• Journal of IKEEE
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• v.18 no.3
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• pp.298-304
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• 2014

Proper operation of the battery powered systems depends on the accuracy of the battery SOC(State of Charge) estimation, therefore it is critical for those systems that SOC is accurately determined. The SOC of the battery is related to the battery aging and the SOC estimation methods without considering the aging of the battery are not accurate. In this paper, a new method that accurately estimate the SOC of the battery is proposed considering the aging of the battery. A mathematical model for the Battery SOC-OCV(Open Circuit Voltage) relationship is presented using Boltzmann equation and aging indicator is defined, and then the SOC is estimated combining the mathematical model and aging indicator. The proposed method takes the aging of the battery into consideration, which leads to an accurate estimation of the SOC. The simulations and experiments show the effectiveness of the proposed method for improving the accuracy of the SOC estimation.

• The Transactions of the Korean Institute of Power Electronics
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• v.25 no.3
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• pp.171-180
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• 2020

This paper presents the implementation and control methods of a battery simulator. The proposed battery simulator can emulate the dynamic characteristics of any actual battery using the second RC ladder model of the equivalent circuit. Moreover, it can emulate the variation of impedance, which is the result of the change of battery characteristics due to the aging effect. The parameters of the battery simulator can be derived from the sequence of tests of the actual battery or only from the data supplied by the battery manufacturer. Proposed methods for the battery simulator are tested by extensive experiments. Test results show that the proposed battery simulator can emulate not only the dynamic characteristics but also the aging effects of the actual battery in real time.

• Journal of the Korean Society of Industry Convergence
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• v.23 no.5
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• pp.853-863
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• 2020

As the battery ages, the internal resistance of the battery increases, so the loss due to the internal resistance increases at the same charging current, causing the battery temperature to rise, which further accelerates battery aging. Therefore, it is necessary to optimize the charging conditions according to the aging of the battery or the current charge amount, and to accurately estimate this, estimation of the parameters of the equivalent circuit is most important. This paper proposes a new measurement technique that can measure the internal resistance of a battery by analyzing a specific high frequency voltage and current applied to the battery. In addition, in order to test the validity of the proposed measurement technique, the current charging amount was estimated based on the measured internal resistance, and the terminal voltage of the constant current charging mode was automatically set and operated. As a result, good results were obtained regardless of the battery voltage. If this equipment is installed in the charging device, it is believed that it will be of great help in the stability management of the aging reusable battery.

• International Journal of Internet, Broadcasting and Communication
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• v.14 no.2
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• pp.129-135
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• 2022

Currently, the rapid growth of electric vehicles and the collection and disposal of waste batteries are becoming a social problem. The purpose of this paper is to propose a fast and efficient battery screening method through a safe inspection and storage method according to the collection and storage of waste batteries of electric vehicles. In addition, as the resistance inside the waste battery increases, an instantaneous voltage drop occurs, and there is a risk of overcharging and overdischarging compared to the initial state of the battery. Accordingly, there are great difficulties in operation, so the final goal of this study is to develop a device for diagnosing aging through real-time battery internal resistance measurement. Final result As a result of simulation of the internal resistance measurement test circuit through external impedance (AC), the actual simulation value was 0.05Ω, RS = Vrms / Irms => Vrms = 8.0036mV, Irms = 162.83Ma. Substitute the suggested method. The result was calculated as Rs = 0.0495Ω. It is possible to measure up to 64 impedances inside the aging diagnostic equipment that enables real-time monitoring of the developed battery cells, and the range can be changed according to the application method.

• Korean Chemical Engineering Research
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• v.56 no.6
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• pp.779-783
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• 2018

In this work, Mathematical modeling was carried-out to predict the charging/discharging characteristics of VRLA (Valve regulated lead acid) battery, which is mainly used as a 12 V lead acid battery for automobile. And It also carried-out how it's characteristics would be changed due to aging. A mathematical modeling technique, which has been mainly used to predict behavior of Lithium-ion batteries, is applied to commercial 70 Ah VRLA battery. The modeling result of Voltage was compared with result of constant current charge / discharge test. From this, it can be seen that the NTGK model can be applied to the lead acid battery with high accuracy. It was also found that the aging of lead-acid battery can be predicted by using it.

• Proceedings of the KIPE Conference
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• 2013.11a
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• pp.200-201
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• 2013

This paper investigates a new approach for Li-Ion battery state-of-charge (SOC) estimation using sliding mode observer (SMO) technique including parameters aging compensation via recursive least squares (RLS). The main advantages of this approach would be low computational load, easiness of implementation along with the robustness of the method for internal battery model parameters estimation. The proposed algorithm was first tested on a set of acquired battery data using implementation in Simulink and later developed as C-code module for firmware application.

• Journal of the Korean Vacuum Society
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• v.16 no.3
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• pp.227-230
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• 2007

In the Li ion battery fabrication process, an aging step has treated as a miner step because there is not so much data to define the relationship between the phenomena generated in aging process and the battery performances. However, the OCV(open circuit voltage) change in the aging process is shown by the electrochemical absorption of the electrolyte component to the both electrodes(anode or cathode) and the absorbed layer to the electrode affects to form the solid electrolyte interface(SEI) layer during the first charge process. In this report, the adsorbed materials are designed deliberately and are cleared to affect to the SEI layer formation.

• 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.

• The Transactions of the Korean Institute of Power Electronics
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• v.26 no.3
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• pp.183-191
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• 2021

A battery management system (BMS) provides some functions for ensuring safety and reliability that includes algorithms estimating battery states. Given the changes caused by various operating conditions, the state-of-health (SOH), which represents a figure of merit of the battery's ability to store and deliver energy, becomes challenging to estimate. Machine learning methods can be applied to perform accurate SOH estimation. In this study, we propose a Long-Term Recurrent Convolutional Network (LRCN) that combines the Convolutional Neural Network (CNN) and Long Short-term Memory (LSTM) to extract aging characteristics and learn temporal mechanisms. The dataset collected by the battery aging experiments of NASA PCoE is used to train models. The input dataset used part of the charging profile. The accuracy of the proposed model is compared with the CNN and LSTM models using the k-fold cross-validation technique. The proposed model achieves a low RMSE of 2.21%, which shows higher accuracy than others in SOH estimation.

• Smart Media Journal
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• v.11 no.11
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• pp.63-74
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• 2022

Nowadays, lithium-ion battery has become more popular around the world. Knowing when batteries reach their end of life (EOL) is crucial. Accurately predicting the remaining useful life (RUL) of lithium-ion batteries is needed for battery health management systems and to avoid unexpected accidents. It gives information about the battery status and when we should replace the battery. With the rapid growth of machine learning and deep learning, data-driven approaches are proposed to address this problem. Extracting aging information from battery charge/discharge records, including voltage, current, and temperature, can determine the battery state and predict battery RUL. In this work, we first outlined the charging and discharging processes of lithium-ion batteries. We then summarize the proposed techniques and achievements in all published data-driven RUL prediction studies. From that, we give a discussion about the accomplishments and remaining works with the corresponding challenges in order to provide a direction for further research in this area.