In recent years, body-centered-cubic (bcc)?-type anion framework has been considered as a deft structural design descriptor for identifying unexplored potential sulfide lithium-ion conductors (SICs). However, the bcc guidance must be completed, and other factors impacting lithium diffusion must be considered. Therefore, they are not always useful in the design of new lithium-ion conductors. In this study, based on previous works, we investigated the lithium-ion hopping and diffusion mechanisms in sulfides. We discussed how the local structural environment impacts lithium hopping from site to site in a micro-manner and how the chemical composition impacts diffusion in a macro-manner. In conclusion, we proved that the zero-transition-metal (0-TM) coordinated channel is essential for the facile hopping of lithium ions, and macro-diffusion can be expected in the materials only if the contents of 0-TM channels exceed the penetrating thresholds. Our study provides a more thorough interpretation of the sulfides' lithium diffusion mechanism and directly connects the structural and functional features of the material.

Electronic conduction is a key factor limiting the performance of lithium (Li) slurry batteries with suspensions as electrodes. In this study, new conductive agents, including carbon nanotubes (CNTs) and graphene (Gen), were mixed with conventional conductive agents, incorporating Ketjen black and Cabot carbon black (Cabot), in different proportions to form a novel binary conductive agent. The effects of the binary conductive agents on the performance of Li slurry batteries were investigated using methods, such as scanning electron microscopy, slurry sedimentation rate, electrical conductivity, electrochemical impedance spectroscopy, and electrochemical performance tests. The results show CNTs wrapped around the surface of active particles, forming numerous conductive sites, while carbon black surrounded the active particles and CNTs to create a conductive path with wider coverage. Furthermore, carbon black adhered to the surface of Gen, enhancing its vertical conductivity. Nonetheless, Gen exhibited agglomeration and uneven dispersion. The slurry containing Gen possesses a low initial sedimentation rate but displays good liquid absorption and retention capabilities, maintaining a stable sedimentation rate of 3.10% over a long term. The slurry with the conductive composite of 1%Cabot+0.3%CNTs presents a high electrical conductivity of 124.75 mS/cm, and the half-cell's electrochemical transfer impedance is calculated as 42 Ω. In addition, the composite presents a discharge specific capacity of 171.06 mAh/g, an initial charge-discharge efficiency as high as 86.01%, and a capacity retention of 92.78% after 230 cycles.

This study prepares the elastic solid polymer electrolyte with polyvinylidene fluoride (PVDF) nanofiber as the base membrane by thermal initiation in-situ polymerization, with Li ditrifluoromethyl sulfonate imide as the Li salt, and butyl acrylate and fluoro-ethylene carbonate (FEC) as the raw materials of the composite solid electrolyte. The introduction of PBA elastomer can enhance the toughness of the composite film and provide buffer space for Li dendrite puncture. The addition of the FEC flame retardant additive can effectively increase the thermal stability and interface stability of the composite film. Furthermore, according to its synergistic effect, problems, such as poor interface contact and low ion conductivity, can be effectively solved. Moreover, the utilization of the functional groups containing C—F in PVDF can facilitate the adsorption of Li ions and the dissociation of Li salts. In addition, the three-dimensional interconnected network structure of PVDF nanofiber membranes can provide a larger specific-surface area, thereby contributing more Li-ion adsorption sites and providing sufficient ordered interconnected Li-ion transfer channels. The introduction of PVDF nanofiber membranes can greatly improve the strength of composite solid-electrolyte membranes, and this is beneficial for inhibiting the growth of Li dendrites. The results show that the ionic conductivity of the elastic, composite solid-electrolyte membrane containing PVDF nanofibers is 3.9 × 10^-4 S/cm at 25 ℃, with tensile strength of 13.8 MPa, thermal decomposition temperature of 200 ℃, and Li⁺ transference number of 0.75. Furthermore, Lithium iron phosphate (LFP)|SPE|Li solid-state battery possesses an initial discharge capacity of 167 mAh/g at 0.1 C, and 122.3 mAh/g after 120 cycles at 0.5 with a capacity retention rate of 95.9%.

