Single Battery Cell Research Articles

State-of-health estimation and classification of series-connected batteries by using deep learning based hybrid decision approach

In rechargeable battery control and operation, one of the primary obstacles is safety concerns where the battery degradation poses a significant factor. Therefore, in recent years, state-of-health assessment of lithium-ion batteries has become a noteworthy issue. On the other hand, it is challenging to ensure robustness and generalization because most state-of-health assessment techniques are implemented for a specific characteristic, operating situation, and battery material system. In most studies, health status of single cell batteries is assessed by using analytical or computer-aided deep learning methods. But, the state-of-health characteristics of series-connected battery systems should be also focused with advances of technology and usage, especially electric vehicles. This study presents a data-driven, deep learning-based hybrid decision approach for predicting the state-of-health of series-connected lithium-ion batteries with different characteristics. The paper consists of generating series-connected battery degradation dataset by using of some mostly used datasets. Also, by employing deep learning-based networks along with hybrid-classification aided by performance metrics, it is shown that estimating and predicting the state-of-health can be achieved not only by using sole deep-learning algorithms but also hybrid-classification techniques. The results demonstrate the high accuracy and simplicity of the proposed novel approach on datasets from Oxford University and Calce battery group. The best estimated mean squared error, root mean square error and mean-absolute percentage error values are not more than 0.0500, 0.2236 and 0.7065, respectively which shows the efficiency not only by accuracy but also error indicators. The results show that the proposed approach can be implemented in offline or online systems with best average accuracy of 98.33 % and classification time of 58 ms per sample.

Multi-Scale Modeling and Simulation of All-Solid-State Sodium-Ion Batteries with Polymer-Ceramic Hybrid Electrolytes

Lithium-ion batteries (LIBs) have been widely considered as the most promising power source for mobile devices. However, LIBs cannot meet the increasing global demand for electrochemical energy storage in the foreseeable future. Sodium ion-batteries (SIBs) are based on more ecofriendly and earth-abandoned materials. Commonly researched SIBs are based on liquid electrolytes. They pose leakage and thermal runaway risks. Solid polymer electrolytes solve these problems but need to operate at temperatures between 60-80°C to compensate insufficient ionic conductivity of polymer electrolytes at ambient temperatures.We present a polymer-ceramic hybrid electrolyte which overcomes the necessity to operate at high temperatures. It combines the advantages of high ion-conducting solid ceramic particles and flexible polymers. Sodium metal serves as the anode and PEO with inorganic fillers is used as cathode electrolyte. Due to their sustainability and high efficiency —comparable to LIBs— sodium-ion batteries are a promising candidate for small scale stationary energy storage applications or mobile energy storage applications with less constrains regarding energy density (e.g. trains, ships, submarines etc.). This contribution highlights a modeling approach for simulations of a single battery cell to develop an optimal cell design of the aforementioned battery system. A newly developed multiscale modeling method for hybrid polymer-ceramic sodium ion batteries reflects the different length scales of the active particle level and the electrode level. We precisely describe the spatially resolved mass and charge transport as well as electrochemical reactions in the active particle and the surrounding electrolyte employing a finite volume method [1]. Both domains are resolved in 3D and the model framework was validated against experimental data. The model output is incorporated in the connected pseudo-two-dimensional battery model for fast simulations of the overall battery cell considering mass and charge transport. The results support the importance of particle (active particle and ceramic filler particle) properties and configurations as well as electrolyte composition. Based on this, we exploit an evolutionary algorithm to suggest optimized battery designs for different use cases. In Addition, we present the evaluation of electrolyte properties with solid-state NMR spectroscopy and their model implementation [2]. μCT imaging experimentally investigates the cathode half-cell morphology. The derived virtual reconstructions enable realistic microstructure simulations of the battery cells. In this presentation, we will suggest two cell designs: The high-energy cell optimizes material efficiency and energy density. The high-power cell opts for high power densities (fast charging).In summary, we present a novel all-solid-state polymer-ceramic hybrid sodium-ion battery type for small stationary or large mobile energy storage units. Our contribution outlines a simulation approach that sheds light on macroscopic and microscopic processes inside the battery-cell using a multiscale modeling method. As a result, a high-energy and a high-power design is derived. Gerbig, M. Holzapfel and H. Nirschl, J. Electrochem. Soc., 170(4), 40517 (2023).Moradipour, A. Markert, T. Rudszuck, N. Röttgen, G. Dück, M. Finsterbusch, F. Gerbig, H. Nirschl and G. Guthausen, J Energ Power Technol, 05(04), 1–21 (2023).

