• Journal of Electrical Engineering and Technology
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• v.13 no.4
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• pp.1759-1768
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• 2018

The energy density, power density and ohm resistance of battery change significantly as results of battery aging, which lead to decrease in the accuracy of the equivalent model. A parameter identification method of the equivale6nt circuit model with 3 R-C branches based on the test database of battery life cycle is proposed in this paper. This database is built on the basis of experiments such as updating of available capacity, charging and discharging tests at different rates and relaxation characteristics tests. It can realize regular update and calibration of key parameters like SOH, so as to ensure the reliability of parameters identified. Taking SOH, SOC and T as independent variables, lookup table method is adopted to set initial value for the parameter matrix. Meanwhile, in order to ensure the validity of the model, the least square method based on variable forgetting factor is adopted for optimizing to complete the identification of equivalent model parameters. By comparing the simulation data with measured data for charging and discharging experiments of Li-ion battery, the effectiveness of the full life cycle database and the model are verified.

• KIPS Transactions on Software and Data Engineering
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• v.12 no.3
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• pp.133-140
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• 2023

The performance of lithium ion batteries depends on the usage environment and the combination ratio of cathode materials. In order to develop a high-performance lithium-ion battery, it is necessary to manufacture the battery and measure its performance while varying the cathode material ratio. However, it takes a lot of time and money to directly develop batteries and measure their performance for all combinations of variables. Therefore, research to predict the performance of a battery using an artificial intelligence model has been actively conducted. However, since measurement experiments were conducted with the same battery in the existing published battery data, the cathode material combination ratio was fixed and was not included as a data attribute. In this paper, we define a training data model required to develop an artificial intelligence model that can predict battery performance according to the combination ratio of cathode materials. We analyzed the factors that can affect the performance of lithium-ion batteries and defined the mass of each cathode material and battery usage environment (cycle, current, temperature, time) as input data and the battery power and capacity as target data. In the battery data in different experimental environments, each battery data maintained a unique pattern, and the battery classification model showed that each battery was classified with an error of about 2%.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.42 no.2
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• pp.150-157
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• 2014

Li-Ion battery is used in the most satellites now due to advantages such as weight, thermal dissipation and self discharge compared to the previous generations of electrochemical batteries. The performance analysis model of the Li-Ion battery is needed to aid the design of new satellite electrical power subsystem. This paper develops the performance analysis model of the Li-Ion battery to apply to the electrical power subsystem design and energy balance analysis on geostationary orbit. The analysis model receives the satellite bus power, solar array power and battery temperature and gives the battery voltage, charge and discharge currents, taper index, state of charge and power dissipation. The results from the performance analysis are compared and analyzed with the flight data to verify the model. The compared results show satisfactory without significant difference with the flight data.

• Proceedings of the KIPE Conference
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• 1998.10a
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• pp.554-559
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• 1998

Equalizing the state of charge of cell that affects the charge/discharge quality and efficiency of the battery through the charge/discharge characteristic experiments of battery source, we develope the high efficiency charge/discharge system which would be used in serial HEV with the constant engine-generator output. For this, establishes the electrical model of Ni-MH battery appropriate to the high efficiency charge/discharge conditions. There is no model of Ni-MH cell, so we used Ni-Cd model and obtain the Ni-MH model through the experiment. A reason that each cell has the same charge/discharge property for applying the cell model to serial/parallel connected battery source extensively is needed. Therefore, in this paper, propose the Ni-MH charger/discharger has the equalization charging function and selectable cut-off function.

• Proceedings of the KSME Conference
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• 2002.08a
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• pp.651-654
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• 2002

Study of electric vehicle is popular with automobile company. However, battery cooling problem has delayed development of electric vehicle. Lifetime of electric vehicle's battery depends on the cooling effect for the battery tray. One model was simulated by 3-D, steady state, incompressible, k-e turbulent model simulation. It is found that flow inlet, outlet and inlet position are very important design parameters.

• Journal of Software Engineering Society
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• v.27 no.1
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• pp.1-7
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• 2018

The consumption of mobile device batteries which are limited resources is an important criterion when circuit designers analyze and evaluate circuits. For this reason, researchers conducted researches with different models of battery consumption to analyze power consumption of mobile devices. The battery consumption model generation techniques have various characteristics depending on availability of sensors, run-time model generation, and models for using in verification and testing. However, there is lack of comparison and analysis between varied battery consumption model generation methods. In this research, we compare and evaluate the analysis methods which have been studied so far to support the circuit investigation for circuit designers. Finally, we suggest the direction of researches in battery consumption analysis using the comparison result.