NiCo₂S₄ can catalyze the redox conversion of polysulfides in Li-S batteries owing to its abundant catalytic sites and metallic nature. However, its catalytic activity and chemisorption properties are not sufficient for suppressing the shuttle effect of polysulfides during long cycles. The oxygen (O)-doped NiCo₂S₄/CNT (O-NiCo₂S₄/CNT) composite is synthesized using the hydrothermal method combined with annealing under air atmosphere, and systematical experiments demonstrate the increasing crystallinity, amorphous surface, and augmenting conductivity for O-NiCo₂S₄/CNT. Moreover, the O doping can effectively modulate the electronic structure of NiCo₂S₄, thus improving the chemical absorption and catalytic conversion ability, promoting the conversion kinetics of polysulfides, and inhibiting its shuttle effect. The Li-S battery based on O-NiCo₂S₄/CNT functionalized separator has an initial high discharge capacity of 1362.7 mAh/g^{at 0.2 C and a capacity of 923 mAh/g after 150 cycles with a capacity decay rate of only 0.21% per cycle. The capacity can reach 701.8 mAh/g at 3 C. This work shows that oxygen atom doping of metal sulfides is an effective strategy for developing efficient catalysts.}

Zinc-nickel battery (ZNB) is regarded as a prospective green-energy storage technology. However, the large-scale application of ZNB is impeded by the deformation, dendrite growth, passivation, and serious side reactions of the zinc anode. In this study, reduced graphene oxide (rGO), Ketjenblack (KB), and carbon conductive film (CCF) are used to prepare the CCF@KB-rGO film with three-dimensional (3D) conductive network based on the spraying technology. The results show good electrochemical performance by AA ZNBs with CCF@KB-rGO. The hyperbranched KB and layered rGO form a 3D conductive network to enhance electron and ion conduction, thus improving the fast charge and low temperature-discharge performance of ZNB. Furthermore, an 80% capacity charge could be achieved within 43 min, and more than 60% rated capacity is output at 0.5 C at -20 ℃. The layered rGO enables zinc to be deposited uniformly and improves the cycle stability of the zinc anode; no obvious dendrites are found in the zinc anode after 300 cycles. The KB and rGO with high specific-surface area in CCF@KB-rGO can adsorb part of the free electrolytes, thus inhibiting the dissolution of the zinc anode and reducing the self-discharge of the battery. The outstanding electrochemical performance and safety of the AA ZNB with CCF@KB-rGO is far better than that of a disposable dry battery in the application of intelligent locks. Thus, the ever-growing requirements of smart lock are met for battery performance. The design based on the proposed 3D conductive film improves the performance of ZNB effectively, thus providing a new idea for the research of ZNBs.

Among the candidate batteries, sodium-ion batteries are in the spotlight because of their sufficient, low-cost, and widely distributed sodium resources. Layered transition metal oxides exhibit intrinsic structural instability owing to multiple phase changes. The low theoretical capacity of polyanionic compounds restricts their future application, and organic cathode materials are easily dissolved in the electrolyte and have poor conductivity. Fortunately, Prussian blue and its analogs (PB and PBAs) cathode materials show extraordinary potential because of their three-dimensional rigid open framework, high theoretical specific capacity, adjustable structure, and facile synthesis. However, the Fe(CN)₆ vacancies and coordination water inevitably generated in the crystal during synthesis limit its further application in energy storage. To solve the above problems, most researchers have optimized the intrinsic structure to improve the quality of the crystal or focused on modifying the surface to enhance the interface stability. Considering the structure-properties relationship, this paper first discusses the PB and PBAs' chemical composition and crystal structure. On this basis, strategies for optimizing the structure of PB and PBAs were analyzed from three aspects: material synthesis, ion doping, and unique structure design. In addition, the latest research progress on the modification of PB and PBA materials is elaborated. Moreover, the future development prospects are discussed, to provide a theoretical reference for developing higher-performance PB and PBAs materials.

The study presents the design of an integrated liquid-cooled plate based on the heat generation characteristics of cylindrical lithium-ion batteries. The effects of inlet flow rate, ambient temperature, and depth of cooling fixed hole on the cooling performance of the integrated liquid-cooled plate were investigated through numerical simulations, and the performance was compared with that of the honeycomb liquid-cooled plate. The results showed that the integrated liquid-cooled plate outperforms its honeycomb counterpart in reducing the maximum temperature of the lithium-ion battery pack. Although the minimum temperature of the battery pack remains higher in the integrated system than that in the honeycomb system under similar ambient conditions, the maximum temperature variation within the battery pack diminishes by an average of 13.3% in the integrated system. Additionally, the integrated plate weighs 9.7% less than the honeycomb plate. As the inlet flow rate of the integrated liquid-cooled plate increased from 30 ml/min to 60 ml/min, the maximum and minimum temperatures of the battery pack decreased considerably, accompanied by a reduction in the maximum temperature difference by 0.458 K and an increase in pressure loss from 352 Pa to 832 Pa. Increasing the depth of the cooling fixed hole led to a gradual decline in the maximum temperature and a corresponding increase in the minimum temperature; both lied within the optimal operating range of lithium-ion batteries. As the hole depth increased from 7 mm to 13 mm, the maximum temperature difference in the battery pack decreased by 20.8% and the weight of the cooling fixed structure increased by 46.2%. At the ambient temperatures of 296.15 K, 298.15 K, 300.15 K, and 302.15 K, the maximum and minimum temperatures of the battery pack were very close to each other after a charge-discharge cycle, with the maximum temperature difference of the battery pack exhibiting a peak at 302.15 K, which is higher than that at other temperatures.

