Energy Storage System Research Articles

LiF-induced in-situ engineering of a dense inorganic SEI for superior lithium storage in black phosphorus anode

Black phosphorus (BP) has been highly regarded as a favourable candidate for fast-charging anode applications owing to its high theoretical capacity and advantageous charge–discharge platform. However, BP faces challenges related to compromised electrochemical performance resulting from an unstable solid-electrolyte interface (SEI) and substantial volumetric expansion. This study proposes the engineering of an inorganic-dense SEI on the surface of BP-C through the strategic incorporation of lithium fluoride (LiF). The presence of LiF in the system preferentially promotes LiPF6 adsorption from the electrolyte, facilitating the in-situ formation of a lithium-enriched inorganic SEI film on the BP-C particulate surfaces. This strategic formation effectively mitigates subsequent electrolytic decomposition, accommodates volumetric expansion, and substantially improves the rate capability and cycling stability of the system. Consequently, the BP-LiF-C electrode demonstrates high initial coulombic efficiency of 86.8 % and maintains a steady capacity of 926.1 mAh g−1 over 700 cycles at 2000 mA g−1. Moreover, when paired with a LiFePO4 cathode, the full cell exhibits long cycling stability, retaining 98.6 % of its capacity after 500 cycles at 2000 mA g−1, and performs at high rate. Therefore, utilising LiF to modulate the interfacial architecture of BP-based electrode composites provide notable guidance to enhance energy storage systems.

Optimal power dispatch in microgrids using mixed-integer linear programming

Abstract As greenhouse gases emissions continue to rise, society is actively seeking methods to reduce them. Microgrids (MGs), which predominantly consist of renewable energy sources, play a significant role in achieving this objective. This paper proposes an optimized methodology for power dispatch in MGs using mixed-integer linear programming (MILP). The MGs include photovoltaic systems, wind turbines, biogas (BG) generators, battery energy storage systems (BESS), electric vehicles (EV), and loads. The model features an objective function focused on cost minimization, power balance, and the necessary limits and constraints for the system’s safe operation. Real-time pricing is employed for energy transactions between the MGs and the main grid. The results demonstrate a cost-efficient operation for the proposed system comprising two MGs and the main grid. During periods of negative power balance, the demand was met by discharging the BESS, EV’s battery, or purchasing energy from the grid. The BESS was charged when energy prices were low and discharged during peak demand periods and high energy prices. The intermittent nature of renewable sources necessitates an efficient management system to ensure reliable operation. Additionally, storage systems help mitigate the variability in generation. The BG generator was another crucial component for power supply due to its flexibility. Integrating these components into the system improved reliability and ensured a secure and balanced operation.

Developing an energy storage systems selection methodology for peak shaving purposes in photovoltaic micro-installations

This study investigates the selection methodology for peak shaving purposes in photovoltaic micro-installations. Due to the increasing share of photovoltaic micro-installations in the national electricity production and the consequent changes in the country's energy policy, the need for energy storage is becoming increasingly apparent. Therefore, based on data collected throughout the study of a photovoltaic installation conducted in years 2020-2024 and data on market electricity prices, the correlation between production, market electricity prices, and energy value was analyzed. The analysis established interconnections between these values and identified factors influencing their changes. It was observed that the market price of electricity is relatively low during hours of increased photovoltaic energy production. By comparing the correlations between energy production and electricity prices over different years, an increasing impact of the increasing share of photovoltaic energy production in national electricity production on the market price of electricity was noted, highlighting the importance of energy storage. For the described household, an energy storage system was selected with a view to meeting the energy demand during hours of higher market electricity prices. The selected model was Lithium-Iron-Phosphate battery with a capacity of 5 kWh. Furthermore, the cost effectiveness of household-scale energy storage systems was examined. Implementing the proposed solutions could result in increased auto-consumption and presumably improve cost-effectiveness of photovoltaic installations in the future.

Inorganic‐Organic Composite Cathode Materials for Aqueous Zinc Ion Batteries

AbstractAs potential candidates for large‐scale energy storage systems, aqueous zinc‐ion batteries (AZIBs) have attracted more and more attention from researchers and investors. Tremendous efforts have been devoted to develop high‐efficient cathode materials for improving comprehensive performances of AZIBs. Recently reported inorganic‐organic composite (IOC) cathode materials exhibited excellent electrochemical performance and were generally superior to corresponding inorganic or organic cathode materials. This paper presents a timely review on recent progresses and challenges in IOCs for AZIBs. Preparation strategies of IOCs have been elaborated and categorized, recent advances and main working mechanisms of IOCs are exhibited with a focus on the analysis methods for mechanism studies, outlooks and challenges of IOC cathodes for AZIBs are outlined to guide their future development.

