• 전기전자학회논문지
• /
• 제18권3호
• /
• pp.298-304
• /
• 2014

배터리를 사용하고 있는 시스템에서 배터리의 잔존 용량에 대한 정보는 매우 중요하며, 따라서 정확한 SOC(State of Charge)의 추정이 필요하다. 배터리는 노화됨에 따라 전체 사용 가능 용량이 줄어들고 성능이 떨어지는데 이러한 노화의 영향을 고려하지 않는 배터리의 SOC 추정 방법은 추정의 정확도가 떨어지는 단점이 있다. 따라서 본 논문에서는 배터리의 노화 상태를 고려하여 배터리의 SOC를 추정하는 새로운 방법을 제안한다. 제안한 방법에서는 배터리의 전압-SOC 특성 곡선을 Boltzmann 방정식을 사용하여 모델링하고 노화 지표를 정의하며, 노화 지표를 Boltzmann 방정식 모델과 결합하여 SOC를 추정한다. 따라서 제안한 방법은 배터리의 노화 상태를 SOC 추정에 반영하여 노화된 배터리에 대한 정확한 SOC 추정이 가능하다. 또한 새 배터리와 1년 사용한 배터리에 대한 실험과 시뮬레이션을 통하여 제안한 방법의 유효성을 확인한다.

• IEIE Transactions on Smart Processing and Computing
• /
• 제4권2호
• /
• pp.126-131
• /
• 2015

When a battery is discharging, the battery's current and terminal voltage must both be measured to estimate its state of charge (SOC). If the SOC can be estimated by using only the current or voltage, hardware costs will decrease. This paper proposes an SOC estimation algorithm that needs to measure only the terminal voltage while a battery is discharging. The battery's SOC can be deduced from its open circuit voltage (OCV) through the relationship between SOC and OCV. But when the battery is discharging, it is not possible to measure the OCV due to the voltage drop in the battery's internal resistance (IRdrop). The proposed algorithm calculates OCV by estimating IRdrop using a dynamic terminal voltage measurement. This paper confirms the results of applying the algorithm in a hardware environment via algorithm binarization. To evaluate the algorithm, a Simulink battery model based on actual values was used.

• 전력전자학회논문지
• /
• 제20권4호
• /
• pp.344-350
• /
• 2015

A battery management system requires accurate information on the battery state of charge (SOC) to achieve efficient energy management of electric vehicle and renewable energy systems. Although correct SOC estimation is difficult because of the changes in the electrical characteristics of the battery attributed to ambient temperature, service life, and operating point, various methods for accurate SOC estimation have been reported. On the basis of piecewise linear (PWL) modeling technique, this paper proposes a simple SOC observer for lithium-polymer batteries. For performance evaluation, the SOC estimated by the PWL SOC observer, the SOC measured by the battery-discharging experiment and the SOC estimated by the extended Kalman filter (EKF) estimator were compared through a PSIM simulation study.

• 전력전자학회:학술대회논문집
• /
• 전력전자학회 2016년도 추계학술대회 논문집
• /
• pp.33-34
• /
• 2016

This paper presents a load sharing method based on the low bandwidth communication (LBC) applied to a DC microgrid in order to balance the state of charge (SOC) of the battery units connected in parallel to the common bus. In this method, SOC of each battery unit is transferred to each other through LBC to calculate average SOC value. After that, droop coefficients of battery units are adjusted according to the difference between SOC of each unit and average SOC value of all batteries in the system. The proposed method can effectively balance the SOC of battery units in charging and discharging duration with a simple low bandwidth communication system.

• Journal of Power Electronics
• /
• 제14권5호
• /
• pp.1038-1046
• /
• 2014

Lithium-ion batteries are widely used in hybrid and pure electric vehicles. State-of-charge (SOC) estimation is a fundamental issue in vehicle power train control and battery management systems. This study proposes a novel model-based SOC estimation method that applies closed-loop state observer theory and a comprehensive battery model. The state-space model of lithium-ion battery is developed based on a three-order resistor-capacitor equivalent circuit model. The least square algorithm is used to identify model parameters. A multi-state closed-loop state observer is designed to predict the open-circuit voltage (OCV) of a battery based on the battery state-space model. Battery SOC can then be estimated based on the corresponding relationship between battery OCV and SOC. Finally, practical driving tests that use two types of typical driving cycle are performed to verify the proposed SOC estimation method. Test results prove that the proposed estimation method is reasonably accurate and exhibits accuracy in estimating SOC within 2% under different driving cycles.