The flow channel structure of a proton-exchange membrane fuel cell (PEMFC) has an important influence on the reaction gas flow, heat exchange, and electrochemical reactions. Common flow channels are concentrated in serpentine, interdigital, point-shaped, wavy, parallel, and related improved channels. However, their gas uniformity, water management, and output performance require improvement. Inspired by the Koch snowflake in the field of mathematical geometry, a new channel structure is proposed, i.e., taking the center of the circle as the center to radiate around. Based on six main channels, different levels of branch channels are added in turn to form 30 channel outlets. A three-dimensional steady-state unidirectional isothermal fuel-cell model was developed. Under an operating temperature of 60 ℃ and inlet relative humidity of 100%, a fuel cell testing platform was built for the experiments and simulated using ANSYS Fluent 2020. The model simulation results were consistent with the experimental results, thereby verifying the effectiveness of the model. The simulation results of the new channel and the traditional serpentine channel were compared, and the current density of the membrane electrode, oxygen mass distribution in the channel, water mass distribution at the interface between the channel and gas diffusion layer, membrane water content, and channel pressure were analyzed. The results showed that, compared with the serpentine channel, the new channel has a lower inlet and outlet pressure difference and slower flow rate but has advantages such as a more uniform distribution of reaction gas, better water management effect, and higher membrane current density and output power. The peak current density increased by 9.60%, and the peak power density increased by 12.70%, which is expected to provide new ideas for the innovation of fuel cell flow channel structure.

This study proposes a low-temperature rapid start-up scheme for mobile energy storage containers to address the problem of decreased emergency support capabilities caused by the long cold start time and severe performance degradation of mobile energy storage container batteries in high-altitude and cold environments. The capacity ratio and low-temperature start-up battery group were calculated based on the capacity requirements of the energy storage container battery system, temperature boundary conditions, and low-temperature start-up time requirements. A standard battery module model and a liquid preheating structure were established. An orthogonal experiment method was used to explore the influence of various factors in the preheating structure on the heating performance of the battery system, and the parameters were optimized. A preheating system with closed-loop liquid preheating coupled with heating-film preheating was designed, and the preheating effect of closed-loop preheating was investigated. The results show that in an environment with a temperature of -20 ℃, the energy storage container can preheat the energy storage battery to above 5 ℃ within 10 minutes.

This study investigates the thermal runaway fire characteristics of electric vehicles (EVs) in an underground garage scenario and the suppression effect of water spray. Different characteristics of the thermal runaway development process of EVs are derived from the temperature changes at different locations inside the vehicle and the roof temperature outside the vehicle before and after the water spray. We scrutinized the temperature evolution of the battery pack, body, and roof during the development of EV thermal runaway. Our results showed that the battery thermal runaway process in the battery pack was proximal and distal, and the primary mode of heat transfer was heat conduction, with the highest temperature exceeding 600 ℃. When water could not directly act on the battery, the cooling effect was limited and only played a mitigating role, failing to reduce the temperature to a safe level. Compared with the change in the battery pack, the temperature change in the cabin had a certain lag, leading to the illusion of safety for the people in the car. We also found that when the scene was filled with a lot of black smoke, the highest temperature on the roof was only 46.1 ℃. The smoke sensor was more suitable for an underground garage than temperature sensor. The results of this study contributed to an in-depth understanding of the thermal hazard of EVs in confined spaces and provided basic data support and reference for the fire safety design of underground garages.

With changes in energy structure, comprehensive energy stations that integrate oil, gas, hydrogen, and electricity have become the developmental trend of future energy refueling stations. As the electrical energy module of a comprehensive energy station, the prefabricated modular heat exchange station carries explosion accident risks caused by thermal runaway of the battery, leading to secondary accidents. This article establishes a 1∶1 three-dimensional simulation model based on an already operational parallel prefabricated module power station. The gas released by the thermal runaway battery in the power station was used as the explosion source. The shock wave propagation process in a combustion and explosion accident in a parallel prefabricated module power station was studied via CFD simulation, and the maximum impact range of the accident under the existing structure was determined. The influence of the setting method of explosion relief device on the explosion relief effect was explored. The research results are summarized as follows. ① The maximum explosion overpressure and impact range generated after a combustion and explosion accident in the exchange station cabin could be obtained through simulation, which had a relatively small impact on the adjacent exchange cabin of the parallel exchange station. ② By setting up an explosion relief device, the maximum explosion pressure and overpressure of the shock wave in the power exchange compartment could be significantly reduced, effectively reducing the consequences of combustion and explosion accidents. ③ The venting effect was approximately linearly related to the opening pressure of the venting device, and the smaller the opening pressure, the more obvious the venting effect. The relative distance between the venting device and ignition source influences the venting effect. The closer the venting device was to the ignition source, the better the venting effect. The venting effect was influenced by the position of the venting device, and the venting rate ranges from high to low at the rear, side, and top. This supports the safe operation of comprehensive energy stations and prevents domino accidents.

