• The Transactions of the Korean Institute of Power Electronics
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• v.25 no.5
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• pp.376-384
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• 2020

An energy storage system is composed of lithium-ion batteries in modern applications. Batteries are regarded as storage devices for renewable and residual energy. The failure of batteries can cause the performance reduction and explosion of battery systems. High maintenance cost is essential when dealing with the problem of battery safety. Therefore an accurate health diagnosis is required to ensure the high reliability of battery systems. A battery pack is a combination of single cells in series and parallel connections. A battery pack has to consider various factors to assess battery health. Battery health involves conventional factors and additional factors, such as cell-to-cell imbalance. For large applications, state-of-health (SOH) can be inaccurate because of the lack of factors that indicate the state of the battery pack. In this study, six characterization factors are proposed for improving the SOH estimation of battery packs. The six proposed characterization factors can be regarded as health indicators (HIs). The six HIs are applied to the principal component analysis (PCA) algorithm. To reflect information regarding capacity, voltage, and temperature, the PCA algorithm extracts new degradation factors by using the six HIs. The new degradation factors are applied to a multiple regression model. Results show the advancement and improvement of SOH estimation.

• Journal of Power Electronics
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• v.13 no.4
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• pp.516-527
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• 2013

This paper presents a new overall system for state-of-available-power (SoAP) prediction for a lithium-ion battery pack. The essential part of this method is based on an adaptive network architecture which utilizes both fuzzy model (FIS) and artificial neural network (ANN) into the framework of adaptive neuro-fuzzy inference system (ANFIS). While battery aging proceeds, the system is capable of delivering accurate power prediction not only for room temperature, but also at lower temperatures at which power prediction is most challenging. Due to design property of ANN, the network parameters are adapted on-line to the current battery states (state-of-charge (SoC), state-of-health (SoH), temperature). SoC is required as an input parameter to SoAP module and high accuracy is crucial for a reliable on-line adaptation. Therefore, a reasonable way to determine the battery state variables is proposed applying a combination of several partly different algorithms. Among other SoC boundary estimation methods, robust extended Kalman filter (REKF) for recalibration of amp hour counters was implemented. ANFIS then achieves the SoAP estimation by means of time forward voltage prognosis (TFVP) before a power pulse occurs. The trade-off between computational cost of batch-learning and accuracy during on-line adaptation was optimized resulting in a real-time system with TFVP absolute error less than 1%. The verification was performed on a software-in-the-loop test bench setup using a 53 Ah lithium-ion cell.

• The Transactions of the Korean Institute of Power Electronics
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• v.26 no.6
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• pp.390-396
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• 2021

Lithium-ion batteries have been designed and used as battery packs with series and parallel combinations that are suitable for use. However, due to its internal electrochemical properties, producing the battery's condition at the same value is impossible for individual cells. In addition, the management of characteristic deviations between individual cells is essential for the safe and efficient use of batteries as aging progresses with the use of batteries. In this work, we propose a method to manage deviation properties and detect abnormal behavior in the configuration of a combined battery pack of these multiple battery cells. The proposed method can separate and detect probabilistic low-frequency information according to statistical information based on Z-score. The verification of the proposed algorithm was validated using experimental results from 10S3P battery packs, and the implemented algorithm based on Z-score was validated as a way to effectively manage multiple individual cell information.

• Journal of Electrical Engineering and Technology
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• v.13 no.1
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• pp.124-132
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• 2018

This paper proposes an LLC resonant converter based battery charger which utilizes an adaptive turn ratio scheme to achieve a wide output voltage range and high efficiency. The high frequency transformer of the LLC converter of the proposed strategy has an adaptively changed turn ratio through the auxiliary control circuit. As a result, an optimized converter design with high magnetizing inductance is possible, while minimizing conduction and turn-off losses and providing a regulated voltage gain to properly charge the lithium ion battery. For a step-by-step explanation, operational principle and optimal design considerations of the proposed converter are illustrated in detail. Finally, the effectiveness of the proposed strategy is verified through various experimental results and efficiency analysis based on prototype 300W Li-ion battery charger and battery pack.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.16 no.6
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• pp.385-391
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• 2023

As the use of electric vehicles has increased to minimize carbon emissions, the analyzing the state and performance of lithium-ion batteries that is instrumental in electric vehicles have been important. Comprehensive analysis using not only the voltage, current and temperature of the battery pack, which can affect the condition and performance of the battery, but also the driving data and charging pattern data of the electric vehicle is required. Therefore, a thorough analysis is imperative, utilizing electric vehicle operation data, charging pattern data, as well as battery pack voltage, current, and temperature data, which collectively influence the condition and performance of the battery. Therefore, collection and preprocessing of battery data collected from electric vehicles, collection and preprocessing of data on driver driving habits in addition to simple battery data, detailed design and modification of artificial intelligence algorithm based on the analyzed influencing factors, and A battery analysis and evaluation model was designed. In this paper, we gathered operational data and battery data from real-time electric buses. These data sets were then utilized to train a Random Forest algorithm. Furthermore, a comprehensive assessment of battery status, operation, and charging patterns was conducted using the explainable Artificial Intelligence (XAI) algorithm. The study identified crucial influencing factors on battery status, including rapid acceleration, rapid deceleration, sudden stops in driving patterns, the number of drives per day in the charging and discharging pattern, daily accumulated Depth of Discharge (DOD), cell voltage differences during discharge, maximum cell temperature, and minimum cell temperature. These factors were confirmed to significantly impact the battery condition. Based on the identified influencing factors, a battery analysis and evaluation model was designed and assessed using the Random Forest algorithm. The results contribute to the understanding of battery health and lay the foundation for effective battery management in electric vehicles.

