• Title/Summary/Keyword: 리튬이온배터리 데이터

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Comparison of the Machine Learning Models Predicting Lithium-ion Battery Capacity for Remaining Useful Life Estimation (리튬이온 배터리 수명추정을 위한 용량예측 머신러닝 모델의 성능 비교)

  • Yoo, Sangwoo;Shin, Yongbeom;Shin, Dongil
    • Journal of the Korean Institute of Gas
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    • v.24 no.6
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    • pp.91-97
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    • 2020
  • Lithium-ion batteries (LIBs) have a longer lifespan, higher energy density, and lower self-discharge rates than other batteries, therefore, they are preferred as an Energy Storage System (ESS). However, during years 2017-2019, 28 ESS fire accidents occurred in Korea, and accurate capacity estimation of LIB is essential to ensure safety and reliability during operations. In this study, data-driven modeling that predicts capacity changes according to the charging cycle of LIB was conducted, and developed models were compared their performance for the selection of the optimal machine learning model, which includes the Decision Tree, Ensemble Learning Method, Support Vector Regression, and Gaussian Process Regression (GPR). For model training, lithium battery test data provided by NASA was used, and GPR showed the best prediction performance. Based on this study, we will develop an enhanced LIB capacity prediction and remaining useful life estimation model through additional data training, and improve the performance of anomaly detection and monitoring during operations, enabling safe and stable ESS operations.

CNN based battery SOC estimation using thermal distribution image (CNN 기반 열 분포 영상을 이용한 배터리 SOC 추정 연구)

  • Kwon, Sanguk;Kim, Jaeho;Kim, Yongsoon;Ahn, Jeongho;Choi, Eojin;Pack, Jinu;Kim, Jonghoon
    • Proceedings of the KIPE Conference
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    • 2019.07a
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    • pp.453-454
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    • 2019
  • 본 논문은 ESS(Energy Storage System)의 과충전, 과방전으로 인한 열 폭주 현상을 방지하기 위한 사전 연구로 원통형 리튬이온 단일 셀의 충/방전에 따른 열 분포를 열화상 카메라로 촬영하여 분석하였다. 실험을 통한 열 분포 이미지를 학습 데이터로 구성하여, SOC(State of Charge)를 추정하는 CNN(Convolution Neural Network) 모델을 제안한다.

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Research on artificial intelligence based battery analysis and evaluation methods using electric vehicle operation data (전기 차 운행 데이터를 활용한 인공지능 기반의 배터리 분석 및 평가 방법 연구)

  • SeungMo Hong
    • 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.

Remaining Useful Life Prediction of Li-Ion Battery Based on Charge Voltage Characteristics (충전 전압 특성을 이용한 리튬 이온 배터리의 잔존 수명 예측)

  • Sim, Seong Heum;Gang, Jin Hyuk;An, Dawn;Kim, Sun Il;Kim, Jin Young;Choi, Joo Ho
    • Transactions of the Korean Society of Mechanical Engineers B
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    • v.37 no.4
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    • pp.313-322
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    • 2013
  • Batteries, which are being used as energy sources in various applications, tend to degrade, and their capacity declines with repeated charging and discharging cycles. A battery is considered to fail when it reaches 80% of its initial capacity. To predict this, prognosis techniques are attracting attention in recent years in the battery community. In this study, a method is proposed for estimating the battery health and predicting its remaining useful life (RUL) based on the slope of the charge voltage curve. During this process, a Bayesian framework is employed to manage various uncertainties, and a Particle Filter (PF) algorithm is applied to estimate the degradation of the model parameters and to predict the RUL in the form of a probability distribution. Two sets of test data-one from the NASA Ames Research Center and another from our own experiment-for an Li-ion battery are used for illustrating this technique. As a result of the study, it is concluded that the slope can be a good indicator of the battery health and PF is a useful tool for the reliable prediction of RUL.

OCV Estimation Based on Artificial Neural Network in Lithium-Ion Battery (리튬 이온 배터리의 ANN 기반 OCV 추정 기법 연구)

  • Hong, Seonri;Han, Dongho;Kang, Moses;Baek, Jongbok;Jeong, Hakgeun;Kim, Jonghoon
    • Proceedings of the KIPE Conference
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    • 2019.07a
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    • pp.445-446
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    • 2019
  • 전기적 등가회로의 모델의 정확도 향상을 위하여 정확한 내부 저항과 OCV의 반영은 필수적이며, 이를 위한 OCV 실험에서 SOC 구간을 작게 작을수록 OCV의 정확도는 향상되지만 실험시간은 증가한다. 따라서 실험 시간을 고려한 적당한 SOC(5%, 10%) 구간으로 실험을 진행하며, 측정 되지 않은 영역의 내부 파라미터는 선형보간법으로 등가회로 모델에 반영한다. 이러한 문제로, 본 연구는 SOC 추정에의 주요 인자인 OCV의 추정 기법으로 뉴럴 네트워크(Neural Network)를 사용하였다. 추정 방법은 뉴럴 네트워크로 기존 OCV 실험 데이터를 학습하여 모델을 구축한다. 학습 모델의 입력값으로 용량 실험 데이터의 전압, 전류를 적용하였고 결과로 얻은 SOC-OCV 곡선을 비교 분석하였다.