The regulation of flexible loads, such as electric vehicles, is an emerging means of enhancing the power grid operation flexibility; however, it is often overlooked in the energy storage planning stage. Therefore, this article proposes an optimized configuration method for energy storage in distribution networks that considers the aggregation regulation of electric vehicles. First, the operation domain of a single electric vehicle was characterized by taking into account the arrival time, departure time, expected electricity, and other characteristic parameters. Then, a second-order approximate practical model of the charging station was obtained based on the Hermann-Minkowski sum of electric vehicles, which was used to characterize the aggregation regulation characteristics of electric vehicles. An energy storage operation model that used branch virtual resistors to characterize charging and discharging losses was established. Furthermore, a two-stage stochastic optimization method for energy storage planning operation was proposed based on the K-Medoids scene generation and Bender's decomposition algorithms. The results based on the improved IEEE-33 node distribution system showed that as the controllable proportion of electric vehicles increased, the total capacity of energy storage configuration would increase, carbon emissions be significantly reduced, and photovoltaic utilization efficiency be significantly improved.

The large-scale integration of distributed energy aggravates the uncertainty, volatility, and randomness of the power system. This study proposes a heuristic algorithm based on simulated annealing and a collaborative simulation framework that combines building-energy simulation with Energy Plus/Matpower software simulating distribution network to realize the formulation of load balancing and loss minimization in the distribution network. The load-balancing and power-loss minimization strategies used for solving line faults (such as disconnection) are studied at the distribution network level. Based on the demand response signal, this study realizes reasonable scheduling of battery energy storage systems for distributed users. Finally, based on a nine-node network, the accuracy and scalability of the proposed algorithm for load balancing and line-loss minimization of the distributed energy-access distribution network are analyzed by simulating and verifying the model with different weight coefficients. The research results have important practical significance for distributed energy-access distribution network.

To reduce fluctuation of the tie-line power in the micro-grid and expand the capacity boundary of a hybrid energy storage system (HESS) in regulation, this study proposes an HESS structure with pumped storage and a capacity-optimization method based on CEEMDAN. First, considering the energy-type energy storage of pumped storage and battery and the power-type energy storage of supercapacitors, the HESS in micro-grid is constructed. Next, the power of the tie line and the total power of the HESS are determined according to the load, and CEEMDAN is used to allocate the power of the storage system. With the minimum annual cost of energy storage as the optimization goal, the capacity-based optimal configuration model of the system is established, and the corresponding solution method is given. The simulation results of the example show that the HESS with pumped storage can improve the system economy and prolong the life of the storage equipment according to the stabilization of the fluctuation of tie-line power. The rationality and effectiveness of the proposed optimal configuration method are verified.

The use of a liquid piston mechanism to strengthen the heat transfer between compressed air and the environment during energy storage can effectively reduce the heat dissipation during compression and enhance the conversion efficiency of electrical energy to air-pressure potential energy during energy storage. Considering the advantages of adiabatic compression and near-isothermal compressed-air energy storage, a reasonable integration of near-isothermal compression and adiabatic compression methods is presented and a composite compressed-air energy storage system is proposed. By establishing thermal calculation models of different compressed-air methods, the high-efficiency energy-storage characteristics under the coupling effect of adiabatic and near-isothermal compressions are analyzed in depth, and the driving mechanism of the near-isothermal compression on the high-efficiency operation of the energy-storage system is clarified. The research results show that the efficiency of the complex, compressed-air energy-storage process is higher than that of the conventional adiabatic, compressed-air energy storage. In addition, the effect of the near-isothermal compressed air on the performance of the complex compressed-air energy storage is more significant, i.e., the efficiency of the variable pressure-exhaust liquid piston near the isothermal compressed-air energy storage is 3% higher than that of the constant pressure exhaust. In addition, the variable exhaust-pressure condition can better adapt to the pressure change inside the storage chamber, weakening the filling process of the storage chamber. Near-isothermal compressed-air processes can increase the efficiency of the exergy by 3.3% with the addition of a shower, and spraying is observed to show variable effects on the near-isothermal, compressed-air efficiency of the liquid piston at different times.