An Energy Management System for Distributed Energy Storage System Considering Time-Varying Linear Resistance

As the proportion of renewable energy in energy use continues to increase, to solve the problem of line impedance mismatch leading to the difference in the state of charge (SOC) of each distributed energy storage unit (DESU) and the DC bus voltage drop, a distributed energy storage system control strategy considering the time-varying line impedance is proposed in this paper. By analyzing the fundamental frequency harmonic components of the pulse width modulation (PWM) signal carrier of the converter output voltage and output current, we can obtain the impedance information and, thus, compensate for the bus voltage drop. Then, a novel, droop-free cooperative controller is constructed to achieve SOC equalization, current sharing, and voltage regulation. Finally, the validity of the system is verified by a hardware-in-the-loop experimental platform.

A Design and Safety Analysis of the “Electricity-Hydrogen-Ammonia” Energy Storage System: A Case Study of Haiyang Nuclear Power Plant

With the development of modernization, traditional fossil energy reserves are decreasing, and the power industry, as one of the main energy consumption forces, has begun to pay attention to increasing the proportion of clean energy generation. With the deepening of electrification, the peak-valley difference of residential electricity consumption increases, but photovoltaic and wind power generation have fluctuations and are manifested as reverse peak regulation. Thermal power plants as the main force of peak regulation gradually reduce the market share, making nuclear power plants bear the heavy responsibility of participating in peak regulation. The traditional method of adjusting operating power by inserting and removing control rods has great safety risks and wastes resources. Therefore, this paper proposes a new energy storage system that can keep the nuclear power plant running at full power and produce hydrogen to synthesize ammonia from excess power. A comprehensive evaluation model of energy storage based on z-score data standardization and objective parameter assignment AHP (analytic hierarchy process) analysis method was established to evaluate energy storage systems according to a multi-index system. With an AP1000 daily load tracking curve as the input model, the simulation model built by Aspen Plus V14 was used to calculate the operating conditions of the system. In order to provide a construction basis for practical engineering use, Haiyang Nuclear Power Plant in Shandong Province is taken as an example. The system layout scheme is proposed according to the local environmental conditions. The accident tree analysis method is combined with ALOHA 5.4.1.2 (Areal Locations of Hazardous Atmospheres) hazardous chemical analysis software and MARPLOT 5.1.1 geographic information technology. A qualitative and quantitative assessment of risk factors and the consequences of leakage, fire, and explosion accidents caused by hydrogen and ammonia storage processes is carried out to provide guidance for accident prevention and emergency rescue. The design of an “Electric-Hydrogen-Ammonia” energy storage system proposed in this paper provides a new idea for zero-carbon energy storage for the peak shaving of nuclear power plants and has a certain role in promoting the development of clean energy.

Enhancing Flexibility and Reliability in Smart Distribution Networks: A Self-Healing Approach

This paper presents a pioneering approach that integrates a Self-Healing System through a Smart Distribution Network, which employs a two-step implementation of a comprehensive Energy Management algorithm and a multi-agent system with an autonomous distributed regeneration method. The novel method improves network operation through optimized management of local microgrids, which include diverse components comprising renewable energy sources, electric vehicle charging infrastructure, and energy storage systems. The first phase concentrates on minimizing network operation costs by adhering to system utilization restrictions and optimal load distribution, while the second phase addresses the optimization of regeneration processes, which decreases discrepancies between recoverable priority loads and switching operations. The evaluation introduces a stochastic programming approach to model and manage uncertainties, and utilizes the Kantorovich method and the roulette wheel mechanism to improve reliability. ...

Isobaric compressed air energy storage system: Water compensating cycle or CO2 compensating cycle?

