• Journal of Electrical Engineering and Technology
• /
• 제13권2호
• /
• pp.733-741
• /
• 2018

Capacity estimation is indispensable to ensure the safety and reliability of lithium-ion batteries in electric vehicles (EVs). Therefore it's quite necessary to develop an effective on-board capacity estimation technique. Based on experiment, it's found constant current charge time (CCCT) and the capacity have a strong linear correlation when the capacity is more than 80% of its rated value, during which the battery is considered healthy. Thus this paper employs CCCT as the health indicator for on-board capacity estimation by means of relevance vector machine (RVM). As the ambient temperature (AT) dramatically influences the capacity fading, it is added to RVM input to improve the estimation accuracy. The estimations are compared with that via back-propagation neural network (BPNN). The experiments demonstrate that CCCT with AT is highly qualified for on-board capacity estimation of lithium-ion batteries via RVM as the results are more precise and reliable than that calculated by BPNN.

• 한국지열·수열에너지학회논문집
• /
• 제17권2호
• /
• pp.1-10
• /
• 2021

Lithium-ion batteries with high energy density, long cycle life and other advantages, have been widely used to energy storage systems(ESS). But as ESS fires frequently occur, the safety concern has become the main obstacle that hinders the large-scale applications of lithium-ion batteries. Especially, thermal runaway is the key scientific problem in battery safety research. Therefore, in this study, we performed a numerical analysis on the thermal runaway phenomenon of NCM111, NCM523 and NCM622 batteries using a two-dimensional analysis model. The results show that the two-dimensional simulation results are generally matched with three-dimensional simulation. Also, In the case of NCM111 with a low Ni content in the temperature range used in this study, thermal runaway phenomenon does occurred very slowly, but as the Ni content is increased, the thermal runaway phenomenon occurs rapidly and the thermal stability tends to be decreased. And, in NCM523 and NCM622 batteries, chain reactions occur almost simultaneously, but in the case of NCM111 battery, it is found that after the SEI(Solid Electrolyte Interface) layer decomposition reaction, the cathode-electrolyte reaction is appeared sequentially. After that, the anodic decomposition reaction is increased and leads to the thermal runaway reaction.

• 전자통신동향분석
• /
• 제38권5호
• /
• pp.90-99
• /
• 2023

Recently, with the trend of information technology convergence and electrification, batteries are being widely used in fields such as industry, transportation, and specific applications. By 2030, the secondary battery market is expected to grow explosively by more than eight times compared with 2020 to $351.7 billion owing to the expanding adoption of electric vehicles. Depending on the electrochemical reactions in the electrode, a primary battery can only discharge through an irreversible reaction, while a secondary battery can be repeatedly charged and discharged using reversible reactions. According to the type of charge carrier ions, secondary batteries may be classified into those made of lithium, sodium, potassium, magnesium, and aluminum ions. We analyze the current status and technological issues of lithium-ion batteries, lithium-sulfur batteries, and solid-state batteries, which are representative examples of lithium secondary batteries. In addition, research trends in lithium secondary batteries are discussed.

• 산업기술연구
• /
• 제43권1호
• /
• pp.15-23
• /
• 2023

Lithium-ion batteries are predominantly employed in electric vehicles and energy storage devices, offering the advantage of high energy density. However, they are susceptible to efficiency degradation when operated at high temperatures due to their sensitivity to the external environment. In this study, we conducted experiments using an indirect cooling method to prevent thermal runaway and explosions in lithium-ion batteries. The results were validated by comparing them with heat transfer simulations conducted through a commercial finite element analysis program. The experiments included single-cell exothermic tests and cooling experiments on a battery pack with 10 cells connected in series, utilizing 21700 lithium-ion batteries. To block external temperature influences, the experimental environment featured an extrusion method insulation in the environmental chamber. The cooling system, suitable for indirect cooling, was constructed with copper tubes and pins. The heat transfer analysis began by presenting a single-cell heating model using commercial software, which was then employed to analyze the heating and cooling of the battery pack.

• 한국전기전자재료학회논문지
• /
• 제37권2호
• /
• pp.215-222
• /
• 2024

Ni-rich cathode materials have been developed as the most promising candidates for next-generation cathode materials for lithium-ion batteries because of their high capacity and energy density. In particular, the electrochemical performance of lithium-ion batteries could be enhanced by increasing the contents of nickel ion. However, there are still limitations, such as low structural stability, cation mixing, low capacity retention and poor rate capability. Herein, we have successfully developed the nanorod-type Ni-rich cathode materials by using co-precipitation method. Particularly, the nanorod-type primary particles of LiNi_0.7Co_0.15Mn_0.15O₂ could facilitate the electron transfer because of their longitudinal morphology. Moreover, there were holes at the center of secondary particles, resulting in high permeability of the electrolyte. Lithium-ion batteries using the prepared nanorod-type LiNi_0.7Co_0.15Mn_0.15O₂ achieved highly improved electrochemical performance with a superior rate capability during battery cycling.

