• The Journal of the Korea institute of electronic communication sciences
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• v.15 no.1
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• pp.71-78
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• 2020

In the case of power generation using renewable energy, power production may not be smooth due to the influence of the weather. The energy storage system (ESS) is used to increase the efficiency of solar and wind power generation. ESS has been continuously fired due to a lack of battery protection systems, operation management, and control system, or careless installation, leading to very big casualties and economic losses. ESS stability and battery protection system operation management technology is indispensable. In this paper, we present a battery level calculation algorithm and a failure prediction algorithm for ESS optimization and stable operation. The proposed algorithm calculates the correct battery level by accumulating the current amount in real-time when the battery is charged and discharged, and calculates the battery failure by using the voltage imbalance between battery cells. The proposed algorithms can predict the exact battery level and failure required to operate the ESS optimally. Therefore, accurate status information on ESS battery can be measured and reliably monitored to prevent large accidents.

• Journal of Aerospace System Engineering
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• v.8 no.4
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• pp.7-17
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• 2014

Objective of this paper is to introduce a new technology known as prognostics and health management (PHM) which enables a real-time life prediction for safety critical systems under extreme loading conditions. In the PHM, Bayesian framework is employed to account for uncertainties and probabilities arising in the overall process including condition monitoring, fault severity estimation and failure predictions. Three applications - aircraft fuselage crack, gearbox spall and battery capacity degradation are taken to illustrate the approach, in which the life is predicted and validated by end-of-life results. The PHM technology may allow new maintenance strategy that achieves higher degree of safety while reducing the cost in effective manner.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2015.10a
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• pp.484-487
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• 2015

This paper describes a dual-input battery charger with MPPT control using photovoltaic and piezoelectric energy. Each energy is harvested from photovoltaic cells and piezoelectric cells and is stored to each capacitor. The battery voltage is boosted by charger block and two energy sources are used as input to charge battery capacitor. A DC-DC boost converter is designed to boost the battery voltage, and inductor sharing technique is employed such that only one inductor is required. The time division ratio for piezoelectric cell and photovoltaic cell is set to 8:1. The proposed circuit is designed in a 0.35um CMOS process technology. The condition of battery capacitor is managed by battery management block and the battery voltage can be boosted up to 3V. The maximum efficiency of the designed entire system is 88.56%, and the chip area including pads is $1230um{\times}1330um$.

• Journal of the Korean Electrochemical Society
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• v.2 no.4
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• pp.218-220
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• 1999

The performance of the Electric Vehicle and Hybrid Electric Vehicle depends on that of the battery pack composed of series connected batteries. And thermal property is one of the main factors which decide the performance of the battery pack. So heat generation rate from the battery under the various driving mode must be measured as precise as possible because thermal characteristics of the battery affect the driving performance and battery pack's life cycle. Besides, to design and develop the battery thermal management system for the EV and HEV, the measurements of the thermal properties of the batteries are needed. However, the established calorimeter is not adequate to test an EV's battery because its cavity is too small to accommodate the EV's battery. Therefore we developed the calorimeter to test the thermal property of the EV's battery. Its cavity size is 120mm long, 75mm wide and 200mm high. The calorimeter is calibrated by the dummy cell which generates the heat rate from zero to 200W. The measuring accuracy of the calorimeter is within $2\%$ and its voltage stability is 2.5mV in the constant temperature bath.

• Journal of Electrical Engineering and Technology
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• v.11 no.4
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• pp.839-847
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• 2016

Energy recovery battery testing systems (ERBTS) have been widely used in battery manufactures. All the ERBTS are connected in parallel which forms a special and complicated micro-grid system, which has the shortcomings of low energy recovery efficiency, complex grid-connected control algorithms issues for islanded detection, and complicated power circuit topology issues. To solve those shortcomings, a DC micro-grid system is proposed, the released testing energy has the priority to be reutilized between various testing system within the local grid, Compared to conventional scheme, the proposed system has the merits of a simplified power circuit topology, no needs for synchronous control, and much higher testing efficiency. The testing energy can be cycle-used inside the local micro-grid. The additional energy can be recovered to AC-grid. Numerous experimental comparison results between conventional and proposed scheme are provided to demonstrate the validity and effectiveness of the proposed technique.

• IEMEK Journal of Embedded Systems and Applications
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• v.1 no.1
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• pp.1-7
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• 2006

In this paper, a novel power management structure for an energy harvesting pervasive system is proposed. The system considers the power state of each subsystem to assign proper power sources. The switch matrix structure utilizes each power source to reduce the peak current of the battery. The power management structure can be interfaced to an embedded system power supply without significant design change.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2011.10a
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• pp.776-778
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• 2011

급속충전 기술은 현재 battery의 성능 및 안전성 등 다양한 요인에 따라 동작성능이 좌우되므로 모니터링 시스템에 의한 충전상태 파악은 충전 알고리즘의 개선, 전기자동차 BMS와 연동, 사용자와의 충전 프로세스 제어 등 다양한 작업환경과 연계되어 있다. 급속충전시스템의 동작 상태를 CAN 프로토콜을 이용하여 실시간으로 감시가 가능한 시스템을 설계 제작하여 전기자동차의 battery를 최단시간에 최적화된 상태로 충전 가능하도록 설계하여 급속충전기 실시간 모니터링 시스템을 설계하는 방법을 제안한다.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.16 no.6
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• pp.385-391
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• 2023

As the use of electric vehicles has increased to minimize carbon emissions, the analyzing the state and performance of lithium-ion batteries that is instrumental in electric vehicles have been important. Comprehensive analysis using not only the voltage, current and temperature of the battery pack, which can affect the condition and performance of the battery, but also the driving data and charging pattern data of the electric vehicle is required. Therefore, a thorough analysis is imperative, utilizing electric vehicle operation data, charging pattern data, as well as battery pack voltage, current, and temperature data, which collectively influence the condition and performance of the battery. Therefore, collection and preprocessing of battery data collected from electric vehicles, collection and preprocessing of data on driver driving habits in addition to simple battery data, detailed design and modification of artificial intelligence algorithm based on the analyzed influencing factors, and A battery analysis and evaluation model was designed. In this paper, we gathered operational data and battery data from real-time electric buses. These data sets were then utilized to train a Random Forest algorithm. Furthermore, a comprehensive assessment of battery status, operation, and charging patterns was conducted using the explainable Artificial Intelligence (XAI) algorithm. The study identified crucial influencing factors on battery status, including rapid acceleration, rapid deceleration, sudden stops in driving patterns, the number of drives per day in the charging and discharging pattern, daily accumulated Depth of Discharge (DOD), cell voltage differences during discharge, maximum cell temperature, and minimum cell temperature. These factors were confirmed to significantly impact the battery condition. Based on the identified influencing factors, a battery analysis and evaluation model was designed and assessed using the Random Forest algorithm. The results contribute to the understanding of battery health and lay the foundation for effective battery management in electric vehicles.

• 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 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.