• Journal of Power Electronics
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• 제18권3호
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• pp.910-922
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• 2018

Conventional battery state-of-charge (SoC) estimation methods either involve sophisticated models or consume considerable computational resource. This study constructs an enhanced coulomb counting method (Ah method) for the SoC estimation of lithium-ion batteries (LiBs) by expanding the Peukert equation for the discharging process and incorporating the Coulombic efficiency for the charging process. Both the rate- and temperature-dependence of battery capacity are encompassed. An SoC mapping approach is also devised for initial SoC determination and Ah method correction. The charge counting performance at different sampling frequencies is analyzed experimentally and theoretically. To achieve a favorable compromise between sampling frequency and accumulation accuracy, a frequency-adjustable current sampling solution is developed. Experiments under the augmented urban dynamometer driving schedule cycles at different temperatures are conducted on two LiBs of different chemistries. Results verify the effectiveness and generalization ability of the proposed SoC estimation method.

• 전력전자학회:학술대회논문집
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• 전력전자학회 2020년도 전력전자학술대회
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• pp.389-390
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• 2020

본 논문은 DC 마이크로그리드에서 배터리 에너지 저장 장치의 SoC(State-of-Charge)를 기반으로 한 계층 제어 기법을 제안한다. DC 마이크로그리드의 안정적인 운영을 위해 계층 구조의 제어방식이 필요하다. 제안하는 계층 제어 기법은 2 레벨로 구성된다. 1차 제어에서는 SoC에 의해 드룹 계수가 조정되고 충·방전 전류가 제어되어 SoC 밸런싱이 이루어진다. 2차 제어는 1차 제어에 의해 발생하는 배전망 전압강하를 보상한다. 제안하는 SoC 기반 계층 제어를 적용한 에너지 저장 장치는 일정 SoC 운용범위에서 동작하고 배전망 전압을 일정하게 유지하여 DC 마이크로그리드의 안정적인 전력관리가 가능해진다. 본 논문에서는 PSIM 시뮬레이션을 통해 제안하는 기법의 유효성을 검증하였다.

• 정보통신설비학회논문지
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• 제10권1호
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• pp.27-32
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• 2011

These days more electric devices are implemented in car, and more accurate estimation of SoC is required. OCV with current integration and Internal Resistance is essential method of Battery SoC Estimation. In this paper we propose OCV with current integration method and compare with Internal Resistance method. In OCV with current integration method estimation error was less than average 2%, but requires more than 5 minutes to stabilize OCV. If Stop and Running conditions are change frequently, estimation error will increase. In Internal resistance Modeling method, in high SoC state, estimation error was more than 15%, and in low SoC state, estimation error was less than 8%.

• 한국응용과학기술학회지
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• 제25권1호
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• pp.52-57
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• 2008

In the present study, impedance characteristics for AgO-Zn cell at various State-of-Charge (SoC) has been studied. The impedance measurements of AgO-Zn cell at various SoC were made over the frequency range from 100kHz to 10mHz with an amplitude 5mV. The impedance parameters have been evaluated by the analysis of the data using an equivalent circuit and a Non-linear least squares (NLLS) fitting method. The total resistances reflects the SoC of the cell. This indicates that the total resistance is important parameter for predicting SoC of AgO-Zn cell.

• 전기화학회지
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• 제11권1호
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• pp.33-36
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• 2008

본 연구에서는 겔식 VRLA (valve regulated lead acid번지의 충전상태(SoC) 판단을 위해 임피던스 기법을 이용하여 조사하였다. 임피던스는 VRLA전지 (2V/1.2Ah)의 다양한 충전상태에서 진폭 10mV로 100kHz에서${\sim}$10mHz까지 측정하였다. 측정된 임피던스 데이터로부터 등가회로를 유도하고, CNLS (Complex Non-linear Least Squares) 법을 사용하여 분석하였다. 양극 쪽의 전하전이 저항과 전기이중층 커패시턴스가 음극보다 높았다. 겔 저항은 충전상태가 감소함에 따라 증가하며 이는 VRLA 전지의 충전상태를 판단하는데 중요한 파라미터임을 확인하였다.

