• Journal of the Semiconductor & Display Technology
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• v.15 no.2
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• pp.16-19
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• 2016

More efficient use of the battery pack is dependent upon how to measure the remaining capacity of the battery accurately. Among various measurement methods, the basic correction measurement method has still been a hot research topic area to reduce the errors. In this paper, the problems of the existing methods have been investigated and the research direction for measuring more accurate remaining capacity has been suggested by applying the numerical simulations in the future.

• International Journal of Advanced Culture Technology
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• v.11 no.3
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• pp.310-314
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• 2023

The purpose of this study is to predict the remaining capacity of lithium-ion batteries and evaluate their performance using five artificial intelligence models, including linear regression analysis, decision tree, random forest, neural network, and ensemble model. We is in the study, measured Excel data from the CS2 lithium-ion battery was used, and the prediction accuracy of the model was measured using evaluation indicators such as mean square error, mean absolute error, coefficient of determination, and root mean square error. As a result of this study, the Root Mean Square Error(RMSE) of the linear regression model was 0.045, the decision tree model was 0.038, the random forest model was 0.034, the neural network model was 0.032, and the ensemble model was 0.030. The ensemble model had the best prediction performance, with the neural network model taking second place. The decision tree model and random forest model also performed quite well, and the linear regression model showed poor prediction performance compared to other models. Therefore, through this study, ensemble models and neural network models are most suitable for predicting the remaining capacity of lithium-ion batteries, and decision tree and random forest models also showed good performance. Linear regression models showed relatively poor predictive performance. Therefore, it was concluded that it is appropriate to prioritize ensemble models and neural network models in order to improve the efficiency of battery management and energy systems.

• 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 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 Journal of the Korea Contents Association
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• v.10 no.4
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• pp.55-65
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• 2010

We present a method for increasing network lifetime without link failure due to lack of battery capacity of nodes in wireless ad-hoc networks with low mobility. In general, a node with larger remaining battery capacity represents the one with lesser traffic load. Thus, a modified AODV routing protocol is proposed to determine a possible route by considering a remaining battery capacity of a node. Besides, the total energy consumption of all nodes increase rapidly due to the huge amount of control packets which should be flooded into the network. To reduce such control packets efficiently, a source node can store information about alternative routes to the destination node into its routing table. When a link failure happens, the source node should retrieve the route first with the largest amount of the total remaining battery capacity from its table entries before initiating the route rediscovery process. To do so, the possibility of generating unnecessary AODV control packets should be reduced. The method proposed in this paper increases the network lifetime by 40% at most compared with the legacy AODV and MMBCR.

• Proceedings of the KIEE Conference
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• 2001.07d
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• pp.2187-2189
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• 2001

The proposed algorithm has produced the rules of relationship between the load voltage, current, discharging electric power and ampere-hours, electric power capacity of battery on the basis of the data. Which were acquired through the battery discharging experiment that is defined by the battery's ambient temperature and various load conditions. Especially, by calculating the parameter of second order polynomial equation relation between the remaining capacity and the electric power, the algorithm is proposed adapting for the discharging pattern. And as the depth of discharging is increasing, the calculation-method of electric power is applied to decrease the accumulated error in the calculation method of capacity accumulation. Also, the proposed algorithm has compensated the temperature considering the capacity change of battery to the temperature.

• Journal of the Korean Society of Industry Convergence
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• v.7 no.4
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• pp.411-417
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• 2004

Recently, researchers and engineers from, the development of reliability engineering and probability fracture mechanics, have begun to take seriously the reliability analysis and the integrity for a corroded pipeline. Pressurized pipelines containing active corrosion defects increase gradually both in extent, and depth with increased periods of exposure. This causes a reduction of the remaining strength and the carrying capacity of a pipeline; and creates uncertainty about the future capacity. The steps that are necessary in order to assess the integrity of corroded pipelines will be discussed in this paper utilizing results from an actual model.

• Journal of Korean Society of Steel Construction
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• v.13 no.5
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• pp.467-476
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• 2001

The construction of highway bridges is almost complete in many countries including the United States. The government and highway agencies change the focus from constructing to maintaining To maintain the bridges effectively there is an urgent need to assess actual bridge loading carrying capacity and to predict their remaining life. The system reliability techniques have to be used for this purpose. Based on lifetime distribution (function) techniques this study illustrates how typical highway bridges can be modeled to predict their remaining life. The parameters of lifetime distribution are generated by Monte. The results can be used for optimization of planning interventions on existing bridges.

• The Transactions of the Korean Institute of Power Electronics
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• v.26 no.3
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• pp.167-175
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• 2021

Reusing electric vehicle batteries after they have been retired from mobile applications is considered a feasible solution to reduce the demand for new material and electric vehicle costs. However, the evaluation of the value and the performance of second-life batteries remain a problem that should be solved for the successful application of such batteries. The present work aims to estimate the remaining useful life of Li-ion batteries through the neuro-fuzzy system with the equivalent circuit parameters obtained by Electrochemical Impedance Spectroscopy (EIS). To obtain the impedance spectra of the Li-ion battery over the life, a 18650 cylindrical cell has been aged by 1035 charge/discharge cycles. Moreover, the capacity and the parameters of the equivalent circuit of a Li-ion battery have been recorded. Then, the data are used to establish a neuro-fuzzy system to estimate the remaining useful life of the battery. The experimental results show that the developed algorithm can estimate the remaining capacity of the battery with an RMSE error of 0.841%.

• 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.