Lithium-Ion Battery Thermal Runaway: Experimental Analysis of Particle Deposition in Battery Module Environment

In this paper, a novel experimental setup to quantify the particle deposition during a lithium-ion battery thermal runaway (TR) is proposed. The setup integrates a single prismatic battery cell into an environment representing similar conditions as found for battery modules in battery packs of electric vehicles. In total, 86 weighing plates, positioned within the flow path of the vented gas and particles, can be individually removed from the setup in order to determine the spatial mass distribution of the deposited particles. Two proof-of-concept experiments with different distances between cell vent and module cover are performed. The particle deposition on the weighing plates as well as the particle size distribution of the deposited particles are found to be dependent on the distance between cell vent and cover. In addition, the specific heat capacity of the deposited particles as well as the jelly roll remains are analyzed. Its temperature dependency is found to be comparable for both ejected particles and jelly roll remains. The results of this study help researches and engineers to gain further insights into the particle ejection process during TR. By implementing certain suggested improvements, the proposed experimental setup may be used in the future to provide necessary data for simulation model validation. Therefore, this study contributes to the improvement of battery pack design and safety.

Detection and analysis of inner potential dynamics in vanadium redox flow batteries

The interconnectedness of the inner potential dynamics during the charge–discharge operation of a vanadium redox flow battery is studied by in-situ measurements of the through-plane potential distribution of the battery cell and the states of charge of the electrolytes. For a quantitative investigation, experimentally probed information is integrated and analyzed using simplified physics-based theoretical models, which collectively enable accurate detection and interpretatation of the inner potential dynamics from the individual components to the single-cell battery level. It has been demonstrated that this collective analysis makes quantifying the ohmic-kinetic and mass transport resistances of the electrodes possible. In particular, the sluggish transport of vanadium ions near the membrane interface of both electrodes leads to additional diffusion overpotential at the beginning of charging and discharging in the electrode. The membrane potentials, which can be detected experimentally, reflect highly coupled conditions of the electrolytes on both sides that affect the voltage of each electrode and the state of charges of the battery. This work demonstrates that unveiling the internal dynamics within a cell leads to an in-depth understanding of the charge–discharge behavior of vanadium redox flow batteries.

A Review on Fast Charging Methodologies of Electric Vehicles

The Li-Ion battery is charged using a constant voltage, constant current charging method. The solution, photovoltaic energy, is making its appearance in the EV charging infrastructure. By altering the transformer ratio, the inverter's voltage gain can be adjusted. Instantaneous thermal gradients are altered by fast charging. The grid and electricity quality may be impacted by EV charging.The goal of this study is to create a comprehensive, current overview of fast charging techniques for battery-electric vehicles (BEVs). The foundational ideas of single battery cell charging as well as existing and upcoming charging standards are covered in this paper. Globally, battery-powered electric vehicles are becoming more and more common. Numerous causes, such as the need to lessen noise and air pollution and our reliance on fossil fuels, are driving this trend. The primary disadvantage of modern electric vehicles is their short range and the length of time needed to charge their batteries. Through research and development, great strides have been achieved recently to use pulse charging, as opposed to continuous current and/or voltage delivery, to shorten the charging period of the batteries in electric vehicles. The portion that needs to be concentrated on estimating the electrical properties of the vehicle's battery is crucial for learning about the potential driving range.

Combined Experimental and Numerical Approach for the Thermal Heat Exchange Investigation of Li-Ion Cells for Automotive Applications

Lithium-ion (Li-ion) battery is an advanced technology in the field of electrochemical energy storage, but its management constitutes one of the most intriguing challenges for electric vehicles. Many parameters need to be controlled and managed and many aspects need to be optimised. This work presents a methodology for laboratory characterization of Nickel Manganese Cobalt (NMC) Lithium-Ion batteries suited for automotive applications. The purpose consists of obtaining a detailed description of the electrical and thermal behaviour of a single battery cell to provide an accurate model (static, dynamic, and thermal) that could ensure optimized real-time battery management by a management system for several battery packs. A battery testing system was built using a bidirectional power supply and a software/hardware interface was implemented within the National Instruments LabVIEW environment that monitors current, voltage and temperature sensors. This dedicated laboratory equipment can be used to apply and report charging/discharging cycles according to the user-defined load profile. A bidimensional CFD dynamic condition/transient simulation in the Ansys FLUENT environment was performed to study the heat thermal fluxes generated by a determined current value in the battery cells, and the results have been compared to the experimental data for validation.

