• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.4
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• pp.536-545
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• 2020

When general statistical regression methods are applied to predict the battery remaining time of a mobile smart device, they yielded the poor accuracy of estimating battery remaining time as the deviations of battery usage time per battery level became larger. In order to improve the estimation accuracy of general statistical regression methods, a preprocessing task is required to refine the measured raw data with large deviations of battery usage time per battery level. In this paper, we propose a data preprocessing framework that preprocesses raw measured battery consumption data and converts them into refined battery consumption data. The numerical results obtained by experimenting the proposed data preprocessing framework confirmed that it yielded good performance in terms of accuracy of estimating battery remaining time under general statistical regression methods for given refined battery consumption data.

• Journal of the Semiconductor & Display Technology
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• v.10 no.4
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• pp.25-29
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• 2011

Emerging demands for rechargeable battery for various applications needs more effective battery management system such as the prediction of the usable time about a battery. Many prediction methods have been suggested but none of them come into bounds of reliability. In this paper, we proposed a new prediction algorithm for the remaining capacity of a rechargeable battery by using the transformed curve based on its impedance. Hardware for monitoring a battery was designed and made. Through a series of experiment, we showed the effectiveness of the proposed prediction algorithm of a battery's remaining capacity.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.7
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• pp.1078-1090
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• 2022

The distribution patterns of battery usage time data per battery level are able to affect the performance of estimating battery remaining time in mobile devices. Outliers may mainly affect the estimation performance of statistical regression methods. In this paper, we propose a software framework that detects and processes outliers to improve the estimation performance of statistical regression methods. The proposed framework first detects outliers that degrade the estimation performance. The proposed framework replaces outliers with smoothed data. The difference between an outlier and its replaced data will be properly distributed into individual data. Finally, individual data are reinforced to improve the estimation performance. The numerical results obtained by experimenting the proposed framework confirmed that it yielded good performance of estimating battery remaining time.

• Journal of the Korean Institute of Electrical and Electronic Material Engineers
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• v.16 no.11
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• pp.1047-1054
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• 2003

Smart battery pack (SBP) for notebook PCs was developed using a cylindrical-type lithium ion battery. Batteries were connected with three serial and two parallel, the nominal capacity and the maximum load of SBP was 4,000mAh and 4.0A, respectively. The SBP was composed of a protection IC, by which safety of lithium ion batteries is maintained against overcharge, overdischarge and overcurrent, and a smart IC, which calculates the remaining capacity and the remaining run time. In matching test on notebook PC using Battery Mark 4.0, real and smart data of END voltage coincided nearly and LB and LLB signal worked norma]]y. And there were errors of less than 1% between the real and the smart data on the residual capacity in the charge and discharge test.

• Journal of Platform Technology
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• v.11 no.2
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• pp.54-65
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• 2023

Due to the nature of electric vehicles, the batteries used for electric vehicles have a very large rated capacity. If an electric vehicle runs for a long time or an electric vehicle is abandoned due to a traffic accident, the electric vehicle battery becomes a waste battery. Even in vehicles that are being abandoned, the remaining capacity of waste batteries for electric vehicles is sufficient for other purposes. Waste batteries for automobiles are very expensive, so they need to be recycled and reused, but there was a problem that the standards for measuring the performance grade of waste batteries for recycling and reuse were insufficient. As a method for measuring the remaining capacity of waste battery, the most stable and reliable method is to measure the remaining capacity of battery using full charge and discharge. However, the inspection method by the full charging and discharging method varies depending on the capacity of the battery, but it takes more than a day to inspect, and many people are making great efforts to solve this problem. In this paper, an electric vehicle battery residual capacity analysis technique using voltage deviation between cells was studied and analyzed as a method to reduce inspection time for electric vehicle batteries. To this end, a full charging and discharging-based capacity measurement system was constructed, experimental data were collected using a nose or waste battery, and the correlation between the voltage deviation and the remaining capacity of the battery pack was analyzed to verify whether it can be used for battery inspection.

• The Transactions of the Korean Institute of Power Electronics
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• v.27 no.1
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• pp.48-55
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• 2022

This paper proposes a battery remaining useful life (RUL) prediction method using a deep learning-based EMD-CNN-LSTM hybrid method. The proposed method pre-processes capacity data by applying empirical mode decomposition (EMD) and predicts the remaining useful life using CNN-LSTM. CNN-LSTM is a hybrid method that combines convolution neural network (CNN), which analyzes spatial features, and long short term memory (LSTM), which is a deep learning technique that processes time series data analysis. The performance of the proposed remaining useful life prediction method is verified using the battery aging experiment data provided by the NASA Ames Prognostics Center of Excellence and shows higher accuracy than does the conventional method.

• Journal of the Korean Electrochemical Society
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• v.4 no.3
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• pp.132-137
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• 2001

Recently, the demand for notebook PC with lithium ion batteries has steadily increased and consumers require them to adopt a SBP(smart battery pack) able to predict the remaining capacity and the run time of batteries precisely. The SBP is composed of a protection If, by which safety of lithium ion batteries is maintained against overcharge, overdischarge and overcurrent, and a smart IC, which calculates the remaining capacity and the remaining run time. The protection IC shut abmormal current down by using overcharge/overdischarge FET. A SBS(smart battery system) is composed of a system host, a smart battery and a smart battery charger. The smart ICs for SBP will be required to provide a low cost, low current consumption and small size. There will need to develop a microcomputer control type IC and an optimum algorism which is able to predict the residual capacity and the residual run time precisely. SBS will apply to many kinds of industry fields such as an electric bicycle, an electric vehicle, a load levelling and a military.

• Journal of Power Electronics
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• v.17 no.5
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• pp.1288-1297
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• 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 Korean Society of Industrial and Systems Engineering
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• v.37 no.2
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• pp.70-76
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• 2014

In industrial situation, electronic and electro-mechanical systems have been using different type of batteries in rapidly increasing numbers. These systems commonly require high reliability for long periods of time. Wider application of battery for low-power design as a prime power source requires us knowledge of failure mechanism and reliability of batteries in terms of load condition, environment condition and other explanatory variables. Battery life is an important factor that affects the reliability of such systems. There is need for us to understand the mechanism leading to the failure state of battery with performance characteristic and develop a method to predict the life of such battery. The purpose of this paper is to develope the methodology of monitoring the health of battery and determining the condition or fate of such systems through the performance reliability to predict the remaining useful life of primary battery with load condition, operating condition, environment change in light of battery life variation. In order to evaluate on-going performance of systems and subsystems adopting primary batteries as energy source, The primitive prototype for performance reliability analysis device was developed and related framework explained.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.65 no.1
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• pp.150-157
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• 2016

In this paper, a control system for E-bike based on IOT was developed, which collects and monitors information of states of E-bike and surrounding environments from several sensors and control devices in E-bike, and informs the possible dangers to rider when riding the E-bike. Developed electronic control system can manage battery efficiently, obtain battery's remaining power in real-time and provide possible riding distance to rider. It makes possible for rider to schedule near optimal riding route in terms of battery usage and respond quickly to battery discharge. Results of applying developed system to E-bike show that according to driving-mode, possible driving distance can be calculated efficiently and using user application App, real-time driver position marking and driving route searching functions lead to energy efficient E-bike driving. Later we will endeavor to integrate BMS, ECU, smart-phone and PC(server) to provide stable driving system based on various driving information of E-bike.