequency modulation, based on the empirical mode decomposition (EMD). By adopting the EMD, the hybrid energy storage system power obtained by the target power is processed, and the modal components obtained from the EMD decomposition are reconstructed to determine the energy storage power allocation. Moreover, the power and capacity planning is conducted, and the optimal optimization scheme of the hybrid energy storage system is determined by taking the whole life cycle cost of the hybrid energy storage as the optimization objective. Our proposed method makes up for the unreasonable energy storage configuration scheme without fully considering the high- and low-frequency response of power unit, the high investment cost of energy storage caused by the inability to fully utilize different forms of energy storage characteristics, the low income of energy storage system, and the unsatisfactory frequency modulation effect. Finally, the effectiveness and economic aspects of the above planning methods are verified using a specific example in Suqian. This study can provide some technical references for the planning of hybrid energy storage in the frequency modulation of thermal power units.

In this paper, the optimal configuration of energy storage systems in active distribution networks with reliability in mind is investigated. First, a reliable calculation method for power supply reliability of a distribution network with a source is proposed, taking into account load time series, distributed photovoltaic output, and energy storage system operation. Then, with reliability in mind, an optimal configuration model of an energy storage system in an active distribution network is proposed. The model comprehensively considers the distribution network's reliability and economy indexes, and the constraints include distribution network power flow, system power balance, photovoltaic output, and energy storage system operation. Finally, using a 17-node distribution network as an example, the genetic algorithm is used to solve the model in this paper, resulting in the optimal installation location and capacity of the energy storage system in the photovoltaic distribution system. Furthermore, the effects of various installation schemes including synchronous configuration of energy storage and distributed photovoltaic, centralized configuration of energy storage on the system side, and setting of weight coefficients of sub-objectives on the objective function are evaluated and discussed.

The '3060 double carbon' goal promotes energy transformation in China. The uncertainty and complexity of the power system associated with the high penetration of renewable energy would increase the demands for regulated power supplies and resilience response capability to accommodate extreme natural disasters and man-made attacks, which facilitates the large-scale application of new energy storage technology in the resilient power system. This paper discusses, in detail, the application of energy storage in resilient power systems under extreme events. Firstly, based on the development trend of energy storage, this study combines the concept connotation, the measurement elements of resilient power systems, and the characteristics of extreme events to explore the necessity of the demand and the application target of energy storage in resilient power systems. The function process, mechanism, and regulation target of energy storage are proposed for the two stages of resilient bearing and recovery under extreme events. Secondly, the application characteristics and mechanism are analyzed for energy storage in the bearing and recovery stages of the resilient power system. Thirdly, this work analyzes the relationship between the aggregate capacities of the decentralized energy storage and the bearing capacities of the resilient power system, and the impact of energy storage on post-disaster network reconfigurations is also assessed. Improvements in the resilient index evaluation framework and the electricity market mechanism formulation under the increasing energy storage capacity are examined in this work. Key technical points are proposed, such as planning, regulation, and quantitative indicators for the resilient application of energy storage. Then, this study proposes the typical scenarios considering the application requirements for extreme events, energy storage performance, and economy. Finally, the perspective of the application of energy storage for resilient power systems in China is discussed.

The lithium-ion batteries (Libs) have been widely used due to their advantages of high energy density and long cycle life. However, the associated lithium plating can cause irreversible capacity fading, reduce the self-heating temperature, and lead to safety hazards, although the lithium stripping process can partially alleviate the effect of lithium plating on the battery. In this paper, the batteries are placed for cycling at low temperatures. The external characteristic methods, which contain the differential voltage analysis (DVA), voltage relaxation profile (VRP), and DVA-VRP, are assessed to quantitatively analyze the reversible lithium stripping process by collecting and analyzing the cycling process of the batteries. The results are verified by the electrochemical model. The present study shows that the features of the DVA and VRP shift along with battery aging. The eigenvalues of these methods are linearly related, and the fitting line shifts downward with the extending rest-time. The results show that the VRP method is more accurate and more time-consuming, whereas the DVA method has higher accuracy in the preliminary stage and the deviation gradually increases with battery aging. The DVA-VRP method can effectively retain accuracy without the disadvantages of large errors and high time costs, and it can also estimate the reversible lithium of the battery in a short time. The method can be used to guide the safety assessment of lithium-ion batteries.

