• Journal of the Institute of Electronics and Information Engineers
• /
• v.54 no.4
• /
• pp.99-107
• /
• 2017

This paper proposes a battery model coefficient correction method for improving the accuracy of existing lithium battery equivalent models. BMS(battery management system) has been researched and developed to minimize shortening of battery life by keeping SOC(state of charge) and state of charge of lithium battery used in various industrial fields such as EV. However, the cell balancing operation based on the battery cell voltage can not follow the SOC change due to the internal resistance and the capacitor. Various battery equivalent models have been studied for estimation of battery SOC according to the internal resistance of the battery and capacitors. However, it is difficult to apply the same to all the batteries, and it tis difficult to estimate the battery state in the transient state. The existing battery electrical equivalent model study simulates charging and discharging dynamic characteristics of one kind of battery with error rate of 5~10% and it is not suitable to apply to actual battery having different electric characteristics. Therefore, this paper proposes a battery model coefficient correction algorithm that is suitable for real battery operating environments with different models and capacities, and can simulate dynamic characteristics with an error rate of less than 5%. To verify proposed battery model coefficient calibration method, a lithium battery of 3.7V rated voltage, 280 mAh, 1600 mAh capacity used, and a two stage RC tank model was used as an electrical equivalent model of a lithium battery. The battery charge/discharge test and model verification were performed using four C-rate of 0.25C, 0.5C, 0.75C, and 1C. The proposed battery model coefficient correction algorithm was applied to two battery models, The error rate of the discharge characteristics and the transient state characteristics is 2.13% at the maximum.

• Journal of Power Electronics
• /
• v.17 no.6
• /
• pp.1625-1636
• /
• 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.

• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.16 no.5
• /
• pp.3391-3398
• /
• 2015

Recently, the large scaled lead-acid battery is widely introduced to efficient operation of the photovoltaic system in many islands. but the demand of lithium-ion battery is getting increased by the operation of wind power and replacement of the lead-acid battery. And also, under the renewable portfolio standard(RPS) and energy efficiency resource standard(EERS) policy of Korea government, the introduction of energy storage system(ESS) has been actively increased. Therefore, this paper presents the operation algorithm of hybrid battery management system(BMS) using the lead-acid and lithium-ion batteries, in order to maximize advantage of each battery. In other words, this paper proposed the algorithm of state of charge(SOC) and hybrid operation algorithm to calculate the optimal composition rate considering the fixed cost and operation cost of each battery. From the simulation results, it is confirmed that the proposed algorithms are an effective tool to evaluate SOC and to optimally operate hybrid ESS.

• Proceedings of the Korean Institute of IIIuminating and Electrical Installation Engineers Conference
• /
• 2008.05a
• /
• pp.321-324
• /
• 2008

This paper presents the battery management system(BMS) for the optimum conditions of the lead-Acid battery in UPS. The proposed system controls the over and under currents of battery for protecting and it was applied algorithm for optimum conditions to estimate the State Of Charge(SOC) in charge or discharge mode. It approved the performance and the algorithm for the estimation of SOC, through the experiments which using the charge and discharge tester and the field tests.

• Proceedings of the KIPE Conference
• /
• 2019.11a
• /
• pp.174-175
• /
• 2019

Battery cells are connected in parallel to enlarge the system capacity. However, cell inconsistency may reduce the overall system capacity and cause the over-charging or over-discharging issue. This paper proposes a SOC-based sequencing equalizer for parallel-connected battery configuration that uses the ANFIS (adaptive neuro-fuzzy inference system) algorithm to make the switching decision. Depend on the load current and the SOC (state-of-charge) rate of cells, the switching decision is made to equalize the SOC of the battery cells. The simulation results show that the system capacity is maximized and the controller is adaptive for a large number of parallel-connected in dynamic load profile.

• Proceedings of the KIPE Conference
• /
• 2016.11a
• /
• pp.75-76
• /
• 2016

The relationship of widely used Open circuit Voltage (OCV) versus State of Charge (SOC) is critical for any reliable SOC estimation technique. However, the hysteresis existing in all type of battery which has been come to the market leads this relationship to a complicated one, especially in Lithium Iron Phosphate (LiFePO4) battery. An accurate model for hysteresis phenomenon is essential for a reliable SOC identification. This paper aims to investigate and propose a method for hysteresis modeling. The SOC estimation is done by using Extended Kalman Filter (EKF), the parameter of the battery is modeled by Auto Regressive Exogenous (ARX) and estimated by using Recursive Least Square (RLS) filter to tract each element of the parameter of the model.

• Journal of Electrochemical Science and Technology
• /
• v.13 no.3
• /
• pp.323-338
• /
• 2022

Heat generation and temperature of a battery is usually presented by an equation of current. This means that we need to adopt time domain calculation to obtain thermal characteristics of the battery. To avoid the complicated calculations using time domain, 'state of charge (SOC)' can be used as an independent variable. A SOC based calculation method is elucidated through the comparison between the calculated results and experimental results together. Experiments are carried for rapid resistive discharge of a large-capacitive lithium secondary battery to evaluate variations of cell potential, current and temperature. Calculations are performed based on open-circuit cell potential (SOC,T), internal resistance (SOC,T) and entropy (SOC) with specific heat capacity.

• Proceedings of the Korean Society of Computer Information Conference
• /
• 2017.07a
• /
• pp.168-169
• /
• 2017

중앙 전력과 단절된 독립형 마이크로그리드는 발전된 신재생 에너지를 저장하는 ESS의 효율적인 운영을 통해 블랙아웃을 방지하고 디젤 발전기의 발전 비용을 최소화하는 것이 필요하다. 본 논문에서는 기상 정보를 이용하여 신재생 에너지 발전량을 예측하고, 최적화 알고리즘을 이용해 생성된 후보 계획의 평가 시 ESS의 SOC를 유지하지 못하는 경우 페널티를 부여함으로써 신재생 발전량 예측의 오류에 대비하였다. 시뮬레이션 실험을 통해 제안하는 SOC 유지를 고려한 운영 계획 최적화 방안이 기존의 예측 제어 기반 최적화 방안에 비해 블랙아웃을 방지하면서도 디젤 발전 비용을 절감할 수 있음을 확인하였다.

• Proceedings of the KIPE Conference
• /
• 2014.11a
• /
• pp.58-59
• /
• 2014

본 논문에서는 비례-적분(PI) 제어의 상태관측기를 구성하여 리튬폴리머 배터리의 충전량(SOC)을 추정하는 기법에 대해 설계한 뒤 실험을 통하여 검증하였다. 리튬폴리머 배터리는 1차 R-C 등가모델로 단순화하여 표현하였고, PI상태관측기를 Matlab/Simulink에서 설계하였다. 상온($25^{\circ}C$)에서 양방향 DC-DC 컨버터를 이용하여 리튬폴리머 배터리에 FTP-72 충 방전 사이클의 전류패턴을 인가한 뒤 SOC 추정기법을 검증하였다. PI상태관측기는 임의의 초기 SOC 상태에서도 오차율 2%이내로 SOC를 추정하여 모델링 에러나 외란에도 강인한 특성이 있는 것을 확인하였다.

• Proceedings of the KIEE Conference
• /
• 2015.07a
• /
• pp.625-626
• /
• 2015

본 논문에서는 주파수 조정용으로 에너지 저장장치를 활용하기 위해 SOC(State Of Charge) 상태에 따른 가변Droop계수를 적용하여 주파수를 회복하기 위해 SOC 목표 값 과 그에 따른 주파수 변화량을 계산해 최적 SOC 목표 값을 찾는 방법을 제안한다.