Numerical Study on Heat Generation Characteristics of Charge and Discharge Cycle of the Lithium-Ion Battery

Lithium-ion batteries are the backbone of novel energy vehicles and ultimately contribute to a more sustainable and environmentally friendly transportation system. Taking a 5 Ah ternary lithium-ion battery as an example, a two-dimensional axisymmetric electrochemical–thermal coupling model is developed via COMSOL Multiphysics 6.0 in this study and then is validated with the experimental data. The proportion of different types of heat generation in a 26,650 ternary lithium-ion battery during the charge/discharge cycle is investigated numerically. Moreover, the impact of essential factors such as charge/discharge multiplier and ambient temperature on the reaction heat, ohmic heat, and polarization heat are analyzed separately. The numerical results indicate that the total heat generated by the constant discharge process is the highest in the charging and discharging cycle of a single battery. The maximum heat production per unit volume is 67,446.99 W/m3 at 2 C multiplier discharge. Furthermore, the polarization heat presents the highest percentage in the charge/discharge cycle, reaching up to 58.18% at 0 C and 1 C multiplier discharge. In a high-rate discharge, the proportion of the reaction heat decreases from 34.31% to 12.39% as the discharge rate increases from 0.5 C to 2 C. As the discharge rate rises and the ambient temperature falls, the maximum temperature increase of the single-cell battery also rises, with a more pronounced impact compared to increasing the discharge rate.

Incorporating Copper and Carbon Nanotube Nanoparticles into Phase Change Materials for Enhanced Thermal Management in Batteries

The primary objective of this study was to explore the impact of integrating nano-additives on heat transfer enhancement within an LHTES (Latent Heat Thermal Energy Storage) system. Our findings revealed that introducing a minimal amount of nano-materials (less than 5.0 vol%) into the Phase Change Material (PCM) led to a remarkable alignment between experimental correlations and mixture models. Furthermore, employing the mixing model allowed for the accurate prediction of outcomes. In the current context of scientific research, there is a strong endorsement for the widespread adoption of Electric Vehicles (EVs) as a sustainable alternative to Internal Combustion Engines (ICEs), crucial for advancing decarbonization and mitigating climate-related crises. Lithium-ion batteries are the predominant choice for EVs and various electrical devices. However, the operational challenges posed by high temperatures, impacting their lifespan, charge/discharge cycles, and the risk of thermal runaway, necessitate effective thermal management systems. Given this background, our study focuses on simulating the heat dissipation of a single cylindrical Li-ion battery cell employing a Nano-enhanced Phase Change Material (NePCM)-based cooling system. We also introduce a novel fin design in this work. Through comprehensive analysis, we examine the performance of battery modules utilizing PCM and NePCM at different discharge rates, both with and without the newly proposed fin design. Our research demonstrates that the incorporation of fins and nanoparticles into PCM significantly enhances heat transfer and reduces the charging time compared to the base PCM. Notably, carbon-based nanoparticles outshine their metal-based counterparts in terms of melting rate and maintaining a uniform temperature profile within the battery.

Modeling of the Battery Pack and Battery Management System towards an Integrated Electric Vehicle Application

The transportation sector is under increasing pressure to reduce greenhouse gas emissions by decarbonizing its operations. One prominent solution that has emerged is the adoption of electric vehicles (EVs). As the electric vehicles market experiences rapid growth, the utilization of lithium-ion batteries (LiB) has become the predominant choice for energy storage. However, it is important to note that lithium-ion battery technology is sensitive to factors, like excessive voltage and temperature. Therefore, the development of an accurate battery model and a reliable state of charge (SOC) estimator is crucial to safeguard against the overcharging and over-discharging of the battery. Numerous studies have been conducted to address lithium-ion battery cell modeling and SOC estimations. These studies have explored variations in the number of RC networks within the model and different estimation methods. However, it is worth mentioning that the capacity of a single lithium-ion battery cell is relatively low and cannot be directly employed in electric vehicles. To meet the total capacity and voltage requirements for electric vehicles, multiple cells are typically connected in series or parallel configurations to form a battery pack. Surprisingly, this aspect has often been overlooked in previous research. To tackle this overlooked challenge, our study introduces a comprehensive battery pack model and an advanced Battery Management System (BMS). We then integrate these components into an electric vehicle model. Subsequently, we simulate the integrated EV-BMS model under the conditions of four different urban driving scenarios to replicate real-world driving conditions. The BMS that we have developed includes an Extended Kalman Filter (EKF)-based SOC estimation system, a mechanism for controlling coolant flow, and a passive cell-balancing algorithm. These components work together to ensure the safe and efficient operation of the battery pack within the electric vehicles.