In the context of carbon peak and carbon neutrality, shortening the operation simulation time and improving the accuracy of simulation results are of great significance for lithium batteries in large-scale energy storage power stations to improve the CFD simulation efficiency and safety management level. Aiming to solve the problem that the existing simulation models cannot support the fast and accurate simulation of their operation states, we propose a method for constructing a thermoelectric coupling model of lithium batteries based on the digital twin. First, the digital twin structure system of lithium batteries is designed and the principle of thermoelectric coupling model construction is analyzed. Second, considering that the LTI reduced-order model of the ANSYS TwinBuilder platform has the characteristics of short calculation time and high simulation accuracy, the coupling mechanism between the thermodynamic model and the equivalent circuit model is analyzed. In addition, a lithium battery thermoelectric coupling digital twin model is established based on the ANSYS TwinBuilder. Further, the parameters of the equivalent circuit model and thermodynamic model are identified offline by the least-squares algorithm and principle analysis, respectively. Considering the influence of factors such as aging and temperature, the recursive least-squares algorithm is used to identify the parameters of the equivalent circuit model online. Finally, the lithium battery equivalent circuit model and thermodynamic model are built in Simulink and ANSYS Icepak, respectively, and coupled to the ANSYS TwinBuilder platform. Moreover, we perform simulation analysis from multiple dimensions. The results of the comparison between the simulation and experimental values demonstrate the effectiveness and accuracy of the proposed model.

To improve the accuracy of the lithium battery model and realize an accurate estimation of the lithium battery state, a second-order fractional electrical model is established for the lithium battery based on the second-order RC equivalent circuit. In this study, the adaptive genetic algorithm is used to realize the parameter identification of the fractional order model, which can increase the convergence speed, reduce the identification time, avoid falling into the local optimal solution, overcome the parameter dispersion, and improve the accuracy of the model parameters. Based on the fractional order electrical model, a state estimation method is proposed for the unscented particle filter by adopting the Schmidt orthogonal transformation. Instead of using traditional unscented particle filters, a method that combines standard sampling with the Schmidt orthogonal transformation is adopted in the selection of sampling points to screen the symmetrically sampled particles, which leads to a reduced number of sampling points and an improved calculation efficiency. In addition, it can also limit the divergence of the estimated value caused by the nonlinearity of the system or the particle shortage caused by a small particle number for the particle filter algorithm. The simulation results show that the established fractional order electrical model can more accurately account for the dynamic characteristics of charging and discharging for lithium batteries, and the proposed state estimation strategy demonstrates higher accuracy than the conventional control strategy. In general, the system robustness is improved, and the SOC of lithium batteries can be estimated with an error of within 1%. Moreover, the overall calculation efficiency is improved, which makes it easy to realize the algorithm in real-time.

The state of charge (SOC) of batteries is one of the most important indicators for battery management. An accurate SOC estimation is necessary to ensure the safe and effective operations of lithium-ion batteries. To improve the accuracy of the SOC estimation of lithium-ion batteries, this paper proposes a SOC estimation method by adopting the integration of the unscented Kalman filter (UKF) and the back propagation (BP) neural network, based on the second-order Thevenin equivalent model.By obtaining the model parameters through hybrid pulse power characteristic tests, the UKF algorithm is used to estimate the initial SOC of the battery. The nonlinear point transformation method is used to avoid the accuracy loss caused by the system linearization process in the extended Kalman filter (EKF). Then, a three-layer BP neural network is constructed, and the estimation errors are corrected by considering the charging and discharging voltage and current, along with other parameters of lithium-ion batteries. The estimation errors are then excluded from the initial estimation results to achieve more accurate results. The charging and discharging results of lithium-ion batteries in the dynamic stress test were collected by the battery charging and discharging tester. The BP-UKF algorithm proposed in this paper was compared with the EKF algorithm and the traditional UKF algorithm under different noise environments. The experimental results show that the maximum error of the proposed BP-UKF algorithm is within 2.18%, the mean absolute percentage error is within 0.54%, and the root mean square error is within 0.0044, demonstrating evident improvements compared with the other two algorithms. In addition, the accuracy of the BP-UKF algorithm is improved more significantly under the condition of large environmental noises.