• 제어로봇시스템학회논문지
• /
• 제14권9호
• /
• pp.904-908
• /
• 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.

• 전력전자학회논문지
• /
• 제20권2호
• /
• pp.175-181
• /
• 2015

In this study, a lithium polymer battery (LiPB) is simply expressed by a primary RC equivalent model. The PI state observer is designed in Matlab/Simulink. The non-linear relationship with the OCV-SOC is represented to be linearized with 0.1 pu intervals by using battery parameters obtained by constant-current pulse discharge. A state equation is configured based on battery parameters. The state equation, which applied Peukert's law, can estimate SOC more accurately. SOC estimation capability was analyzed by utilizing reduced Federal Test Procedure (FTP-72) current profile and using a bi-directional DC-DC converter at temperature ($25^{\circ}C$). The PI state observer, which is designed in this study, indicated a SOC estimation error rate of ${\pm}2%$ in any of the initial SOC states. The PI state observer confirms a strong SOC estimation performance despite disturbances, such as modeling errors and noise.

• 한국정보기술학회논문지
• /
• 제18권5호
• /
• pp.55-64
• /
• 2020

태양광 시스템의 안정성과 신뢰성 향상을 위해서는 배터리의 잔존량 (State of Charge, SOC)을 정확하게 추정하여야 한다. 본 연구에서는 gradient descent, Levenberg-Marquardt 및 scaled conjugate gradient 학습방법을 사용한 인공 신경회로망 (Artificial Neural Networks, ANN)과 적응형 뉴로-퍼지 추론 시스템 (Adaptive Neuro-Fuzzy Inference System, ANFIS)을 사용한 SOC 추정방법을 제안한다. 입력으로는 충전 시작 전압 및 적류적산법을 통해 구한 충전 전류를 사용하여 추정된 SOC를 출력한다. 4개의 모델 (ANN-GD, ANN-LM, ANN-SCG, 및 ANFIS)을 사용하여 SOC 추정 방법을 구현하였고 실험을 통해 MATLAB을 사용하여 4개의 모델의 성능을 비교 분석하였다. 실험 결과로부터 ANFIS 모델을 사용한 배터리의 SOC 추정이 가장 정확도가 높았으며 빠른 속도로 수렴함을 확인하였다.

• 조명전기설비학회논문지
• /
• 제29권9호
• /
• pp.96-102
• /
• 2015

This paper presents battery charging method using 3-phase AC-DC boost converter. General battery charging method is that charging the battery voltage to the reference voltage according to the constant current(CC) control, when it reaches the reference voltage, charging the battery fully according to the constant voltage(CV) control. However, battery chaging time is increased because of the battery impedance, constant current charging section which shoud take the large amount of charge is narrow, and constant voltage charging section which can generate insufficient charge is widen. To improve this problem, we proposes the method to reduce the charging time according to the SOC(State of Charge) estimation using battery impedance.

• 전력전자학회:학술대회논문집
• /
• 전력전자학회 2018년도 전력전자학술대회
• /
• pp.156-158
• /
• 2018

The inconsistencies between paralleled battery cells are becoming more considerable issue in high capacity battery applications like electric vehicles. Due to differences in state-of-charge (SOC) and internal resistance within individual cells in parallel, charging or discharging current is not appropriately balanced to each cell in terms of SOC, which may shorten the lifetime or sometimes cause safety issues. In this paper, an intelligent cell-balancing algorithm is proposed to overcome the inconsistency issue especially for paralleled battery cells. In this scheme, SOC information collected in the sub-BMS module is sent to the main-BMS module, where the number of parallel cells to be connected to DC bus is continuously updated based on the suggested SOC comparison rule. To verify the method, operation of the algorithm on 4 paralleled battery cells are simulated on Matlab/Simulink. The simulation result shows that the SOCs of paralleled cells are evenly redistributed. It is expected that the proposed algorithm provides high reliable and prolong the life cycle and working capacity of the battery pack.