Using a hybrid system to improve a lithium-ion battery in the presence of phase change material and the effect of air on the battery charge and discharge

In this article, a numerical analysis is done on the temperature of 4 plate-shaped battery cells with phase change material (PCM) chambers around each one in a rectangular shape. The batteries are placed in a channel with air flow. The study is done transiently in a time of 10 min. The batteries are of lithium ion type and the analysis is provided in two dimensions. The battery cells are arranged in the form of two single battery cells at the beginning, and end of the channel and two battery cells in the middle of the channel. These two middle batteries are placed in parallel. By changing the distance between the two middle batteries from two to three cm, this study is conducted to investigate the temperature of each of the four battery cells and changes in the amount of frozen PCM. Finally, the results showed that the temperature of the two batteries at the beginning and the end, increased continuously during the 10 min of the study. At a distance of three cm from the middle batteries, the lowest temperature occurred on the first and last batteries, while at the same distance, the highest temperature occurred on the middle ones. At a distance of two cm from the middle batteries, the lowest amount of frozen PCM was observed, while at a distance of three cm from the middle batteries, the highest amount of frozen PCM was found on the first and last batteries.

Optimization of module structure considering mechanical and thermal safety of pouch cell lithium-ion batteries using a reliability-based design optimization approach

Design optimization is an important method for improving the performance of lithium-ion batteries. However, the majority of earlier studies on battery optimization have generally concentrated on enhancing the performance of a single battery cell or focusing on particular objectives of the module and pack structures. Therefore, this study mainly focuses on developing a generalized optimization framework to increase the energy density of the module while satisfying the mechanical and thermal safety requirements. To the best of our knowledge, this work is the first to attempt to enhance the module performance while considering both mechanical and thermal safety. Moreover, this study develops a reliability-based design optimization (RBDO) framework for module structures that considers system uncertainties to mitigate the possibility of safety failures and obtain a reliable design. The mechanical behavior of the module shows how swelling, breathing, material types, and properties affect the final module length and stress. The effects of the C-rate and design parameters on the maximum temperature and temperature deviation are also investigated. All designs satisfy the target probability of failure, verifying that the RBDO framework of the module structure has been successfully established. The proposed RBDO framework provides guidance for the design and operation of battery modules.

An efficient state-of-health estimation method for lithium-ion batteries based on feature-importance ranking strategy and PSO-GRNN algorithm

As a critical index in ensuring the safe and reliable deployment of lithium-ion batteries (LIBs), the estimation performance of state-of-health (SOH) seriously affects the popularization of electric vehicles (EVs). However, for the existing SOH estimation methods based on machine learning algorithms, the health features are manually extracted mainly based on personal experience, without algorithm-based automatic preferential selection. Thus, this study proposes a battery SOH estimation method based on the feature-importance ranking strategy and generalized regression neural network optimized by particle swarm optimization (PSO-GRNN). Firstly, 19 vital features were extracted from charging voltage, charging current, temperature, and incremental capacity (IC) curves. Secondly, the importance indexes for these features were calculated and optimally ranked by fusing the machine learning algorithm (random forest) and statistical analysis (Pearson correlation coefficient). Accordingly, different combinations of high-ranking features were compared to determine the best number of the final feature set. Thirdly, the SOH estimation model is built by the PSO-GRNN algorithm with optimal 7 high-ranking features. Based on the publicly available NASA datasets, the proposed SOH estimation model was validated on the data from a single-cell battery. Furthermore, the datasets from three cells with different cycling test conditions were utilized, where the data from two cells and the other cell were alternately selected as the training and test sets, respectively. The SOH estimation errors (squared absolute error and mean squared error) for single-cell and multi-cell experiments were less than 1 % and 3 %, respectively. Experimental results show that the proposed method is robust and can achieve accurate and lightweight SOH estimation.