Isobaric operation of air storage can remove the throttling losses existing in isochoric reservoir, making full use of the storage volume and lowering system construction cost. The water cycle and CO2 cycle are two of the most commonly configurations to stabilize the pressure in the air storage unit. The choice between the two cycles depends on whether the technical complexity and costs are justified by performance gains. This paper mainly focuses on developing the MAP design, a kindly selection diagram plotting the better value of performance indicators between the two systems, to determine the more favorable compensating cycle in the constant storage operation of the compressed air. The analysis results indicate that higher air storage pressure increases the system efficiency. The levelized cost of storage is provided with a valley value when the air storage pressure is at 6.6 MPa for the CO2 cycle and 14 MPa for the water cycle. The optimized systems can share a comparative efficiency of 68.04 % and 68.07 %, and the levelized cost of storage is 0.8237 ¥/kWh for the CO2 cycle and 0.7869 ¥/kWh for the water cycle. The water is suggested to be the compensating fluid for the isobaric system when the water machine efficiency is higher than 0.85 or else opting for CO2 proves to be a more economically viable choice.

Cooperative game robust optimization control for wind-solar-shared energy storage integrated system based on dual-settlement mode and multiple uncertainties

Aiming at the problems of renewable energy output uncertainties and single scenario operation mode of energy storage systems, a cooperative game robust optimization control method for wind-solar-shared energy storage system based on dual-settlement mode of power market is proposed in this paper. A cooperative game-based energy management framework under dual settlement mode of electricity market is constructed, the profit relationship between shared energy storage under multiple application scenarios and renewable energy are extracted and the corresponding profit models are established. Considering the multiple uncertainties of renewable energy and electricity prices, combined with robust optimization theory, a multi-level two-stage robust optimization model is established to make optimal electricity trading decisions for renewable energy and shared energy storage. Additionally, the cooperative game robust optimization model is solved by i-C&CG algorithm. The effectiveness of proposed control method is verified through actual operating data of a certain power grid in China. The simulation results show that the cooperative game robust optimization model achieves the optimal operation of the wind-solar-shared energy storage system considering multiple uncertainties, which can improve the ability of the system to cope with the uncertainty risk and the reliability of the system.

Optimization operation strategy of wind–pumped storage integrated system participation in spot market considering dual uncertainties

Abstract With the gradual increase in the penetration rate of renewable energy, the multifunctional role of pumped storage is becoming increasingly prominent, and the joint operation of “renewable energy + pumped storage” is a current research hotspot. However, during the joint system operation, there are dual risks from internal (renewable energy output) and external (market prices) factors, which significantly impact the system’s overall revenue. Therefore, an analysis is conducted around the operational mechanism of the “wind power–pumped storage” joint operation, and the uncertain factors faced during the system’s operation are identified. Second, an optimization model for wind power–pumped storage under deterministic scenarios is constructed, employing robust optimization theory and information gap decision theory to describe the uncertainty of electricity prices and wind power, thus forming a hybrid of the information gap decision theory and the robust optimization model for wind power–pumped storage. Finally, the results show that: (1) The total revenue of the model proposed in the paper has increased by 2.36% compared to the robust optimization model and by 9.04% compared to the deterministic model, significantly enhancing the model’s robustness and risk resistance capabilities. (2) From the perspective of the economic feasibility of different energy storage system configurations, the wind plant equipped with pumped storage has the highest economic feasibility, with an internal rate of return of 9.8% and net present value of 872 million Chinese Yuan, which is higher than that of compressed air energy storage and electrochemical energy storage systems. (3) Decision makers can set the risk deviation coefficient and the uncertainty budget according to their risk preferences, thereby changing the robustness of the model for differentiated decision making. However, an increase in the uncertainty budget coefficient will cause the total revenue of the joint operation system first to increase and then decrease, with the maximum revenue achievable within the range of 500–625; the total revenue reaches its maximum when the risk deviation coefficient is between 0.1 and 0.125.

Techno-economic analysis of hybrid renewable energy systems for cost reduction and reliability improvement using dwarf mongoose optimization algorithm

