• Journal of IKEEE
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• v.24 no.4
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• pp.1098-1103
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• 2020

This paper deals with a method for estimating the battery state-of-health(SOH) through electrical experiments and electrochemical modeling of lithium-ion secondary battery. In order to confirm the actual battery SOH through the battery electrical aging experiment, the current integration method was used. The SOH is estimated using the internal resistance value derived from the electrical experiment. Also, in electrochemical modeling, the SOH is estimated through the change of the SEI layer with the increase of the number of cycles. The new SOH is derived by applying weighting factor to the three methods of estimating SOH, including the actual battery SOH.

• Journal of Electrical Engineering and Technology
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• v.11 no.6
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• pp.1802-1811
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• 2016

A novel battery SOH estimation algorithm based on outlier detection has been presented. The Battery state of health (SOH) is one of the most important parameters that describes the usability state of the power battery system. Firstly, a battery system model with lifetime fading characteristic was established, and the battery characteristic parameters were acquired from the lifetime fading process. Then, the outlier detection method based on angular distribution was used to identify the outliers among the battery behaviors. Lastly, the functional relationship between battery SOH and the outlier distribution was obtained by polynomial fitting method. The experimental results show that the algorithm can identify the outliers accurately, and the absolute error between the SOH estimation value and true value is less than 3%.

• The Transactions of the Korean Institute of Power Electronics
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• v.26 no.4
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• pp.241-248
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• 2021

A battery state-of-health (SOH) estimation algorithm using a machine learning-based linear regression method is proposed for estimating battery aging. The proposed algorithm analyzes the change trend of the open-circuit voltage (OCV) curve, which is a parameter related to SOH. At this time, a section with high linearity of the SOH and OCV curves is selected and used for SOH estimation. The SOH of the aged battery is estimated according to the selected interval using a machine learning-based linear regression method. The performance of the proposed battery SOH estimation algorithm is verified through experiments and simulations using battery packs for electric vehicles.

• The Transactions of the Korean Institute of Power Electronics
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• v.11 no.6
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• pp.558-562
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• 2006

Although secondary batteries, called rechargeable batteries, are very important energy elements in modern society, their application is hindered by the typical nonlinear and irreversible characteristics. Precise monitoring of the state of health(SOH) for each battery cell on line is crucial for stable operation and proper management of them. This paper proposes diagnostic method of the SOH for a battery cell on line without interruption on its operation nor bad effect on its life. This paper practically diagnoses on 120 industrial batteries and provides some guide lines to decide whether to exchange or not.

• The Transactions of the Korean Institute of Power Electronics
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• v.24 no.3
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• pp.139-146
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• 2019

As the number of electric vehicles (EVs) in Jejudo Island increases, the secondary use of EV batteries is becoming increasingly mandatory not only in reducing greenhouse gas emissions but also in promoting resource conservation. For the secondary use of EV batteries, their capacity and performance at the end of automotive service should be evaluated properly. In this study, the battery state information from the on-board diagnostics or OBD2 port was acquired in real time while driving three distinct routes in Jejudo Island, and then the battery operating characteristics were assessed with the driving routes. The route with higher altitude led to higher current output, i.e., higher C-rate, which would reportedly deteriorate state of health (SOH) faster. In addition, the SOH obtained from the battery management system (BMS) of a 2017 Kia Soul EV with a mileage of 55,000 km was 100.2%, which was unexpectedly high. This finding was confirmed by the SOH estimation based on the ratio of the current integral to the change in state of charge. The SOH larger than 100% can be attributed to the rated capacity that was lower than the nominal capacity in EV application. Therefore, considering the driving environment and understanding the SOH estimation process will be beneficial and necessary in evaluating the capacity and performance of retired batteries for post-vehicle applications.

• Journal of Sensor Science and Technology
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• v.32 no.6
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• pp.462-469
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• 2023

Electric vehicles (EVs) have recently been in the spotlight and have been rapidly developed to reduce the carbon emission with respect to the transport sector. Most EVs currently employ lithium-ion batteries (LIBs) as power sources because they have a higher energy density and a lower self-discharge than other batteries. However, the LIBs cannot respond to high power demands when the state of health (SOH) falls below 80%. Therefore, the SOH of the LIBs must be accurately measured or estimated. To date, many methods have been studied and proposed for measuring or estimating the SOH. In this paper, representative methods among them are reclassified and introduced.

• Journal of Power Electronics
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• v.12 no.1
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• pp.1-9
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• 2012

This study investigates an application of the Hamming network-dual extended Kalman filter (DEKF) based on pattern recognition for high accuracy state-of-charge (SOC)/capacity estimation and state-of-health (SOH) prediction at various temperatures. The averaged nine discharging/charging voltage-temperature (DCVT) patterns for ten fresh Li-Ion cells at experimental temperatures are measured as representative patterns, together with cell model parameters. Through statistical analysis, the Hamming network is applied to identify the representative pattern that matches most closely with the pattern of an arbitrary cell measured at any temperature. Based on temperature-checking process, model parameters for a representative DCVT pattern can then be applied to estimate SOC/capacity and to predict SOH of an arbitrary cell using the DEKF. This avoids the need for repeated parameter measuremet.

• 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.

• Proceedings of the KIPE Conference
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• 2013.07a
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• pp.447-448
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• 2013

This paper investigates a new approach based on the fuzzy-logic controlled methodology that is suitable for analyzing and evaluating large format $LiMn_2O_4$ cell performance via online state-of-health (SOH) estimation for energy storage system (ESS) applications. First of all, the values of the cell resistance R and maximum cell capacity $Q_{max}$ are calculated from three factors such as voltage, current, and time that were measured by discharging/charging sequence. Then, using two values R and $Q_{max}$ previously calculated, present SOH of an arbitrary $LiMn_2O_4$ cell can be estimated using the defined fuzzy-logic inference system. The main advantage of this approach is wide parameters tuning possibility for good correspondence of SOH decay with other accurate estimation method and the possibility to perform suitable online SOH estimation.

• Proceedings of the KIPE Conference
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• 2006.06a
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• pp.266-269
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• 2006

현대사회에서 축전지라 불리우는 2차 전지는 그 용도가 중요하지만, 비선형적이고 다양한 파라메타에 따른 복잡한 특성 때문에 그 사용법에 있어서 발전에 제한을 받아왔다 [1][2]. 각 배터리셀의 건강상태(SOH)를 실시간으로 정확히 파악하는 것은 장비의 안정된 운전과 원활한 축전지관리를 위하여 필수적이다. 본 논문에서는 축전지의 내부컨덕턴스를 측정하는 간접적인 방법에 의하여 장비의 운전이나 축전지의 수명에 영향을 주지 않고 축전지의 건강상태(SOH)를 실시간으로 진단하는 방법을 제시하고, 실제로 120개의 축전지에 대한 컨덕턴스 자료에 의하여 건강상태를 진단하고 교체시기를 판단한다.