Research on Calendar Aging for Lithium-Ion Batteries Used in Uninterruptible Power Supply System Based on Particle Filtering

The aging process of lithium-ion batteries is an extremely complex process, and the prediction of the calendar life of the lithium-ion battery is important to further guide battery maintenance, extend the battery life and reduce the risk of battery use. In the uninterruptible power supply (UPS) system, the battery is in a floating state for a long time, so the aging of the battery is approximated by calendar aging, and its decay rate is slow and difficult to estimate accurately. This paper proposes a particle filtering-based algorithm for battery state-of-health (SOH) and remaining useful life (RUL) predictions. First, the calendar aging modeling for the batteries used in the UPS system for the Shanghai rail transportation energy storage power station is presented. Then, the particle filtering algorithm is employed for the SOH estimation and RUL prediction for the single-cell battery calendar aging model. Finally, the single-cell SOH and RUL estimation algorithm is expanded to the pack and group scales estimation. The experimental results indicate that the proposed method can achieve accurate SOH estimation and RUL prediction results.

Computational design and analysis of LiFePO4 battery thermal management system (BTMS) using thermoelectric cooling/thermoelectric generator (TEC–TEG) in electric vehicles (EVs)

The best option for addressing the issue of rising carbon dioxide levels, which is the primary cause of global warming, currently involves using electric vehicles (EVs). The successful production of EVs can be attributed to batteries. However, one major issue lies in the rise in temperatures for the battery system of EVs. Therefore, a good battery thermal management system (BTMS) is necessary. Several traditional and non-traditional types of these systems are available. BTMSs for EVs have utilized thermoelectric cooling (TEC) and thermoelectric generator (TEG). The current research introduces a hybrid BTMS that combines thermoelectric materials with forced air. While the use of thermoelectric materials in BTMS is not a new concept, this approach offers a novel solution. In the current study, the thermoelectric cooler (TEC) and thermoelectric generator (TEG) are combined into a single unit. While TECs have long been used in BTMS, the new addition of TEGs allows for the conversion of lost heat from the TEC's hot surface into a reverse voltage that powers both the TEC and TEG. Additionally, the TEG helps to reduce the overall temperature of the battery container by converting heat into a potential difference, as previously mentioned. Simulation of the single battery cell and the full BTMS is realized using the ANSYS 2021R1 software. A single battery cell and BTMS utilize 6,197,879 and 12,697,173 numbers of mesh, respectively. The introduced BTMS was utilized in the current study to decrease the maximum surface temperature of a single battery cell by approximately 7 °C.

Atomic iron on porous graphene films for catalyzing the VO2+/VO2+ redox couple in vanadium redox flow batteries

The electrocatalytic activity of the electrode materials towards the vanadium redox couples is a major factor in determining the performance of vanadium redox flow batteries (VRFBs). Herein, we report the employment of iron single atoms supported on a monolithic porous graphene film (Fe1-PGF) as electrodes for catalyzing the VO2+/VO2+ redox couple. The high intrinsic catalytic activity of the atomic Fe sites, the superior surface hydrophilicity, along with the enhanced mass transfer efficiency and exposure of active sites benefited from the porous structure, endow the Fe1-PGF electrode with improved electrochemical performance compared to metal-free PGF counterpart and commercial graphite felt electrode. Consequently, the single-cell battery assembled with Fe1-PGF exhibits a high voltage efficiency (77.96%) and energy efficiency (70.27%) at the current density of 60 mA cm−2 as well as lower overpotential and higher rate capability compared to the control samples. This study demonstrates that single atom catalysts (SACs) are promising candidates for catalyzing the vanadium redox couples in VRFBs and motivates the exploration of SACs in other types of redox flow batteries.

Experimental studies of a static flow immersion cooling system for fast-charging Li-ion batteries

ABSTRACT This study explores the performance of a steady-state flow single-phase non-conductive liquid immersion cooling system in a single-cell Li-ion battery under a variety of thermal environments such as 25°C, 40°C, and 60°C and tested at different charge C-rates. The experimental results show that the proposed cooling system exhibited the best cooling performance of less than 5°C and a significant temperature rise reduction of 34% at 3C rate under the ambient temperature of 25°C. Also, it provided uniform temperature distribution within the cell when charged at fast charge rates of 2C and 3C operations.