The global energy crisis, particularly in isolated and remote regions, has increased interest in renewable energy sources (RESs) to meet growing energy demands. Integrating RESs with energy storage systems offers a promising solution to mitigate fluctuations and intermittency, but concerns about cost and reliability remain. This study explores the optimal design of various microgrid configurations, combining photovoltaic (PV), wind turbine (WT), battery energy storage system (BESS), and diesel generator (DG) systems for Najran city, Saudi Arabia, via real-world meteorological and load demand data. The Dwarf Mongoose Optimization Algorithm (DMOA), alongside the salp swarm algorithm (SSA) and whale optimization algorithm (WOA), was applied to minimize the levelized cost of energy (LCOE) while improving system reliability. The results demonstrate that the PV/BESS configuration, although cost-effective with an LCOE of 0.038 USD/kWh, fail to meet reliability constraints with a loss of power supply probability (LPSP) of 0.679. In contrast, the PV, WT, BESS, and DG configurations achieved an LPSP of 1.9 × 10^--8% with an LCOE of 0.199 USD/kWh, offering a robust and reliable solution for the region's energy needs. This paper presents a novel application of the DMOA for optimizing hybrid renewable energy systems, demonstrating its effectiveness in achieving a balance between cost and reliability. This strategy provides a viable approach for sustainable energy planning in similar regions facing energy challenges.

Capacity Optimization of Wind–Solar–Storage Multi-Power Microgrid Based on Two-Layer Model and an Improved Snake Optimization Algorithm

A two-layer optimization model and an improved snake optimization algorithm (ISOA) are proposed to solve the capacity optimization problem of wind–solar–storage multi-power microgrids in the whole life cycle. In the upper optimization model, the wind–solar–storage capacity optimization model is established. It takes wind–solar power supply and storage capacity as decision variables and the construction cost of the whole life cycle as the objective function. At the lower level, the optimal scheduling model is established, considering the output characteristics of various types of power supplies and energy storage, microgrid sales, and purchases of power as constraints. At the same time, the model considers constraints, such as the power balance, the operating state of the energy storage system, the power sales and purchases, and the network fluctuations, to ensure the system operates efficiently. Taking a microgrid in South China as an application scenario, the model is solved and the optimal capacity allocation scheme of the microgrid is obtained. Meanwhile, the demand response mechanism and the influence of planning years are introduced to further optimize the configuration scheme, and the impact of different rigid–flexible load ratios and various planning horizons on microgrid capacity optimization is analyzed, respectively, by a numerical example. The comparison shows that the ISOA has better optimization performance in solving the proposed two-layer model.

Functional Separator with Poly(Acrylic Acid)-Enabled Li2CO3-Free Garnet Coating for Long-Cycling Lithium Metal Batteries.

Lithium metal batteries (LMBs) possess a theoretical energy density far surpassing that of commercial lithium-ion batteries (LIBs), positioning them as one of the most promising next-generation energy storage systems. Modifying separators with composite coatings comprising oxide solid-state electrolyte (SSE) particles and polymers can improve the cycling stability and safety of LMBs. However, exposure to air forms Li2CO3 on oxide SSE particles, diminishing their ion flux regulation at the electrode/electrolyte interface. Utilizing the reaction of Li2CO3 with polyacrylic acid (PAA) to form lithium polyacrylate (LiPAA), an ultra-thin composite coating on polyethylene (PE) separator with Li2CO3-free Li6.4La3Zr1.4Ta0.6O12 (LLZTO) particles and LiPAA binder is fabricated in one step. The exposed Li2CO3-free LLZTO surface increases the ionic conductivity and lithium ion (Li+) transference number of the functional separator, resulting in small resistance and uniform Li deposition of the Li metal anode. Consequently, the Li//LiCoO2 cell with the functional separator exhibits a significantly improved life of 980 cycles with 80.9% capacity retention under lean-electrolyte conditions. Both the Li//LiCoO2 coin cell and pouch cell using thin Li foil anode demonstrate good cycling stability and high mechanical robustness. This study provides a green and scalable approach for fabricating advanced separators for LMBs.

Optimized multi-head self-attention mechanism for SOH prediction in lithium-ion battery energy storage systems within microgrids

Estimating the State of Health (SOH) of lithium batteries is crucial for the safety and stability of the microgrids. Currently, existing data-driven methods struggle to predict across multiple groups of lithium batteries varying in type, capacity, and degree of degradation, as the prediction process requires the collection of extensive operational data for processing and computation. This paper analyzes the characteristics of lithium battery storage units within the microgrids and proposes a novel prediction method based on an improved attention mechanism. In the operational process, we collected the data under constant current (CC) and constant voltage (CV) charging modes to obtain health indicators with high correlation. We proposed a novel optimized multi-head attention method, designated OM-Attention, which employs a multi-head self-attention-based model to compute multiple sets of lithium batteries within a network node in a simultaneous manner. To validate the effectiveness of the OM-Attention method, we randomly selected four lithium batteries from the NASA public dataset for multiple experiments. Compared to SOH prediction methods, our approach exhibits superior performance with reduced Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Mean Square Error (MSE) to 4.16%, 5.02%, and 0.302×10−4, respectively. Additionally, the R2-score achieved by OM-Attention is 97.7%.