Compressed-air energy storage is considered the most promising large-scale physical energy-storage technology; in addition, the compressor, as a key component, has an important impact on the overall performance of the system. In this field, centrifugal compressors have advantages over other types of compressors with characteristics of large flow, high pressure ratio, and wide operating conditions. Owing to the influence of the charging characteristics of the fixed-volume gas-storage device, the compressor often functions in the off-design condition. The accurate prediction of the compressor's performance can improve system efficiency and reduce R&D investment. Since the 1950s, considerable amount of research has been conducted on the performance prediction of centrifugal compressors and various performance prediction methods have been established. In this paper, performance prediction methods are categorized into mechanism modeling, similarity conversion, and data-driven models. According to the summarization of the basic principles and research progress of each method, the differences of each method in terms of the modeling cycle, prediction accuracy, portability, and application scenarios are qualitatively analyzed, and the future development trend of performance prediction is prospected. The findings of this study could guide in the research and application of the performance prediction method to centrifugal compressors.

The elimination of the "bucket effect" of battery systems in a fundamental manner is a challenging problem in the field of battery energy storage system (BESS). At present, this problem is being solved by pursuing the consistency of battery cells during their production and use; however, this method not only incurs a high cost but also is unable to fully eliminate the bucket effect. Therefore, we propose the dynamic reconfigurable-battery (DRB) energy storage technology based on energy digitalization. In comparison to the conventional norm of fixed series-parallel connections, the DRB networks use new program-controlled connections between battery cells/modules. By controlling the charging/discharging time of each battery unit connected to the circuitry, each battery cell/module could work in its "best effort" manner with no over-charge or over-discharge. Based on the DRB energy-storage technology, we propose the energy control and system-level intrinsically safe control methods. The energy control problem is formulated as an optimization issue, and the intrinsically safe control methods based on the controllable series and parallel technology are analyzed. The real-world operation data show that DRB networks can fundamentally improve safety, reliability, efficiency and cycle life of BESSs, paving a new path for building large-scale, long-life, and low-cost BESSs.

With the advantages of high energy and power densities, Li-ion batteries (LiBs) are widely used to power an increasingly diverse range of applications, including portable electrochemical energy-storage devices, electric vehicles, and large energy-storage power plants. In addition, they are considered the most competitive power sources for future green smart grids. With the increasing demand for energy sources and storage devices, LiBs with high energy density are continuously being pursued. However, high energy densities could result in high safety risks. The conventional organic liquid electrolyte components and olefin-based separators used in existing LiBs are flammable. In addition, nonuniform distribution of components, inhomogeneous interfacial contacts, and electrical, thermal, or mechanical abuses in the battery operating process can cause internal short circuit, thus releasing large amounts of Joules heat, resulting in a rapid temperature rise and thermal runaway propagation, thus triggering toxic gas release, smoke, fire, combustion or even explosion. To improve the safety and cycling lifetime of LiBs, the mechanism and process of thermal runaway must be understood. In addition, detection and warning technologies must be developed for the early-stages warning of the battery thermal runaway. Compared with technologies on monitoring the terminal voltage, current, and surface temperature, the gas-sensing approach can effectively detect the thermal runaway at a very early stage. During the thermal runaway process, LiBs produce characteristic gases, such as O₂, H₂, carbon oxides (CO, CO₂), hydrocarbons (C₂H₄, CH₄, etc.), and fluorine gases (HF, POF₃, etc.), through chemical or electrochemical reactions. As such, the thermal runaway behavior of LiBs could be monitored and early warnings can be issued by detecting the composition and concentration of the released characteristic gases. This review comprehensively presents the research progress and prospects of gas-sensing techniques for the thermal runaway of LiBs. First, the paper summarizes the main causes and processes of the thermal runaway of LiBs. Next, the characteristic gas generation and corresponding detecting techniques are described. Then, this paper elaborates on the research progress on the gas detecting and sensing technologies for the early warning of the thermal runaway. Furthermore, gas-sensing technologies for the early warning in the thermal runaway in LiBs are proposed. This review provides guidance for the gas sensing technologies to achieve an early warning system of the thermal runaway in LiBs. Moreover, the findings of this study show the development of LiBs with high safety and high energy density.