• The Transactions of the Korean Institute of Power Electronics
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• v.27 no.3
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• pp.265-274
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• 2022

This paper proposes a machine learning-based screening algorithm to build the retired battery pack of the energy storage system. The proposed algorithm creates the dataset of various performance parameters of the retired battery, and this dataset is preprocessed through a principal component analysis to reduce the overfitting problem. The retried batteries with a large deviation are excluded in the dataset through a density-based spatial clustering of applications with noise, and the K-means clustering method is formulated to select the group of the retired batteries to satisfy the deviation requirement conditions. The performance of the proposed algorithm is verified based on NASA and Oxford datasets.

• Proceedings of the KIPE Conference
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• 2018.07a
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• pp.156-158
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• 2018

The inconsistencies between paralleled battery cells are becoming more considerable issue in high capacity battery applications like electric vehicles. Due to differences in state-of-charge (SOC) and internal resistance within individual cells in parallel, charging or discharging current is not appropriately balanced to each cell in terms of SOC, which may shorten the lifetime or sometimes cause safety issues. In this paper, an intelligent cell-balancing algorithm is proposed to overcome the inconsistency issue especially for paralleled battery cells. In this scheme, SOC information collected in the sub-BMS module is sent to the main-BMS module, where the number of parallel cells to be connected to DC bus is continuously updated based on the suggested SOC comparison rule. To verify the method, operation of the algorithm on 4 paralleled battery cells are simulated on Matlab/Simulink. The simulation result shows that the SOCs of paralleled cells are evenly redistributed. It is expected that the proposed algorithm provides high reliable and prolong the life cycle and working capacity of the battery pack.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.17 no.5
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• pp.199-208
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• 2017

Lithium polymer batteries are used in energy storage systems (ESS), electric vehicles (EVs), etc. due to their high safety, fast charging and long lifecycle, and now they are used in agricultural drones. However, when overcharging and overdischarging, the lithium-polymer battery is destroyed in the gap structure in the lithium-ion battery and the battery life is reduced. In order to prevent overcharge and overdischarge, uneven cell voltage Cell balancing system is needed. In this paper, a fuzzy controller suitable for nonlinear systems is proposed by detecting the unbalanced cells by detecting the voltage difference between charging and discharging of each cell, and suggesting the applied cell balancing algorithm. In this paper, we have designed the cell balancing of the battery pack of agricultural drones by fuzzy control and it is designed for equal control between cells. As a final result, we checked whether cell balancing is good, and when there are two cells, Cell balancing was confirmed. We tested whether it could be used for other products. As a result, we confirmed that cell balancing is good regardless of the number of cells used.

• Journal of the Semiconductor & Display Technology
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• v.14 no.4
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• pp.8-12
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• 2015

As NMP (N-Methyl-2pyrrolidone) is becoming important in many fields, the demand for it is also rising rapidly. With its chemical property of high boiling point, low vapor pressure and high water solubility, it is easy to recover it after processing. Therefore, it is increasingly needed to develop a system that effectively recovers NMP solvent. The study produced a system modeling using AMESim software before developing high purity solvent recovery (HPSR) system to recover NMP solvent. Then, it verified reliability by comparing the simulation model with the test result.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.22 no.5
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• pp.15-21
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• 2021

Electrically powered airplanes can reduce CO₂ emissions from fossil fuel use and reduce airplane costs in the long run through efficient energy use. For this reason, advanced aviation countries such as the United States and the European Union are leading the development of innovative technologies to implement the full-electric airplane in the future. Currently, the research and development to convert existing two-seater engine airplanes to electric-powered airplanes are underway domestically. The airplane converted to electric propulsion is the KLA-100, which aims to carry out a 30-minute flight test with a battery pack installed using the engine mounting space and copilot space. The lithium-ion battery installed on the airplane converted to electric propulsion was designed with a specific power of 150Wh/kg, weight of 200kg, and a C-rate 3~4. This study confirmed the possibility of a 30-minute flight with a designed battery pack before conducting a flight test of a modified electrically propelled airplane. The battery performance was verified by dividing the 30-minute flight profile into start/run stage, take-off stage, climbing stage, cruise stage, descending stage, and landing/run stage. The final target of the 30-minute flight was evaluated by calculating the battery capacity required for each stage. Furthermore, the flight performance of the electrically propelled airplane was determined by calculating the flight availability time and navigation distance according to the flight speed.