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Deep Learning Approaches to RUL Prediction of Lithium-ion Batteries (딥러닝을 이용한 리튬이온 배터리 잔여 유효수명 예측)

  • Jung, Sang-Jin;Hur, Jang-Wook
    • Journal of the Korean Society of Manufacturing Process Engineers
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    • v.19 no.12
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    • pp.21-27
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    • 2020
  • Lithium-ion batteries are the heart of energy-storing devices and electric vehicles. Owing to their superior qualities, such as high capacity and energy efficiency, they have become quite popular, resulting in an increased demand for failure/damage prevention and useable life maximization. To prevent failure in Lithium-ion batteries, improve their reliability, and ensure productivity, prognosticative measures such as condition monitoring through sensors, condition assessment for failure detection, and remaining useful life prediction through data-driven prognostics and health management approaches have become important topics for research. In this study, the residual useful life of Lithium-ion batteries was predicted using two efficient artificial recurrent neural networks-ong short-term memory (LSTM) and gated recurrent unit (GRU). The proposed approaches were compared for prognostics accuracy and cost-efficiency. It was determined that LSTM showed slightly higher accuracy, whereas GRUs have a computational advantage.

Comparison of Learning Techniques of LSTM Network for State of Charge Estimation in Lithium-Ion Batteries (리튬 이온 배터리의 충전 상태 추정을 위한 LSTM 네트워크 학습 방법 비교)

  • Hong, Seon-Ri;Kang, Moses;Kim, Gun-Woo;Jeong, Hak-Geun;Beak, Jong-Bok;Kim, Jong-Hoon
    • Journal of IKEEE
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    • v.23 no.4
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    • pp.1328-1336
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    • 2019
  • To maintain the safe and optimal performance of batteries, accurate estimation of state of charge (SOC) is critical. In this paper, Long short-term memory network (LSTM) based on the artificial intelligence algorithm is applied to address the problem of the conventional coulomb-counting method. Different discharge cycles are concatenated to form the dataset for training and verification. In oder to improve the quality of input data for learning, preprocessing was performed. In addition, we compared learning ability and SOC estimation performance according to the structure of LSTM model and hyperparameter setup. The trained model was verified with a UDDS profile and achieved estimated accuracy of RMSE 0.82% and MAX 2.54%.

A Case Study on Operation of Energy Management System Connected with Renewable Energy (신재생에너지 연계형 에너지관리장치의 운영 사례 연구)

  • Cho, Jai Young;Ra, In-Ho
    • Smart Media Journal
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    • v.7 no.2
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    • pp.71-77
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    • 2018
  • This paper proposes the components of the energy management system (EMS) for optimum operation of renewable energy and associated energy storage system (ESS), the functions to be considered in designing, the analysis of operational effects, and finally the reduction of electricity costs. To accomplish the objectives, a lithium-ion battery system and an energy management system have installed in a PV system, and it presents the results analyzed with operation data for a year. To increase the system operation efficiency, we propose the effect that EMS is used to replace the demand power at the peak time with the charge power at the light load time, which suggests the influence of contributing to the charge benefit and load leveling according to the ESS tariff.

State of Health and State of Charge Estimation of Li-ion Battery for Construction Equipment based on Dual Extended Kalman Filter (이중확장칼만필터(DEKF)를 기반한 건설장비용 리튬이온전지의 State of Charge(SOC) 및 State of Health(SOH) 추정)

  • Hong-Ryun Jung;Jun Ho Kim;Seung Woo Kim;Jong Hoon Kim;Eun Jin Kang;Jeong Woo Yun
    • Journal of the Microelectronics and Packaging Society
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    • v.31 no.1
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    • pp.16-22
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    • 2024
  • Along with the high interest in electric vehicles and new renewable energy, there is a growing demand to apply lithium-ion batteries in the construction equipment industry. The capacity of heavy construction equipment that performs various tasks at construction sites is rapidly decreasing. Therefore, it is essential to accurately predict the state of batteries such as SOC (State of Charge) and SOH (State of Health). In this paper, the errors between actual electrochemical measurement data and estimated data were compared using the Dual Extended Kalman Filter (DEKF) algorithm that can estimate SOC and SOH at the same time. The prediction of battery charge state was analyzed by measuring OCV at SOC 5% intervals under 0.2C-rate conditions after the battery cell was fully charged, and the degradation state of the battery was predicted after 50 cycles of aging tests under various C-rate (0.2, 0.3, 0.5, 1.0, 1.5C rate) conditions. It was confirmed that the SOC and SOH estimation errors using DEKF tended to increase as the C-rate increased. It was confirmed that the SOC estimation using DEKF showed less than 6% at 0.2, 0.5, and 1C-rate. In addition, it was confirmed that the SOH estimation results showed good performance within the maximum error of 1.0% and 1.3% at 0.2 and 0.3C-rate, respectively. Also, it was confirmed that the estimation error also increased from 1.5% to 2% as the C-rate increased from 0.5 to 1.5C-rate. However, this result shows that all SOH estimation results using DEKF were excellent within about 2%.

Implementation of a Mobile Sensor Device Capable of Recognizing User Activities (사용자 움직임 인식이 가능한 휴대형 센서 디바이스 구현)

  • Ahn, Jin-Ho;Park, Se-Jun;Hong, Eu-Gene;Kim, Ig-Jae;Kim, Hyoung-Gon
    • Journal of the Institute of Electronics Engineers of Korea SD
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    • v.46 no.10
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    • pp.40-45
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    • 2009
  • In this paper, we introduce a mobile-type tiny sensor device that can classify the activities of daily living based on the state-dependent motion analysis using a 3-axial accelerometer in real-time. The device consists of an accelerometer, GPS module, 32bit micro-controller for sensor data processing and activity classification, and a bluetooth module for wireless data communication. The size of device is 50*47*14(mm) and lasts about 10 hours in operation-mode and 160 hours in stand-by mode. Up to now, the device can recognize three user activities ("Upright", "Running", "Walking") based on the decision tree. This tree is constructed by the pre-learning process to activities of subjects. The accuracy rate of recognizing activities is over 90% for various subjects.