• International Journal of Internet, Broadcasting and Communication
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• v.15 no.3
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• pp.118-121
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• 2023

Recently, green energy support policies have been announced around the world in accordance with environmental regulations, and asthe market grows rapidly, demand for batteries is also increasing. Therefore, various methodologies for battery diagnosis and recycling methods are being discussed, but current accurate life prediction of batteries has limitations due to the nonlinear form according to the internal structure or chemical change of the battery. In this paper, CS2 lithium-ion battery measurement data measured at the A. James Clark School of Engineering, University of Marylan was used to predict battery performance with high accuracy using a convolutional neural network (CNN) model among deep learning-based models. As a result, the battery performance was predicted with high accuracy. A data structure with a matrix of total data 3,931 ☓ 19 was designed as test data for the CS2 battery and checking the result values, the MAE was 0.8451, the RMSE was 1.3448, and the accuracy was 0.984, confirming excellent performance.

• Proceedings of the KIPE Conference
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• 2017.11a
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• pp.167-168
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• 2017

This paper investigates to identify an optimal for analysis of battery behavior in system-level applications such as Battery Energy Storage Systems in Smart Grid infrastructures and Electrical vehicles. At system level applications, it is mandatory to check model for meaningful equivalency and practical ability for extension from unit cell to Battery stack. The investigation of current battery models in relation to their suitability for study and analysis of system level applications of battery helpful for identification of optimal model and it also provides an intuition and direction to develop the most suitable model, if such models are not available already.

• Journal of Electrical Engineering and Technology
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• v.9 no.6
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• pp.2134-2141
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• 2014

This paper addresses on a wind power system with BESS(Battery Energy Storage System). The concerned system consists of four parts: the wind speed production model, the wind turbine model, configure capacity of the battery energy storage, battery model and control of the BESS. First of all, we produce wind speed by 4-component composite wind speed model. Secondly, the maximum available wind power is determined by analyzing the produced wind speed and the characteristic curve of wind power. Thirdly, we configure capacity of the BESS according to wind speed and characteristic curve of wind speed-power. Then, we propose a control strategy to track the power reference. Finally, some simulations have been demonstrated to visualize the feasibility of the proposed methodology.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.58 no.9
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• pp.1714-1719
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• 2009

In this paper, methodology of battery optimal designing is proposed. Fuel cell model including dynamic characteristic is developed and load model is produced by considering driving schedule. Using these models, required energy of load and supplying energy from fuel cell are analyzed by comparing simulation results. Also parameter of fuel cell model is changed variously and battery capacity is calculated in each cases. And methode of battery optimal designing is presented by regarding dynamic characteristic of fuel cell.

• Proceedings of the KIEE Conference
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• summer
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• pp.1525-1527
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• 2004

Modeling of the battery for 42V Power-Net system is presented. For the Battery Management System(BMS) algorithm in a Mildhybrid vehicle, accuracy in SOC estimation is crucial. The battery model is needed for the BMS algorithm as well as system computer symulation for the energy management. The battery model was composed of impedance elements and the each element of the model is estimated by the analysis of the terminal voltage. The result of the model is confirmed by experimental data.

• Journal of Electrical Engineering and Technology
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• v.7 no.6
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• pp.932-939
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• 2012

This paper presents an equivalent circuit model of a LIPB (Li-Ion Polymer battery) for Hybrid Electric Vehicles (HEVs). The proposed equivalent circuit can be used to predict the charging/discharging characteristics in time domain as well as the impedance characteristic analysis in frequency domain. Based on these features, a one-cell model is established as a function of Depth of Discharge (DoD), and a 48-cell model for a battery pack was also established. It was confirmed by experiment that the proposed model predict the discharging and impedance (AC) characteristics quite accurately at different constant current levels. To check the usefulness of the proposed circuit, the model was used to simulate a motor driving circuit with an Insulated Gate Bipolar Transistor (IGBT) inverter and Brushless DC (BLDC) motor, and it is confirmed that the model can calculate the battery voltage fluctuation in time domain at different DoDs.

• Transactions of the Korean Society of Mechanical Engineers B
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• v.38 no.3
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• pp.227-234
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• 2014

Fuel consumption rates of electric vehicles strongly depend on their battery performance. Because the battery performance is sensitive to the operating temperature, temperature management of the battery ensures its performance and durability. In particular, the temperature distribution among modules in the battery pack affects the cooling characteristics. This study focuses on the thermal modeling of a battery pack to observe the temperature distribution among the modules. The battery model is a prismatic model of 10 NiMH battery modules. The thermal model of the battery consists of heat generation, convective heat transfer through the channel and conduction heat transfer among modules. The heat generation is calculated by the electric resistance heat during the charge/discharge state. The model is used to determine a strategy for proper thermal management in Electric vehicles.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.49 no.12
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• pp.989-996
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• 2021

In the case of an unmanned aerial vehicle using a battery as a power source, there are restrictions in performing the mission because the battery capacity is limited. To extend the mission capability, it is important to minimize battery usage while the flight to the mission area. In addition, by using the battery usage prediction model, the possibility of mission completeness can be determined and it can be a criterion for selecting an emergent landing point in the mission planning stage. In this paper, we propose a battery usage prediction model considering as one of the environmental factors in the three-dimensional space. The required power is calculated according to the flight geometry of an unmanned aerial vehicle. True battery usage which is predicted from the required power is verified through the comparison with the battery usage prediction model. The optimal flight trajectory that minimizes battery usage is produced and compared with the shortest travel distance.

• The Transactions of the Korean Institute of Power Electronics
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• v.24 no.1
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• pp.16-24
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• 2019

The performance of the battery management system (BMS) algorithm is important for ensuring the stability and efficient operation of battery packs. Such a performance is determined by the internal parameters of the electrical equivalent circuit model (EECM). This study proposes a performance improvement and verification of battery parameters for the BMS algorithm using electrical experiments and tools. The parameters were extracted through electrical characteristic experiments, and an EECM based on Ah counting was designed. Simulation results using the EECM were compared with actual experimental data to determine the best parameter extraction method.

• Journal of the Korea Convergence Society
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• v.11 no.6
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• pp.7-14
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• 2020

A Battery Monitoring System capable of State-of-Charge(SOC) estimation using the Extended Kalman Filter(EKF) is described in this paper. In order to accurately estimate the SOC of the battery, the battery cells were modeled as the Thevenin equivalent circuit model. The Thevenin model's parameters were measured in experiments. For the Battery Monitoring System, we designed a battery monitoring device that can calculate the SOC estimation using the EKF and a monitoring server that controls multiple battery monitoring devices. We also develop a web-based dashboard for controlling and monitoring batteries. Especially the computation of the monitoring server could be reduced by calculating the battery SOC estimation at each Battery Monitoring Device.