• Journal of Information Processing Systems
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• v.12 no.3
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• pp.436-454
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• 2016

This research presents the battery discharge rate models for the energy consumption of mobile phone batteries based on machine learning by taking into account three usage patterns of the phone: the standby state, video playing, and web browsing. We present the experimental design methodology for collecting data, preprocessing, model construction, and parameter selections. The data is collected based on the HTC One X hardware platform. We considered various setting factors, such as Bluetooth, brightness, 3G, GPS, Wi-Fi, and Sync. The battery levels for each possible state vector were measured, and then we constructed the battery prediction model using different regression functions based on the collected data. The accuracy of the constructed models using the multi-layer perceptron (MLP) and the support vector machine (SVM) were compared using varying kernel functions. Various parameters for MLP and SVM were considered. The measurement of prediction efficiency was done by the mean absolute error (MAE) and the root mean squared error (RMSE). The experiments showed that the MLP with linear regression performs well overall, while the SVM with the polynomial kernel function based on the linear regression gives a low MAE and RMSE. As a result, we were able to demonstrate how to apply the derived model to predict the remaining battery charge.

• Proceedings of the KIEE Conference
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• 1997.07c
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• pp.1035-1037
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• 1997

This paper deals with the mathematical modeling of battery energy storage systems interconnected with the distribution system. This battery model takes account of self-discharge, battery storage capacity, internal resistance and overvoltage. The model components are decided by using an approximation technique and experimental results. This model can be used to evaluate battery performance of battery energy storage systems interconnected with distribution system.

• Journal of Electrical Engineering and Technology
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• v.13 no.3
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• pp.1147-1155
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• 2018

This paper presents an improved method to determine the internal parameters for improving accuracy of a lithium ion battery equivalent circuit model. Conventional methods for the parameter estimation directly using the curve fitting results generate the phenomenon to be incorrect due to the influence of the internal capacitive impedance. To solve this phenomenon, simple correction procedure with transient state analysis is proposed and added to the parameter estimation method. Furthermore, conventional dynamic equation for correction is enhanced with advanced RC impedance dynamic equation so that the proposed modeling results describe the battery dynamic characteristics more exactly. The improved accuracy of the battery model by the proposed modeling method is verified by single cell experiments compared to the other type of models.

• Journal of Intelligence and Information Systems
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• v.24 no.2
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• pp.243-264
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• 2018

Although the worldwide battery market is recently spurring the development of lithium secondary battery, lead acid batteries (rechargeable batteries) which have good-performance and can be reused are consumed in a wide range of industry fields. However, lead-acid batteries have a serious problem in that deterioration of a battery makes progress quickly in the presence of that degradation of only one cell among several cells which is packed in a battery begins. To overcome this problem, previous researches have attempted to identify the mechanism of deterioration of a battery in many ways. However, most of previous researches have used data obtained in a laboratory to analyze the mechanism of deterioration of a battery but not used data obtained in a real world. The usage of real data can increase the feasibility and the applicability of the findings of a research. Therefore, this study aims to develop a model which predicts the battery deterioration using data obtained in real world. To this end, we collected data which presents change of battery state by attaching sensors enabling to monitor the battery condition in real time to dozens of golf carts operated in the real golf field. As a result, total 16,883 samples were obtained. And then, we developed a model which predicts a precursor phenomenon representing deterioration of a battery by analyzing the data collected from the sensors using machine learning techniques. As initial independent variables, we used 1) inbound time of a cart, 2) outbound time of a cart, 3) duration(from outbound time to charge time), 4) charge amount, 5) used amount, 6) charge efficiency, 7) lowest temperature of battery cell 1 to 6, 8) lowest voltage of battery cell 1 to 6, 9) highest voltage of battery cell 1 to 6, 10) voltage of battery cell 1 to 6 at the beginning of operation, 11) voltage of battery cell 1 to 6 at the end of charge, 12) used amount of battery cell 1 to 6 during operation, 13) used amount of battery during operation(Max-Min), 14) duration of battery use, and 15) highest current during operation. Since the values of the independent variables, lowest temperature of battery cell 1 to 6, lowest voltage of battery cell 1 to 6, highest voltage of battery cell 1 to 6, voltage of battery cell 1 to 6 at the beginning of operation, voltage of battery cell 1 to 6 at the end of charge, and used amount of battery cell 1 to 6 during operation are similar to that of each battery cell, we conducted principal component analysis using verimax orthogonal rotation in order to mitigate the multiple collinearity problem. According to the results, we made new variables by averaging the values of independent variables clustered together, and used them as final independent variables instead of origin variables, thereby reducing the dimension. We used decision tree, logistic regression, Bayesian network as algorithms for building prediction models. And also, we built prediction models using the bagging of each of them, the boosting of each of them, and RandomForest. Experimental results show that the prediction model using the bagging of decision tree yields the best accuracy of 89.3923%. This study has some limitations in that the additional variables which affect the deterioration of battery such as weather (temperature, humidity) and driving habits, did not considered, therefore, we would like to consider the them in the future research. However, the battery deterioration prediction model proposed in the present study is expected to enable effective and efficient management of battery used in the real filed by dramatically and to reduce the cost caused by not detecting battery deterioration accordingly.