• Journal of applied mathematics & informatics
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• v.39 no.5_6
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• pp.851-858
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• 2021

In general, secondary batteries are widely used as an electric energy source. Among them, the state of energy storage of mobile devices is very important information. As a method of estimating a state, there is a method of estimating the state by integrating the current according to an energy storage state of a battery, and a method of designing a state estimator by measuring a voltage and estimating a charge amount based on a battery model. In this study, we designed the state estimator using an extended Kalman filter to increase the precision of the state estimation of the charge amount by including the error of the system model and having the robustness to the noise.

• The Transactions of the Korean Institute of Power Electronics
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• v.24 no.4
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• pp.286-293
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• 2019

This study compares and analyzes online update techniques, which estimate the parameters of battery equivalent circuit models in real time. Online update techniques, which are based on extended Kalman filter and recursive least square methods, are constructed by considering the dynamic characteristics of batteries. The performance of the online update techniques is verified by simulation and experiments. Each online update technique is compared and analyzed in terms of complexity and accuracy to propose a suitable guide for selecting algorithms on various types of battery applications.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.10a
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• pp.168-169
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• 2022

As the electric vehicle market has grown rapidly, the battery market has grown exponentially. Due to the gap between the generation speed of quality control technology and battery mass production speed for batteries mounted on electric vehicles, many durability problems have arisen for batteries. Most accidents are caused by electrical factors, but there is no technology to quickly inspect them. In this paper, we are going to propose a quick analysis of battery cell defects using the GAN model.

• Journal of Energy Engineering
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• v.22 no.4
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• pp.338-346
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• 2013

The conventional lead acid battery is optimized for cranking performance of engine. Recently electric devices and fuel economy technologies of battery have influenced more deep cycle of dynamic behavior of battery. I also causes to reduce battery life-time. This study proposed that aging battery model is focused for increasing of battery durability. The stress factors of battery aging consist of discharge rate, charging time, full charging time and temperature. This paper considers the electrochemical kinetics, the ionic species conservation, and electrode porosity. For prediction of battery life cycle we consider battery model containing strong impacts, corrosion of positive grid and shedding. Finally, we validated that modeling results were compared with the accelerated thermal measurement data.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.12 no.12
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• pp.5443-5449
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• 2011

Battery post clamp has the role to fix each of terminals at electric condenser by connecting with the cable of power source. In this study, optimum design was achieved by reducing the material cost and the weight of vehicle with one part of battery post clamp. Stress and displacement were obtained by optimizing with design variables. The advanced model by the design through this study were compared with the original model. These optimum values can be applied usefully with the manufacturing field of battery component.

• Journal of the Korean Operations Research and Management Science Society
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• v.18 no.1
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• pp.55-69
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• 1993

The tests in a battery can be performed in different sequences, and different sequential testing strategies will have exactly the same overall performances for that battery, but at different expected total costs and expected time consumptions. By using a multiobjective dynamic programming model, we are able to find all noninferior testing strategies for a given battery of tests and a stopping rule.

• Journal of Power Electronics
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• v.12 no.1
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• pp.113-119
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• 2012

This paper describes the design and control of a phase shift full bridge converter with a current doubler, which can be used for the on-board charger for the lead-acid battery of electric forklifts. Unlike the common resistance load, the battery has a large capacitance element and it absorbs the entire converter output ripple current, thereby shortening the battery life and degrading the system efficiency. In this paper a phase shift full bridge converter with a current doubler has been adopted to decrease the output ripple current and the transformer rating of the charger. The charge controller is designed by using the small signal model of the converter, taking into consideration the internal impedance of the battery. The stability and performance of the battery charger is then verified by constant current (CC) and constant voltage (CV) charge experiments using a lead-acid battery bank for an electric forklift.

• Journal of Electrical Engineering and Technology
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• v.11 no.6
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• pp.1802-1811
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• 2016

A novel battery SOH estimation algorithm based on outlier detection has been presented. The Battery state of health (SOH) is one of the most important parameters that describes the usability state of the power battery system. Firstly, a battery system model with lifetime fading characteristic was established, and the battery characteristic parameters were acquired from the lifetime fading process. Then, the outlier detection method based on angular distribution was used to identify the outliers among the battery behaviors. Lastly, the functional relationship between battery SOH and the outlier distribution was obtained by polynomial fitting method. The experimental results show that the algorithm can identify the outliers accurately, and the absolute error between the SOH estimation value and true value is less than 3%.

• Journal of Institute of Control, Robotics and Systems
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• v.14 no.9
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• pp.904-908
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• 2008

Every autonomous system like a robot needs a power source known as a battery. And proper management of the battery is very important for proper operation. To know State of Charge(SOC) of a battery is the very core of proper battery management. In this paper, the SOC estimation problem is tackled based on the well known Extended Kalman Filter(EKF). Combined the existing battery model is used and then EKF is employed to estimate the SOC. SOC table is constructed by extensive experiment under various conditions and used as a true SOC. To verify the estimation result, extensive experiment is performed with various loads. The comparison result shows the battery estimation problem can be well solved with the technique proposed in this paper. The result of this paper can be used to develop related autonomous system.

• 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.