The performance of a lithium battery degrades gradually with increasing use time. If the replacement is not completed on time, serious accidents such as explosions may occur. Rapid and accurate prediction of battery state of health (SOH) is critical for lithium battery system management, maintenance, and safe use. In this paper, a machine learning model based on indirect His (health indicators) and GPR (Gaussian process regression) is proposed to predict the SOH of lithium batteries. First, the analysis of the lithium battery discharge process extracts some easily available and suitable for the direct external features of dynamic operations as indirect His, and their correlation with SOH, eventually selecting average discharge voltage, such as pressure drop discharge time, maximum discharge temperature, and platform stage discharge voltage initial plummet in value as the health index. Second, using the above mentioned His as input features, the GPR algorithm is used to establish a lithium battery degradation model, and the MAE (mean absolute error) is less than 2% for the prediction of NASA lithium battery datasets, while the RMSE is kept within 4%. Finally, the model is compared to other commonly used machine learning models, and then into multiple experimental conditions of battery model generalization performance analysis, control about 6% of the maximum error of the prediction. The experimental results show that the proposed indirect His and GPR have relatively higher prediction precision and good generalization ability.

Accurate estimation of the state of charge of Li-ion batteries is required to guarantee the safe operation of battery systems. SOC estimation methods based on recurrent neural networks, like Gated Recurrent Unit, have recently received much attention because they can achieve accurate SOC estimation without using pre-defined battery models. However, due to their high computational complexity, these estimation methods are difficult to apply in engineering. To address the issues of high computational complexity caused by the large number of hidden state iterations required for SOC estimation in traditional GRU neural networks, a recursive update method with hidden state temporal succession is proposed, and it is possible to obtain the accurate SOC estimate at the current moment with only one network calculation of the sampled data at the current moment by improving the output structure of GRU networks. When compared to the traditional GRU method reported in the literature, this recursive GRU method can reduce the computational effort by more than 99% while maintaining SOC estimation accuracy, which has a better application prospect. Furthermore, in some application scenarios where there is a lack of battery training data, the method can combine migration learning to quickly complete network training. The method has been validated using laboratory test datasets and public datasets, and it is capable of performing accurate SOC estimation for different temperature environments, aging states, and Li-ion battery models, with a maximum estimation error of less than 3%.

With the rapid development of the new-energy industry and the concept of "integration of source network, charge, and storage", lithium-ion batteries have been widely used for energy storage. To control the group cost, the battery modules applied in the field of energy storage are developing towards high voltage and large capacity, which puts forward higher requirements for the grouping technique of lithium-ion batteries, especially for soft-pack batteries. In this paper, based on the theoretical calculation and finite element analysis method, the expansion force analysis of the soft package large module for energy storage is carried out to investigate the structural stability of the module in the whole life cycle. The results show that the selection of foam, the material of the end plates, and the number and strength of the fixing bolts used for the end plates are important factors in the design process of the large soft pack module for energy storage. A supreme design scheme can effectively reduce or even avoid the influence of the battery expansion force on module structures, improve the structural stability of the module, and produce modules with better safety and longer service life.

A platform is designed based on the thermal performance testing methods and testing processes of solid electric heat storage devices proposed in Thermal Storage Electric Heating Devices (GB/T39288—2020). By referring to domestic and foreign standards, the platform can test full working conditions (including the pure heat storage condition, pure heat release condition, and storage and release conditions), with improved the thermal and energy storage performance testing for solid electric heat storage devices. The platform can carry out the standard thermal and energy storage performance test for the solid electric heat storage device by improving the test procedures of GB/T 39288—2020 combined with the heat release testing under both non-heating and heating states. To verify the reliability of the platform, one standard test is conducted on a solid electric heat storage device. The platform records the parameters such as electricity, time, and temperature during the test; draws the temperature curve, heating wire current-voltage curve, and heat output load curve during the test; and calculates the thermal and energy storage performances of the solid electric heat storage through improved and supplemented formulas. Based on the calculation results, the energy storage performance diagram is drawn under the rated thermal output power. The diagram clearly shows the effective release temperature range, energy storage capacity, and energy utilization of the solid electric heat energy storage device. The result of the test is found close to the theoretical value and empirical value, which proves that the platform can be used for self-inspection or teaching by equipment manufacturers. The platform also has the function of displaying user-defined heat load curves. This function can help teachers and learners research and learn solid electric heat energy storage devices under real working conditions.

