• 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%.

• The Transactions of the Korean Institute of Electrical Engineers P
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• v.58 no.2
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• pp.214-220
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• 2009

Battery is one of the emergency power and its reliability is a very important to keep up the minimum of building capabilities in case of interruption of electric power. This paper, a comparison was made between three different types of instrument on 30 valve regulated lead acid(VRLA) TYPE 12[V]/100[AH] batteries, and then their indicated measured values(impedance/conductance) were compared with the measured capacity of the battery. As a result, Measured value of instrument is strongly related to battery's capacity in the same group battery and Impedance/Conductance guide line for diagnosis of lead-acid battery's state of health(SOH) is a different from each battery guoup.

• Proceedings of the KIPE Conference
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• 2018.07a
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• pp.464-465
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• 2018

트램 및 전기자동차와 같은 운송 시스템에 들어가는 배터리팩은 지속적인 진동을 받게 되고 이러한 진동은 SOH(State of Health)를 감소시킨다. 뿐만 아니라 진동으로 인해 배터리팩 내부 셀들 간의 SOH가 불균일해지는 문제점이 있다. SOH의 불균형은 배터리의 수명을 단축시킨다. 본 논문에서는 각 셀 간의 SOH 균형을 위한 Thermal Balancing 기법을 제시한다.

• Smart Media Journal
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• v.12 no.11
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• pp.103-112
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• 2023

Recently, the need to prevent battery fires and accidents has emerged, as the use of lithium-ion batteries has increased. In order to prevent accidents, it is necessary to predict the state of health (SOH) and check the replacement timing of the battery with a lot of degradation. This paper proposes a model for predicting the degradation state of a battery by using four battery degradation indicators: maximum voltage arrival time, current change time, maximum temperature arrival time, and incremental capacity (IC) that can be obtained in the battery charging process, and LSTM using an attention mechanism. The performance of the proposed model was measured using the NASA battery data set, and the predictive performance was improved compared to that of the general LSTM model, especially in the SOH 90-70% section, which is close to the battery replacement cycle.

• The Transactions of the Korean Institute of Power Electronics
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• v.26 no.3
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• pp.183-191
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• 2021

A battery management system (BMS) provides some functions for ensuring safety and reliability that includes algorithms estimating battery states. Given the changes caused by various operating conditions, the state-of-health (SOH), which represents a figure of merit of the battery's ability to store and deliver energy, becomes challenging to estimate. Machine learning methods can be applied to perform accurate SOH estimation. In this study, we propose a Long-Term Recurrent Convolutional Network (LRCN) that combines the Convolutional Neural Network (CNN) and Long Short-term Memory (LSTM) to extract aging characteristics and learn temporal mechanisms. The dataset collected by the battery aging experiments of NASA PCoE is used to train models. The input dataset used part of the charging profile. The accuracy of the proposed model is compared with the CNN and LSTM models using the k-fold cross-validation technique. The proposed model achieves a low RMSE of 2.21%, which shows higher accuracy than others in SOH estimation.

• Proceedings of the KIPE Conference
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• 2019.11a
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• pp.79-81
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• 2019

전기자동차(EV)뿐만 아니라 ESS(Energy Storage System) 등의 사용량이 증가하면서 리튬이온배터리의 중요성은 점점 커지고 있다. 리튬 이온 배터리의 정확한 상태를 추정하는 것은 배터리의 안전하고 신뢰성 있는 작동을 위해 매우 중요하다. 본 논문에서는 AEKF(Adaptive Extended Kalman Filter)를 이용한 배터리 파라미터와 충전상태(SOC, State of Charge)를 추정하고, 이를 활용하여 배터리의 건강상태(SOH, State of Health)를 추정하는 간단한 알고리즘을 제시한다. AEKF에 파라미터 값을 적용하여 SOC를 추정하고, 추정된 SOC값과 전류 적산을 이용하여 SOH를 추정한다. SOC 오차에 따른 SOH 추정 값의 편차는 SOC 연산 간격을 늘리고 가중치 필터를 적용하여 최소화시킴으로써 결과의 정확성을 향상했다. 다양한 자동차의 표준 주행 패턴을 적용한 실험을 통해 제안된 방법을 이용하여 얻어진 SOH 추정 결과는 RMSE(Root Mean Square Error) 1.428% 이내임을 검증하였다.

• Journal of Power Electronics
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• v.16 no.1
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• pp.238-248
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• 2016

The state-of-charge (SOC) and state-of-health (SOH) estimation of batteries play important roles in managing batteries for automotive applications. However, an accurate state estimation of a battery is difficult to achieve because of certain factors, such as measurement noise, highly nonlinear characteristics, strong hysteresis phenomenon, and diffusion effect of batteries. In certain vehicular applications, such as idle stop-start systems (ISSs), significant errors in SOC/SOH estimation may lead to a failure in restarting a combustion engine after the shut-off period of the engine when the vehicle is at rest, such as at a traffic light. In this paper, a dual extended Kalman filter algorithm with a dynamic equivalent circuit model of a lead-acid battery is proposed to deal with this problem. The proposed algorithm adopts a battery model by taking into account the hysteresis phenomenon, diffusion effect, and parameter variations for accurate state estimations of the battery. The validity of the proposed algorithm is verified through experiments by using an absorbed glass mat valve-regulated lead-acid battery and a battery sensor cable for commercial ISS vehicles.

• Proceedings of the KIPE Conference
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• 2019.11a
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• pp.194-195
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• 2019

배터리는 사용 기간과 회수가 증가함에 따라 수명이 점차 감소한다. SOH(State-Of-Health)는 배터리의 초기 상태와 현재 상태를 비교하여 배터리의 수명 상태를 나타내는 지표이며, 이는 배터리를 사용함에 있어서배터리의 현재 충전상태를 나타내는 SOC(State-Of-Charge)와 함께 정확한 추정을 필요로 한다. 본 논문에서는 리튬이온 배터리를 C-rate에 따라 노화시키며 각 C-rate별 SOH 추정 경향성을 분석하였다. 배터리의 SOC와 SOH는 확장 칼만 필터를 병렬적으로 사용하는 이중 확장 칼만 필터를 활용하여 추정한다. 배터리의 노화실험은 완전충전과 완전충전을 반복하는 전류 프로파일을 인가하였으며, 실험은 상온(25℃)에서 실행하였다.

• Proceedings of the KIPE Conference
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• 2018.11a
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• pp.133-134
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• 2018

배터리의 효율적인 사용을 위해 배터리 관리 시스템(BMS)는 중요하다. 그 중 배터리의 잔존 수명을 나타내는 지표인 SOH(State of Health)를 예측하기 위해 본 논문에서는 18650 리튬이온 셀에 전기적 노화 실험(Cycle Life Test)을 적용하였다. 방전 용량 및 저항 변화에 의한 SOH 변화를 인공 신경망(Artificial Neural Network)을 사용하여 예측하도록 설계하고 이에 대한 검증을 수행하였다.

• Proceedings of the Korean Institute of IIIuminating and Electrical Installation Engineers Conference
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• 2008.05a
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• pp.317-320
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• 2008

This paper presents the battery management system(BMS) for the optimum conditions of the lead-Acid battery in UPS. The proposed system controls the over and under currents of battery for protecting and it was applied algorithm for optimum conditions to estimate the State Of Charge(SOC) and State Of Health(SOH) in charge or discharge mode. It approved the performance and the algorithm for the estimation of SOH, through the experiments which using the charge and discharge tester and the field tests.