A high-temperature composite phase change heat storage electric heating device (CPCHSD) utilizes low-valley electricity, abandoned wind power, abandoned photovoltaics, and other electric energy to achieve energy storage applications through electric heat conversion. It is mainly used for clean heating in the northern region and flexibility transformation of thermal power units under deep peak shaving. Because this device uses low-valley electricity for heating, the heating cost is greatly reduced. This paper summarizes the high-temperature CPCHSD for a boiler-room "oil to electricity" heating project in the urban area of Beijing. The heat storage material and operating principle of the device are introduced. The high-temperature composite phase change heat storage system is designed according to the heat load of the target user of the heating project. To determine the feasibility of the transformation, this study focused on the energy consumption analysis and operation cost of the entire heating season based on load calculations. This provides a detailed comparison with those before the transformation. The results show that the heating system based on a high-temperature CPCHSD can not only meet the heat demand of users, but also reduce carbon emissions. After calculation and transformation, 591.8 tons of carbon emissions could be reduced in one heating season. Compared to the direct heating system and oil-fired boiler, the operating costs were reduced by 57.76% and 84.23%. The efficient use of clean energy improves the system's economy and proves the feasibility of the transformation.

Lithium-ion battery is an important part of the fixed electrochemical energy storage, and the estimation of its state of health (SOH) is of great significance for its safe and stable operation. At present, health-feature extraction is focused on the charging stage of a battery, and few methods have been developed to extract health features at the relaxation stage. This study proposes a method to extract health features from the relaxation stage and use Gaussian process regression (GPR) to estimate the SOH. First, based on the accelerated cycle aging test data of Li-NMC batteries, the variation of the time constant at the relaxation stage is analyzed, and a power function, which reflects the terminal voltage in the relaxation stage well, is used for modeling. Second, the key features that can characterize the relaxation stage are extracted, and the SOH estimation model is established based on GPR. Finally, the accuracy was verified using batteries with different aging current multiplicities. The results are compared when the model is generated using 15- and 60-minute relaxation-stage curves. The accuracy of the Gaussian-process regression method is also compared with support vector machine and tree regression method, and the accuracy of SOH estimation was verified under multiple states of charge. The proposed SOH estimation model was validated to achieve an optimal root mean square error (RMSE) of 0.6%; the RMSE is still less than 1% when using 15-minute data for SOH estimation.

Accurate estimation of the state of health (SOH) of electrochemical energy storage batteries is crucial for ensuring their safe and reliable operation. Data-driven methods have been widely used for SOH estimations. However, existing methods overlook the temporal health information and feature extraction between multiple consecutive cycles of battery operation and the relationship between these features and the SOH value. This study proposes a novel SOH estimation model called multi-cycle net (MCNet) to address these issues. This model does not require manual extraction of health features; it only takes current and voltage measurements during the charging phase of the battery as input. It automatically extracts features relevant to the SOH estimation within each cycle, extracts relevant features between multiple consecutive cycles, and then combines them for SOH estimation. First, to construct the multicycle tensor input data and improve the convergence speed of the model, the sampled data from the charging phase of each cycle with varying lengths were preprocessed through length alignment, maximum-minimum normalization, and concatenation of multiple consecutive historical cycle data. Second, the preprocessed tensor data were used as input to build the MCNet model for predicting the SOH using a publicly available battery dataset. The average-absolute- and root-mean-square errors were within 1% and 1.4%, respectively. Finally, the proposed model was compared with other commonly used sequence prediction models, and a comparative experiment was conducted using only single-cycle current and voltage data as inputs. The results demonstrate that the proposed model achieves higher accuracy in SOH estimation, and using multiple cycles' current and voltage data as input improves the estimation accuracy.

The valve-regulated lead-ACID Batteries (VRLA), as a reserve DC power supply, is widely used in substations, and its performance directly affects the safe operation of the core equipment of the substation. Therefore, the state-of-health (SOH) is an important indicator for evaluating the deterioration of battery performance. However, the evaluation of the SOH of VRLA by using the verification capacity method results in a long cycle and high cost. To quickly and conveniently obtain the SOH of the lead-acid battery, this study proposes a fast detection device based on the electrochemical impedance spectroscopy (EIS) substation for determining the SOH of lead-acid batteries. This device injects 0.1-200 Hz of sinusoidal incentive current through the battery, and the response voltage at different frequencies is obtained using the adjustable secondary amplifier circuit. The battery EIS is obtained by calculating the fast Fourier transform (FFT). The impedance measurement range is wider and the measurement time is faster. By considering the battery AC impedance spectrum of different health states as the benchmark, the analysis of the EIS signal is compared with the standard battery AC impedance spectrum, and segmented impedance-related evaluation methods are used to determine the health status of the target battery. The propose method integrates the EIS measurement with SOH. The experiments show that the average error of the SOH-detection results of different batteries is 0.69%, the maximum error is 2.041%, and the test time is less than 2 min.