Electrochemical Modelling of Na-MCl2 Battery Cells Based on an Expanded Approximation Method

Battery models are mathematical systems that aim to simulate real battery cell sufficiently accurately. Finding a comprise between complexity, computational effort and accuracy is thereby key. In particular, modelling sodium–nickel–chloride/iron-chloride cells (Na-NiCl2/FeCl2), as a promising alternative for stationary energy storage, bears some challenges. The literature shows a few interesting approaches, but in most of them the second active material (NiCl2 or FeCl2) or the entire discharging/charging cycle is not considered. In this work, an electrochemical and thermal model of Na-NiCl2/FeCl2 battery cells is presented. Based on an equivalent circuit approach combined with electrochemical calculations, the hybrid model provides information on the performance of the cell for charging and discharging with a constant current. By dividing the cathode space into segments, internal material and charge flows are predicted, allowing important insights into the internal cell processes. Besides a low calculation effort, the model also allows a flexible adaption of cathode composition and cell design, which makes it a promising tool for the development of single battery cells as well as battery modules and battery systems.

A Sensitivity Study of a Thermal Propagation Model in an Automotive Battery Module

Thermal runaway is a major concern for lithium-ion batteries in electric vehicles. A manufacturing fault or unusual operating conditions may lead to this event. Starting from a single battery cell, more cells may be triggered into thermal runaway, and the battery pack may be destroyed. To prevent this from happening, safety solutions need to be evaluated. Physical testing is an effective, yet costly, method to assessing battery safety performance. As such, the potential of a numerical tool, which can cut costs and reduce product development times, is investigated in terms of capturing a battery module’s tolerance to a single cell failure. A 3D-FE model of a battery module was built, using a commercial software, to study thermal runaway propagation. The model assumes that when the cell jelly roll reaches a critical value, thermal runaway occurs. This approach was considered to study the module’s tolerance to a single cell failure, which was in reasonable agreement with what had been observed in full-scale experiments. In addition, quantitative sensitivity study on the i) model input parameters, ii) model space, and iii) time resolutions on the computed start time instant and time duration of thermal runaway were performed. The critical temperature was found to have the greatest influence on thermal runaway propagation. The specific heat capacity of jelly roll was found to significantly impact the thermal runaway time duration. The multi-physics model for battery thermal propagation is promising and worth to be applied with care for designing safer batteries in combination with physical testing.

Thermal management scheme and optimization of cylindrical lithium-ion battery pack based on air cooling and liquid cooling

Battery thermal management system (BTMS) ensures the batteries work in a safe and suitable temperature range. In this study, a hybrid BTMS based on air cooling and liquid cooling is proposed. The heat generated by the battery is transferred to the coolant by heat conducting blocks (HCBs) which are evenly spaced along the axial direction of it to maintain the normal operation of the battery pack. Air cooling is then introduced to maintain the battery's temperature uniformity at the battery pack's edge. A three-dimensional simulation model was designed and established to explore the number and size of HCBs, the effects of flow rate and the addition of air cooling on the comprehensive performance of BTMS. The results indicate that a good balance of cooling performance, power consumption, and lightweight will be achieved when the number of HCBs is three, the diameter of the cooling channel on the heat exchanger block is 6 mm and the flow rate of each cooling channel is 0.002 kg/s. In this case, the maximum temperature (Tmax) is 34.41 °C and the maximum temperature difference (ΔT) is 1.53 °C. The addition of air cooling lowers Tmax and ΔT by 3.75 °C and 0.96 °C, respectively, and lowers the maximum temperature difference of single battery cell from 6.31 °C to 3.86 °C. Additionally, when intermittent air cooling is used, system power consumption is decreased while the battery pack can operate within the proper temperature range.

Effect of Capacity Variation in Series-Connected Batteries on Aging

Batteries are used in various combinations in various fields. Research on single-cell batteries is well underway and is approaching a stabilization phase. However, problems caused by battery combinations are still insufficiently studied. The purpose of this study was to investigate the cause of fires due to gradual damage in a large-capacity energy storage system (ESS). In the paper Energy Storage System Safety Operation Plan by Preventing Overcharge During Relaxation Time, which was based on the fact that most fires in large-capacity energy storage devices occurred during the diastolic period, it was proven that the inflow of compensation current due to a voltage imbalance in the cell was the cause. The total amount of compensation current is determined by the voltage deviation of the battery. Batteries connected in series have different rates of aging due to differences in their capacities. Thus, with use, the total amount of compensating current continues to increase until a fire occurs. In this study, by analyzing the effect of battery-capacity deviation on the aging of individual cells, it was confirmed that the capacity deviation increased as the battery was used, resulting in an increase in the total amount of compensation current. In addition, if a solution to the problem is presented and the proposed solution is applied, the allowable range of battery-capacity deviation will be widened. We used Matlab 2009a, assuming a real environment. Using Simulink, problems were identified through simulation, improvement measures were suggested, and the proposed method was verified via simulation.