• Journal of Power Electronics
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• 제18권4호
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• pp.1211-1222
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• 2018

This study presents a power distribution control scheme for a three-phase interleaved parallel DC/DC converter in a battery energy storage system. To extend battery life and increase the power equalization rate, a control method based on the nth order of the state of charge (SoC) is proposed for the charging and discharging processes. In the discharging process, the battery sets with high SoC deliver more power, whereas those with low SoC deliver less power. Therefore, the SoC between each battery set gradually decreases. However, in the two-stage charging process, the battery sets with high SoC absorb less power, and thus, a power correction algorithm is proposed to prevent the power of each particular battery set from exceeding its rated power. In the simulation performed with MATLAB/Simulink, results show that the proposed scheme can rapidly and effectively control the power distribution of the battery sets in the charging and discharging processes.

• Journal of Power Electronics
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• 제13권4호
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• pp.569-583
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• 2013

In this study, the simultaneous use of a multi-level converter (MLC) as a DC-motor drive and as an active battery cell balancer is investigated. MLCs allow each battery cell in a battery pack to be independently switched on and off, thereby enabling the potential non-uniform use of battery cells. By exploiting this property and the brake regeneration phases in the drive cycle, MLCs can balance both the state of charge (SoC) and temperature differences between cells, which are two known causes of battery wear, even without reciprocating the coolant flow inside the pack. The optimal control policy (OP) that considers both battery pack temperature and SoC dynamics is studied in detail based on the assumption that information on the state of each cell, the schedule of reciprocating air flow and the future driving profile are perfectly known. Results show that OP provides significant reductions in temperature and in SoC deviations compared with the uniform use of all cells even with uni-directional coolant flow. Thus, reciprocating coolant flow is a redundant function for a MLC-based cell balancer. A specific contribution of this paper is the derivation of a state-space electro-thermal model of a battery submodule for both uni-directional and reciprocating coolant flows under the switching action of MLC, resulting in OP being derived by the solution of a convex optimization problem.

• 한국환경과학회지
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• 제33권7호
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• pp.523-532
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• 2024

In popular tourist destinations such as Jeju and Gangwon, electric rental cars are increasingly adopted. However, sudden battery drain due to weather conditions can pose safety issues. To address this, we developed a battery consumption analysis model that considers resistive energy factors such as acceleration, rolling resistance, and aerodynamic drag. Focusing on the effects of ambient temperature and wind speed, the model's performance was evaluated during an empirical validation period from November to December 2023. Comparing predicted and actual state of charge (SoC) across different routes identified ambient temperature, wind speed, and driving time as major sources of error. The mean absolute error (MAE) increased with lower temperatures due to reduced battery efficiency. Higher wind speeds on routes 1 and 6 resulted in larger errors, indicating the model's limitation in considering only tailwinds for aerodynamic drag calculations. Additionally, longer driving times led to higher actual SoC than predicted, suggesting the need to account for varying driver habits influenced by road conditions. Our model, providing more accurate SoC predictions to prevent battery depletion incidents, shows high potential for application in navigation apps for electric vehicle users in tourist areas. Future research should endeavor to the model by including wind direction, HVAC system usage, and braking frequency to improve prediction accuracy further.

• Journal of Electrical Engineering and Technology
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• 제12권5호
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• pp.2043-2050
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• 2017

This paper presents two MPC (Model Predictive Control) based charging and discharging algorithms of BESS (Battery Energy Storage System) for capacity firming of wind generation. To deal with the intermittency of the output of wind generation, a single BESS is employed. The proposed algorithms not only make the output of combined systems of wind generation and BESS track the predefined reference, but also keep the SoC (State of Charge) of BESS within its physical limitation. Since the proposed algorithms are both presented in simple if-then statements which are the optimal solutions of related optimization problems, they are both easy to implement in a real-time system. Finally, simulations of the two strategies are done using a realistic wind farm library and a BESS model. The results on both simulations show that the proposed algorithms effectively achieve capacity firming while fulfilling all physical constraints.

• 로봇학회논문지
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• 제18권1호
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• pp.88-92
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• 2023

Lithium-ion batteries are actively used in various industrial sites such as field robots, drones, and electric vehicles due to their high energy efficiency, light weight, long life span, and low self-discharge rate. When using a lithium-ion battery in a field, it is important to accurately estimate the SoC (State of Charge) of batteries to prevent damage. In recent years, SoC estimation using data-based artificial neural networks has been in the spotlight, but it has been difficult to deploy in the embedded board environment at the actual site because the computation is heavy and complex. To solve this problem, neural network lightening technologies such as network pruning have recently attracted attention. When pruning a neural network, the performance varies depending on which layer and how much pruning is performed. In this paper, we introduce an optimized pruning technique by improving the existing pruning method, and perform a comparative experiment to analyze the results.