Accurate estimation of the battery state of health (SOH) is a critical technology in battery management systems, which is crucial for ensuring the safe and reliable operation of electric vehicles. To solve the problem of low SOH estimation accuracy due to insufficient generalization performance of a single kernel function in Gaussian process regression (GPR) and the tendency of hyperparameter selection to fall into local optimality, an SOH estimation method based on the grey wolf optimization algorithm (GWO) and a combined kernel function was proposed. First, the characteristics of battery aging were extracted using incremental capacity analysis (ICA) method. The capacity-voltage curve of constant-current charging of the battery was interpolated and the increment capacity (IC) curve was calculated using the difference method. The IC curve was smoothed using Savitzky-Golay filtering, and the peak height, voltage, and area were extracted as health features. Second, multidimensional scaling (MDS) was presented to eliminate feature redundancy and reduce the computational complexity of the model. The Pearson coefficient was used to verify the correlation between the proposed health features and SOH. Then, considering the nonlinearity of the SOH degradation trajectory and the quasi-periodicity of battery capacity regeneration, the combination of the neural network kernel function and periodic kernel function was used as the covariance kernel function of GPR, and the initial hyperparameters of the combined kernel function were optimized by the GWO method. Finally, the proposed method was compared with SVR, ELM, and GPR models based on the NASA battery data set to verify the accuracy of the GWO-GPR model. The 60^th, 80^th, and 100^th cycles were used as estimation starting points to verify the robustness of the model.

The accurate estimation of the state-of-health (SOH) of lithium-ion batteries (LiBs) plays a critical role in ensuring the stable and efficient operation of energy storage systems. This study proposes a fusion model based on cross-validation-trained linear regression weighting to enhance the precision of data-driven methods for SOH estimation. First, health features are extracted from the discharge voltage curve as well the as charging and discharging temperature curves. Second, Pearson correlation coefficients are used to analyze the selected features, determining the health-indicator parameters for the network model inputs. Finally, attention mechanisms were incorporated into the long short-term memory (LSTM) and gated recurrent unit (GRU) to establish the LSTM-Attention and GRU-Attention models, respectively. These models are trained using the first 50% of data from NASA's battery aging datasets, B0005, B0006, B0007, and B0018, with the remaining 50% used for validation. The LSTM- and GRU-Attention models produce SOH estimates of {\widehat{y}}_{\mathrm{L}-\mathrm{A}} and {\widehat{y}}_{\mathrm{G}-\mathrm{A}}, respectively. Then, the fusion model proposed in this study performs linear regression weighting on these two estimates, yielding a maximum root mean square error (RMSE) and mean absolute error (MAE) of 0.00291 and 0.00200, respectively. Furthermore, the robustness of the proposed model is demonstrated by subjecting the health factors input to various proportions of Gaussian white noise. The results indicate that the fusion model exhibits strong resistance to interference, with a maximum RMSE and MAE of only 0.03562 and 0.02889, respectively.

The equivalent circuit model (ECM) is one of the primary types of battery models that play a crucial role in battery characteristic analysis and state estimation. However, the widely used resistor-capacitor (RC) structured ECM currently in use fails to adapt to complex and dynamic scenarios. For instance, traditional ECMs fail to accurately reflect the special phenomenon of battery polarization voltage under high current rates, unable to accurately characterize the impedance characteristics of batteries at high current rate conditions. To address this, the present study conducts battery peak current experiments at different states of charge and analyzes the polarization voltage and impedance characteristics of the battery under peak current conditions using experimental data. Then, a negative resistance-capacitance segment is introduced to fit the experimental results, and the conventional ECM is improved to better represent the polarization phenomena under high current rate conditions. Additionally, a parameter separation method based on the inflection points of impedance curves is proposed by comparing the characteristics of the conventional RC segment with the negative RC segment. The proposed method has low computational complexity and a convenient model solution. Finally, the model with separated parameters is validated. The results show that the proposed ECM and parameter identification method can effectively realize the polarization voltage variation of the battery under high current rate conditions and can accurately represent the battery voltage characteristics. The experimental results demonstrate that the proposed model maintains an error of <0.05 V. Moreover, the proposed ECM greatly enhances the accuracy compared with the conventional RC model, and it does not rely on complex electrochemical models, maintaining a simple model structure.

Lithium-ion batteries are widely used in military and civilian fields because of their advantages, such as high power density, high energy density, and long cycle life. However, the performance of lithium-ion batteries is significantly degraded at low temperatures, which hinders their application in extreme environments, such as polar regions, plateaus, and space. In this study, the discharge performance of the battery at different temperatures (-20～25 ℃) was studied using a soft-packed three-electrode device. Along with being combined with electrochemical impedance spectroscopy, the discharge behavior and impedance characteristics of the positive and negative electrodes of the battery were independently studied under low-temperature conditions. The main limiting factors restricting the low-temperature performance of the battery were analyzed, and strategies for further improving the low-temperature performance of the battery were provided. Studies have shown that the charge-transfer impedance of the negative electrode is the primary source of the impedance of the entire battery, and its electrode polarization is the leading cause of battery polarization. However, with a decrease in temperature, the contribution of the positive electrode polarization to the battery polarization increases. When the temperature drops below -10 ℃, the positive electrode becomes the main limiting factor for the low-temperature performance of the battery.