An algorithm based on locally optimized RANSAC (locally optimized random sample consensus, LO-RANSAC) is recommended for the problems of low trace defect detection accuracy, high false detection rate, and high missed detection rate of scratch defect detection on the surface of a lithium battery. First, in response to the problem of pepper noise, large noises that exist on the scratch defects of lithium batteries, a filtering algorithm based on improved adaptive median filtering and connected domains filtering is proposed. Secondly, to address the problems that the detection accuracy of detecting trace defects does not meet expectations and the false detection and missed detection rates are high, a locally optimized RANSAC algorithm is introduced. Finally, a scratch defect classification based on the LO-RANSAC algorithm is proposed. The experimental results demonstrate that when compared to the standard RANSAC algorithm, the proposed algorithm's average detection accuracy is increased by 5.9%, when compared to the convolution-based neural network algorithm is increased by more than 15%, reaching 98.2%. Among several algorithms, the algorithm achieves the lowest false positive and false negative rate for trace defects. The average detection speed is 1.7 times faster than the standard RANSAC algorithm, with an FPS (frame per second) of 12.49 images detected per second. The proposed algorithm has a high detection accuracy, a low false detection rate, and missed detection rate, and a detection speed that meets real-time detection requirements, allowing it to meet the detection needs of trace defect on the surface of lithium battery pole pieces and solve the problem of automatic detection of trace defects on the surface of lithium battery pole pieces.

Various sensors inside the power battery of novel energy vehicles are used to monitor the safety of the battery system, and sensor failure can cause errors in the charge state and other indicators, which can severely trigger the risk of thermal runaway. For effective and accurate battery sensor fault diagnosis, we propose a fault diagnosis method based on genetic algorithm-based particle swarm optimization and fuzzy neural network (GAPSO-FNN). The proposed method is applied to diagnose the sensor faults of lithium-ion batteries. We obtained data on battery sensor faults by combining the hardware platform and Matlab/Simulink environment, then preprocessed and extracted features from the fault data, and finally used the GAPSO-FNN-based method to diagnose battery sensor faults and compared the results with conventional neural network (NN)- and fuzzy neural network (FNN)-based methods. Simulation results show that the GAPSO-FNN-based method improves the accuracy by 25% and 10% compared with the conventional NN- and FNN-based methods and the fault diagnosis accuracy can reach 95%. Thus, the proposed method effectively improves fault diagnosis accuracy while reducing the amount of information required for fault diagnosis.

The economic efficiency of power batteries can be enhanced by the cascade utilization of retired lithium-ion batteries. However, the confusion of batteries' identification information, the difference in charge state, and the overlap of working voltage make it extremely difficult and unreliable to distinguish LiFePO₄ and Ni-Co-Mn oxide-based power batteries by judging only the open-circuit voltage. Therefore, electrochemical impedance spectroscopy (EIS) was employed as a rapid and nondestructive method to identify the power lithium-ion batteries of chemical systems by establishing the dependences among the capacity, interfacial capacitances, reaction resistances, Warburg impedances, and liquid resistance and then investigating the influence of capacity on the real and imaginary parts of electrochemical impedance based on the equivalent circuit of power lithium-ion batteries. The investigation showed that the ratio of the real part to the imaginary part was independent of the capacity. Consequently, the intrinsic feature only involving EIS frequency could be used to quickly identify the power lithium-ion batteries of different chemical systems, avoiding the poor efficiency of charge and discharge routes. Further, the effectiveness of the EIS method was rudimentarily verified with pouch LiFePO₄ and Ni-Co-Mn oxide-based batteries. As a result, LiFePO₄-based power batteries with capacities of 10, 12, and 50 Ah showed that the ratio only varied with EIS frequency and considerably contrasted from those of Ni-Co-Mn oxide-based batteries.

China, Japan, and South Korea currently account for more than 90% of the global power battery market. Since 2020, China's power battery industry has shown a trend of continuous growth, while South Korea and Japan's share of power batteries has continued to decline. With the rapid expansion of the global electric vehicle market, as well as the layout of battery technology and industry in Europe and the US, competition in the global battery industry will become more intense in the future. This paper presents the South Korean and Japanese governments' battery technology and industry development strategies and measures in response to the new competitive situation, as well as the development status of major battery enterprises and automobile enterprises in South Korea and Japan in terms of increasing battery industry and technology investment and expanding the market. This paper examines the trend of battery technology and industry development in South Korea and Japan. The new strategy's implementation will play critical roles in improving domestic battery industry technology, building a battery ecosystem, ensuring supply chain security, and expanding the global market. This paper summarizes China's battery industry's recent national policies, technology, and industrial characteristics, analyzes the impact of new battery strategic initiatives by South Korea and Japan on China, and proposes enlightening and relevant countermeasures and suggestions.