• 산업기술연구
• /
• 제40권1호
• /
• pp.13-18
• /
• 2020

Lithium-ion batteries have a small volume, light weight and high energy density, maximizing the utilization of mobile devices. It is widely used for various purposes such as electric bicycles and scooters (e-Mobility), mass energy storage (ESS), and electric and hybrid vehicles. To date, lithium-ion batteries have grown to focus on increasing energy density and reducing production costs in line with the required capacity. However, the research and development level of lithium-ion batteries seems to have reached the limit in terms of energy density. In addition, the charging time is an important factor for using lithium-ion batteries. Therefore, it was urgent to develop a high-speed charger to shorten the charging time. In this thesis, a discharger was fabricated to evaluate the capacity and characteristics of Li-ion battery pack which can be used for e-mobility. To achieve this, a smart discharger is designed with a combination of active load, current sensor, and temperature sensor. To carry out this thesis, an active load switching using sensor control circuit, signal processing circuit, and FET was designed and manufactured as hardware with the characteristics of active discharger. And as software for controlling the hardware of the active discharger, a Raspberry Pi control device and a touch screen program were designed. The developed discharger is designed to change the 600W capacity battery in the form of active load.

• Journal of Electrochemical Science and Technology
• /
• 제13권1호
• /
• pp.19-31
• /
• 2022

Lithium-ion battery development is one of the most active contemporary research areas, gaining more attention in recent times, following the increasing importance of energy storage technology. The galvanostatic intermittent titration technique (GITT) has become a crucial method among various electrochemical analyses for battery research. During one titration step in GITT, which consists of a constant current pulse followed by a relaxation period, transient and steady-state voltage changes were measured. It draws both thermodynamic and kinetic parameters. The diffusion coefficients of the lithium ion, open-circuit voltages, and overpotentials at various states of charge can be deduced by a series of titration steps. This mini-review details the theoretical and practical aspects of GITT analysis, from the measurement method to the derivation of the diffusivity equation for research cases according to the specific experimental purpose. This will shed light on a better understanding of electrochemical reactions and provide insight into the methods for improving lithium-ion battery performance.

• Journal of Power Electronics
• /
• 제17권5호
• /
• pp.1288-1297
• /
• 2017

The estimation of the remaining useful life (RUL) of lithium-ion (Li-ion) batteries is important for intelligent battery management system (BMS). Data mining technology is becoming increasingly mature, and the RUL estimation of Li-ion batteries based on data-driven prognostics is more accurate with the arrival of the era of big data. However, the support vector machine (SVM), which is applied to predict the RUL of Li-ion batteries, uses the traditional single-radial basis kernel function. This type of classifier has weak generalization ability, and it easily shows the problem of data migration, which results in inaccurate prediction of the RUL of Li-ion batteries. In this study, a novel multi-kernel SVM (MSVM) based on polynomial kernel and radial basis kernel function is proposed. Moreover, the particle swarm optimization algorithm is used to search the kernel parameters, penalty factor, and weight coefficient of the MSVM model. Finally, this paper utilizes the NASA battery dataset to form the observed data sequence for regression prediction. Results show that the improved algorithm not only has better prediction accuracy and stronger generalization ability but also decreases training time and computational complexity.

• Journal of Electrochemical Science and Technology
• /
• 제13권1호
• /
• pp.90-99
• /
• 2022

Owing to the rising concern of global warming, lithium-ion batteries have gained immense attention over the past few years for the development of highly efficient electrochemical energy conversion and storage systems. In this study, alpha-phase VOPO₄·2H₂O with nanosheet morphology was prepared via a facile hydrothermal method for application in high-performance lithium-ion batteries. The X-ray diffraction and scanning electron microscopy (SEM) analyses indicated that the obtained sample had an alpha-2 (αII) phase, and the nanosheet morphology of the sample was confirmed using SEM. The lithium-ion battery with VOPO₄·2H₂O as the anode exhibited excellent long-term cycle life and a high capacity of 256.7 mAh g^-1 at room temperature. Prelithiation effectively improved the specific capacity of pristine VOPO₄·2H₂O. The underlying electrochemical mechanisms were investigated by carrying out AC impedance, rate capability, and other instrumental analyses.

• International Journal of Advanced Culture Technology
• /
• 제11권3호
• /
• pp.310-314
• /
• 2023

The purpose of this study is to predict the remaining capacity of lithium-ion batteries and evaluate their performance using five artificial intelligence models, including linear regression analysis, decision tree, random forest, neural network, and ensemble model. We is in the study, measured Excel data from the CS2 lithium-ion battery was used, and the prediction accuracy of the model was measured using evaluation indicators such as mean square error, mean absolute error, coefficient of determination, and root mean square error. As a result of this study, the Root Mean Square Error(RMSE) of the linear regression model was 0.045, the decision tree model was 0.038, the random forest model was 0.034, the neural network model was 0.032, and the ensemble model was 0.030. The ensemble model had the best prediction performance, with the neural network model taking second place. The decision tree model and random forest model also performed quite well, and the linear regression model showed poor prediction performance compared to other models. Therefore, through this study, ensemble models and neural network models are most suitable for predicting the remaining capacity of lithium-ion batteries, and decision tree and random forest models also showed good performance. Linear regression models showed relatively poor predictive performance. Therefore, it was concluded that it is appropriate to prioritize ensemble models and neural network models in order to improve the efficiency of battery management and energy systems.