The assessment of the state of safety (SOS) of Li-ion batteries (LiB) is required to determine the sustained impact of the internal and external conditions on battery safety, as well as the monitoring of the safety status of batteries throughout their lifecycle. SOS assessment can provide a judgment basis for advance fault warning and intelligent operation and maintenance; this is crucial for improving the security and reliability of the energy storage system. However, several questions still remain to be answered about the usability and accuracy of SOS assessment results in battery management systems or big data platforms. A LiB has many failure modes, a complex influence mechanism, and fuzzy definition of SOS. This paper summarizes the definition and classification, evaluation method, influencing factors, and safety boundary of battery SOS. In addition, the paper summarizes the influence mechanism of nine factors, namely voltage, ambient temperature, current, mechanical deformation, limiting external conditions, state of charge, state of health, internal resistance, and state of Li plating on the safety of LiBs. The shortcomings of the current SOS evaluation of LIBs are discussed based on three aspects, namely the coupling mechanism of multiple factors, security threshold migration model, and quantitative evaluation method. Finally, the paper points out future research direction.

This bimonthly review paper highlights 100 recent published papers on lithium batteries. We searched the Web of Science and found 4706 papers online from Aug. 1, 2023 to Sep. 30, 2023. 100 of them were selected to be highlighted. Spinel oxides and High-nickel ternary layered oxides as cathode materials are still under extensive investigations of the effects of doping and interface modifications on their electrochemical performances and surface and bulk evolution of structures under prolong cycling. For alloying mechanism anode materials, such as silicon-based composite materials, many researchers pay attention to material preparations and the optimization of electrode structure to buffer volume changes, and emphasize on the application of functional binders and modification of the interface. Large efforts were devoted to design the three-dimensional structure electrode, interface modification and inhomogeneity plating of lithium metal anode. The researches of solid-state electrolytes are mainly focused on their structure design and performances in sulfide based-, chloride based-, oxide based-solid-state electrolytes and their composites, whereas liquid electrolytes are improved by the optimal design of solvents and lithium salts for different battery applications and novel functional additives. For solid-state batteries, the studies are mainly focused on the suitability of layered oxide cathode materials with sulfide based- and oxide based-solid-state electrolytes. To suppress the "shuttle effect" and activate sulfur of Li-S battery, composite sulfur cathode with high ion/electron conductive matrix and functional binders are studied. Other relevant works are also presented to the dry electrode coating technology. There are a few papers for the characterization techniques of lithium-ion transport in the cathode and lithium deposition. Furthermore, theoretical calculations are done to understand the viscosity of electrolyte. The interface solid state electrolyte/cathode are also widely studied.

In alignment with the "carbon peak, carbon neutral" initiative, the relevance of hydrogen energy is increasing. Although the integration of alkaline water electrolysis (AWE) and proton exchange membrane electrolysis (PEM) shows promise in the field of green hydrogen production, its economic viability has been less explored. To address this gap, we introduced a levelized cost of hydrogen (LCOH) model, offering a life-cycle economic assessment for hydrogen production. Quantitative analysis was conducted on a 10000 m³/h hydrogen production system configured with a 4∶1 ratio of AWEto PEM. Subsequently, sensitivity analysis was performed. The results showed that the LCOH for the combined hydrogen production system was 33.22 CNY/kg, which was 3.31 CNY/kg higher than that of the AWE hydrogen production system. Notably, the electricity costs constitute 53.32% of the entire life-cycle cost. Increasing the PEM allocation ratio improved the adaptibility of hydrogen production system to renewable energy fluctuations as well as increased the production cost. In actual projects, focus should be on the variations in the cost of the electrolyzer system, and the PEM allocation ratio could be optimized using the LCOH model. Considering 33.22 CNY/kg as the benchmark, a 20% and 40% reduction in the price of the PEM electrolyzer system increased the PEM allocation ratio to 27.92% and 46.25%, respectively. Similar to the conventional water-electrolysis hydrogen production systems, controlling the electricity price is crucial for enhancing the economic feasibility of combined hydrogen production; a reduction of 0.01 CNY/kWh in electricity price could lead to a decrease of 0.45 CNY/kg. Given the current allocation ratios, the improvement of AWE electrolyzer efficiency should be prioritized, especially when operating under budget constraints.