• Journal of Power Electronics
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• v.18 no.3
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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.

• Proceedings of the KIPE Conference
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• 2020.08a
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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.

• Journal of The Institute of Information and Telecommunication Facilities Engineering
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• v.10 no.1
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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%.

• Journal of the Korean Applied Science and Technology
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• v.25 no.1
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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.

• Journal of the Korean Electrochemical Society
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• v.11 no.1
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• pp.33-36
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• 2008

In the present study, impedance spectrometry has been used for predicting State-of-Charge (SoC) of gel type, Valve Regulated Lead Acid (VRLA), battery. The impedance measurements of VRLA battery (2V/1.2 Ah) at various SoC were made over the frequency range from 100kHz to 10mHz with an amplitude 10 mV. The impedance parameters have been evaluated by the analysis of the data using an equivalent circuit and a complex non-linear least squares (CNLS) fitting method. The charge transfer resistance values and double layer capacitance values of the positive electrode were higher than those of the negative electrode. The gel resistance values increased with decreasing in SoC. This indicates that the gel resistance is an important parameter for predicting SoC of VRLA battery.

• Journal of Power Electronics
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• v.18 no.4
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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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• v.13 no.4
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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.

• Journal of Electrical Engineering and Technology
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• v.12 no.5
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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.

• The Journal of Korea Robotics Society
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• v.18 no.1
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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.

• Journal of Power Electronics
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• v.15 no.3
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• pp.775-785
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• 2015

Many equalization circuits have been proposed to improve pack performance and reduce imbalance. Although bidirectional equalization topologies are promising in these methods, pre-equalization global equalization strategy is lacking. This study proposes a novel state-of-charge (SoC) equalization algorithm for bidirectional equalizer based on particle swarm optimization (PSO), which is employed to find optimal equalization time and steps. The working principle of bidirectional equalization topologies is analyzed, and the reason behind the application of SoC as a balancing criterion is explained. To verify the performance of the proposed algorithm, a pack with 12 LiFePO4 batteries is applied in the experiment. Results show that the maximum SoC gap is within 2% after equalization, and the available pack capacity is enhanced by 13.2%. Furthermore, a comparison between previously used methods and the proposed PSO equalization algorithm is presented. Experimental tests are performed, and results show that the proposed PSO equalization algorithm requires fewer steps and is superior to traditional methods in terms of equalization time, energy loss, and balancing performance.