Proposal design and thermodynamic optimization of an afterburning-type isothermal compressed air energy storage system integrated with molten salt thermal storage

The isothermal compressed air energy storage is a potential technique for large-scale energy storage. In this study, the molten salt thermal storage is integrated with the afterburning-type isothermal compressed air energy storage system, which uses liquid piston compression technique, to enhance the thermal performances. Thermodynamic models of the hybrid energy storage system were developed, and the genetic algorithm was used to optimize multiple parameters to enhance the energy efficiency. Four operation modes, i.e., the high, medium-high, medium-low, and the low output power modes were proposed, to enhance the operational flexibility of the hybrid energy storage system. When the system operates in the operation mode of medium-low output power, a portion of afterburning heat is stored in the molten salt and used in the operation mode of low output power. Results show that the roundtrip efficiency of high, medium-high, medium-low, and the low output power modes are 63.57 %, 59.33 %, 57.13 %, and 83.28 %, respectively. Exergy analysis was carried out to quantify the irreversibilities of key components. In the three modes of operation with afterburning, the combustion chamber has the highest portion of exergy loss, and in the operation mode without afterburning, the exergy loss of heat exchangers is higher than that of expanders.

Machine learning analysis for heat transfer enhancement in nano-encapsulated phase change materials within L-shaped enclosure with heated blocks

Phase change materials(PCMs) are crucial to energy storage systems due to their enhanced thermal properties. They significantly boost energy efficiency and promote sustainability. Nevertheless, the low thermal conductivity of PCMs presents a significant challenge, which is addressed by utilizing nano-encapsulation to enhance energy efficiency in energy storage systems. Motivated by this, the current study conducts a theoretical investigation to explore the heat transfer characteristics in a buoyancy-driven Nano-Encapsulated Phase Change Materials(NEPCM) nanofluid within an L-shaped porous enclosure with the impacts of a heated block and magnetic field. Furthermore, the fusion temperature plays a crucial role in initiating phase change in NEPCM, thereby impacting the heat transfer process. Hence, identifying the optimal fusion temperature is essential. To accomplish this, a machine learning approach was employed to identify the ideal fusion temperature. A dataset of 160 data points across four different fusion temperature values was used in this analysis. Additionally, the machine learning model analyzed how variations in fusion temperatures impact physical parameters. An in-house Matlab code is utilized to solve the dimensionless fluid transport equations employing the finite difference method. The results indicate that increasing the nanoparticle volume fraction significantly enhances the heat transfer rate across all physical parameters. Specifically, under higher thermal buoyancy force, increasing the volume fraction from 1% to 5% results in a 90.04% increase in the heat transfer rate. The numerical analysis demonstrates that heat transfer rates improve significantly when the fusion temperature is adjusted to 0.5, a result further validated by machine learning techniques. At this temperature, thermal buoyancy force increases by 0.98% and 2.68% compared to values of 0.1 and 0.9, respectively, while the Stefan number shows increases of 159.42% and 87.48% under these conditions; thereby, the heat transfer rate increases at this value. This computational study provides important insights into the significance of fusion temperature, emphasizing the need to determine its optimal value for improving heat transfer. Identifying this optimal value can enhance the efficiency of thermal energy storage systems and improve cooling performance in electronic devices.

Study on start-up characteristics of single piston free piston linear generator for small-scale compressed air energy storage system

