• 한국산업융합학회 논문집
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• 제23권5호
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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.

• Transactions on Electrical and Electronic Materials
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• 제18권3호
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• pp.136-140
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• 2017

The state of charge (SOC) is an important parameter in a battery-management system (BMS), and is very significant for accurately estimating the SOC of a battery. Li-ion batteries boast of excellent performance, and can only remain at their best working state by means of accurate SOC estimation that gives full play to their performances and raises their economic benefits. This paper summarizes some measures taken in SOC estimation, including the discharge experiment method, the ampere-hour integral method, the open circuit voltage method, the Kalman filter method, the neural network method, and electrochemical impedance spectroscopy (EIS. The principles of the various SOC estimation methods are introduced, and their advantages and disadvantages, as well as the working conditions adopted during these methods, are discussed and analyzed.

• 한국수소및신에너지학회논문집
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• 제30권2호
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• pp.155-162
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• 2019

The energy management technology for an independent fuel cell-battery hybrid system is developed for a household usage. To develop an efficient energy management technology, a simulation model is first developed. After the model is verified with experimental results, three energy management schemes are developed. Three control techniques are a fuzzy logic control (FLC), a state machine control (SMC), and a hybrid method of FLC and SMC. As the fuel cell-battery hybrid system is used for a house, battery state of charge (SOC) regulation is the most important factor for an energy management because SOC should be kept constant every day for continuous usage. Three management schemes are compared to see SOC, power split, and fuel cell power variations effects. Experimental results are also presented and the most favorable strategy is the state machine combined fuzzy control method.

• 전력전자학회:학술대회논문집
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• 전력전자학회 2007년도 추계학술대회 논문집
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• pp.60-62
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• 2007

The open circuit voltage (OCV) is widely used to estimate the state of charge (SOC) in many estimation algorithms. However, the relationship between the OCV and SOC can not be exactly same for all batteries. Because the conventional OCV-SOC differs between batteries, there is a problem that the relationship of the OCV-SOC should be measured to accurately estimate the SOC. Therefore, the conventional OCV-SOC is modified to a new relationship in this paper. Thus, problems resulting from the defects of the extended Kalman filter (EKF) can be avoided by preventing the relationship from varying. In this paper, SOC and capacity of the lithium-ion battery are estimated using the dual EKF with the proposed method.

• 한국자동차공학회논문집
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• 제20권1호
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• pp.119-123
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• 2012

In traditional vehicles, a great amount of energy is dissipated by braking. In electric vehicles (EVs), however, electric motors can be controlled to operate as generators to convert kinetic and potential energy of vehicles into electrical energy and store it in batteries. In this paper, the relationship between regenerative braking factor and battery final SOC is derived and the final SOC from the relationship is compared to that from simulation. Two types of braking algorithms are introduced and applied to an EV, and the final SOC derived from simulation is compared to that derived from the relationship.

• 전력전자학회논문지
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• 제9권4호
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• pp.319-325
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• 2004

무공해 자동차를 만들기 위해 중요한 요소로서는 주행거리와 알맞은 가격이다. 니켈-수소 배터리와 같은 향상된 배터리의 개발은 부분적으로 문제를 해결할 수 있으며, 또 하나의 효과적인 방법은 배터리 관리 시스템이나. 니켈-수소 배터리와 배터리 ECU는 주행거리, 가속도, 등판능력과 같은 무공해 자동차의 성능에 영향을 미치는 중요한 구성품이다. 예컨대 운행 중 쉽게 발생하는 단락, 과방전, 과충전은 배터리와 관계되는 가장 큰 문제점이기 때문에 전용의 HEV용 배터리 ECU의 개발은 필수적이다. 본 논문에서는 배터리 전류적산 및 전압에 기초한 HEV용 SOC 알고리즘을 제안하고 배터리 ECU를 설계 및 해석하였으며, 시험을 통해 타당성을 검증하였다.

• Journal of Power Electronics
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• 제17권6호
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• pp.1625-1636
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• 2017

A power electronic transformer (PET) based on the cascaded H-bridge (CHB) and the isolated bidirectional DC/DC converter (IBDC) is capable of accommodating a large scale battery energy storage system (BESS) in the medium-voltage grid, and is referred to as a power electronic transformer based battery energy storage system (PET-BESS). This paper investigates the PET-BESS and proposes a coordinative control strategy for it. In the proposed method, the CHB controls the power flow and the battery state-of-charge (SOC) balancing, while the IBDC maintains the dc-link voltages with feedforward implementation of the power reference and the switch status of the CHB. State-feedback and linear quadratic Riccati (LQR) methods have been adopted in the CHB to control the grid current, active power and reactive power. A hybrid PWM modulating method is utilized to achieve SOC balancing, where battery SOC sorting is involved. The feedforward path of the power reference and the CHB switch status substantially reduces the dc-link voltage fluctuations under dynamic power variations. The effectiveness of the proposed control has been verified both by simulation and experimental results. The performance of the PET-BESS under bidirectional power flow has been improved, and the battery SOC values have been adjusted to converge.

• 전력전자학회:학술대회논문집
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• 전력전자학회 2013년도 추계학술대회 논문집
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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.

• 전력전자학회:학술대회논문집
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• 전력전자학회 2019년도 추계학술대회
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• pp.70-72
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• 2019

For the safe and reliable operation of Lithium-ion batteries in Electric Vehicles (EVs) or Energy Storage Systems (ESSs), it is essential to have accurate information of the battery such as State of Charge (SOC). Many kinds of different techniques to estimate the SOC of the batteries have been developed so far such as the Kalman Filter. However, when it is applied to the multiple number of batteries it is difficult to maintain the accuracy of the estimation over all cells due to the difference in parameter value of each cell. Moreover the difference in the parameter of each cell may become larger as the operation time accumulates due to aging. In this paper a novel Deep Neural Network (DNN) based SOC estimation method for multi cell application is proposed. In the proposed method DNN is implemented to learn non-linear relationship of the voltage and current of the lithium-ion battery at different SOCs and different temperatures. In the training the voltage and current data of the Lithium battery at charge and discharge cycles obtained at different temperatures are used. After the comprehensive training with the data obtained with a cell resulting estimation algorithm is applied to the other cells. The experimental results show that the Mean Absolute Error (MAE) of the estimation is 0.56% at 25℃, and 3.16% at 60℃ with the proposed SOC estimation algorithm.

• 전력전자학회:학술대회논문집
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• 전력전자학회 2013년도 전력전자학술대회 논문집
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• pp.317-318
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• 2013

State-of-Charge (SOC) is one of the most important indicators for the battery management system. Thus its precise estimation is crucial not only for effectively utilizing the energy but also preventing critical situations from happening to the powertrain of the vehicle. However, lead-acid battery is time-variant and highly nonlinear, and the hysteresis phenomenon causes large errors in estimating SOC. This paper proposes a novel SOC estimation technique for the lead-acid battery by using Extended Kalman Filter (EKF) considering hysteresis effect. The validity of the proposed technique is verified through the experiments.