This study presents a new idea of applying single piston free piston linear generator (FPLG) to small-scale compressed air energy storage (CAES) system. Firstly, a single piston FPLG prototype for CAES is built. Then, the thermodynamic, motion and output characteristics of FPLG during start-up process are studied based on the experimental data. The results show that the start-up process of single piston FPLG can be divided into three stages, and the order of achieving a steady state is movement and output > in-cylinder pressure > the in-cylinder temperature. The in-cylinder temperature increases in a spiral pattern, and the temperature difference between adjacent cycles at the same state point decreases. In the 0th cycle, when the free piston assembly (FPA) moves from top dead center (TDC) and bottom dead center (BDC), the peak current and power output are greater than the other cycles. The peak current and power output when FPA moves from TDC to BDC are greater than those when FPA moves from BDC to TDC. The maximum current and power output can reach 0.8 A and 62.25 W. At the beginning of the expansion stage, as the intake duration (tin) increases, the in-cylinder pressure increases first and then decreases. As the intake pressure (pin) increases, the in-cylinder pressure continues to increase. In addition, the in-cylinder temperature of working chamber A (TA) is lower than in-cylinder temperature of working chamber B (TB), and the temperature difference between TA and TB can reach 12.4 K. At higher pin condition, the intake point cannot be too close to the two ends and tin cannot be too long. At lower pin condition, the intake point cannot be too close to both ends and tin cannot be too short to ensure the system starts. The purpose of this paper is to provide valuable insights and guidance for the practical application of small-scale CAES system.

Optimal scheduling of distributed generation in smart microgrids: A comprehensive model and efficient algorithm

Abstract This study proposes an optimal scheduling model for distributed generation (DG) within smart microgrids, incorporating various distributed energy resources (DERs) such as photovoltaic panels, wind turbines, biomass generators, and energy storage systems. To address the complexities of the scheduling problem, we design a hybrid optimization algorithm combining Genetic Algorithms (GA) and Particle Swarm Optimization (PSO). This hybrid algorithm leverages the global search capabilities of GA and the local search efficiency of PSO to achieve robust and efficient convergence to near-optimal solutions. A comprehensive case study based on a real-world smart microgrid system demonstrates the effectiveness of the proposed model and algorithm. The results indicate significant reductions in total operational costs, enhanced renewable energy utilization, reduced grid dependency, and improved system reliability. This research highlights the potential for broader implementation of the model, contributing to the advancement of smart grid technologies and the transition towards sustainable energy systems.

Robust polymer electrolytes with fast ion-transport nanochannels constructed by carbon dots for long lifespan Li metal batteries

As an anode material in energy storage systems, Li metal has exceptional merits, including high specific capacity, low density, and low redox potential. However, challenges such as lithium dendrites and unstable solid electrolyte interphase (SEI) hinder the practical application of lithium metal batteries (LMBs). Solid polymer electrolytes (SPEs), notably PVDF-HFP, are expected to overcome the above problems because of their mechanical merits and safe features, but their limited ion transportation behaviors result in insufficient ionic conductivity at room temperature. Herein, we invent an approach to prepare high performance electrolytes composed by porous PVDF-HFP and functionalized carbon dots (CDs). Such porous PVDF-HFP polymer membranes are created by a subtle phase inversion process, possessing a sponge-like structure, vertical lithium-ion transport channels and CDs decorated surfaces, thus able to uptake the liquid electrolyte (LE) to form gel polymer electrolytes (GPEs). The CDs fillers are produced in large scale with adjustable surface states, which can enhance Li salts disassociation to release free Li ions. As a result, the optimal conductivity of the as-prepared GPEs at room temperature is up to 8.7 mS cm−1 and the lithium transference number reaches 0.64. Our GPEs endow Li symmetric batteries with very stable SEI and long cycling life over 2200 h, and help realize high capacities and retention rates of the LMBs using LiFePO4(LFP) or LiCoO2(LCO) cathodes.

Fast and Smart State Characterization of Large-Format Lithium-Ion Batteries via Phased-Array Ultrasonic Sensing Technology

Lithium-ion batteries (LIBs) are widely used in electric vehicles and energy storage systems, making accurate state transition monitoring a key research topic. This paper presents a characterization method for large-format LIBs based on phased-array ultrasonic technology (PAUT). A finite element model of a large-format aluminum shell lithium-ion battery is developed on the basis of ultrasonic wave propagation in multilayer porous media. Simulations and comparative analyses of phased array ultrasonic imaging are conducted for various operating conditions and abnormal gas generation. A 40 Ah ternary lithium battery (NCMB) is tested at a 0.5C charge-discharge rate, with the state of charge (SOC) and ultrasonic data extracted. The relationship between ultrasonic signals and phased array images is established through simulation and experimental comparisons. To estimate the SOC, a fully connected neural network (FCNN) model is designed and trained, achieving an error of less than 4%. Additionally, phased array imaging, which is conducted every 5 s during overcharging and overdischarging, reveals that gas bubbles form at 0.9 V and increase significantly at 0.2 V. This research provides a new method for battery state characterization.