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

• Journal of Power Electronics
• /
• 제16권1호
• /
• pp.238-248
• /
• 2016

The state-of-charge (SOC) and state-of-health (SOH) estimation of batteries play important roles in managing batteries for automotive applications. However, an accurate state estimation of a battery is difficult to achieve because of certain factors, such as measurement noise, highly nonlinear characteristics, strong hysteresis phenomenon, and diffusion effect of batteries. In certain vehicular applications, such as idle stop-start systems (ISSs), significant errors in SOC/SOH estimation may lead to a failure in restarting a combustion engine after the shut-off period of the engine when the vehicle is at rest, such as at a traffic light. In this paper, a dual extended Kalman filter algorithm with a dynamic equivalent circuit model of a lead-acid battery is proposed to deal with this problem. The proposed algorithm adopts a battery model by taking into account the hysteresis phenomenon, diffusion effect, and parameter variations for accurate state estimations of the battery. The validity of the proposed algorithm is verified through experiments by using an absorbed glass mat valve-regulated lead-acid battery and a battery sensor cable for commercial ISS vehicles.

• 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.

• 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.

• Journal of Electrical Engineering and Technology
• /
• 제9권5호
• /
• pp.1729-1738
• /
• 2014

The offline verification of state of charge estimation, power estimation, fault diagnosis and emergency control of battery management system (BMS) is one of the key technologies in the field of electric vehicle battery system. It is difficult to test and verify the battery management system software in the early stage, especially for algorithms such as system state estimation, emergency control and so on. This article carried out the virtual environment modeling for verification of battery management system. According to the input/output parameters of battery management system, virtual environment is determined to run the battery management system. With the integration of the developed BMS model and the external model, the virtual environment model has been established for battery management system in the vehicle's working environment. Through the virtual environment model, the effectiveness of software algorithm of BMS was verified, such as battery state parameters estimation, power estimation, fault diagnosis, charge and discharge management, etc.

• 한국지능시스템학회논문지
• /
• 제20권2호
• /
• pp.214-219
• /
• 2010

본 논문은 리튬폴리머 배터리의 잔존충전용량 추정을 위한 비선형 퍼지 $H_{\infty}$ 필터의 설계 방법을 제시한다. 배터리 셀의 동적방정식을 T-S 퍼지시스템으로 모델하고 선형행렬 부등식의 해를 이용하여 퍼지필터를 설계한다. 제시한 퍼지 $H_{\infty}$ 필터의 성능을 입증하기 위하여 UDDS 전류 프로파일을 사용한 실험 데이터를 이용하여 모의실험을 수행하였다.

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

• 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
• /
• 제28권11호
• /
• pp.77-83
• /
• 2014

The Estimation of State of Charge(SOC) for batteries is an important aspect of a Battery Management System(BMS). A method for estimating the SOC is proposed in order to overcome the individual disadvantages of the current integral and Open Circuit Voltage(OCV) estimation methods by combining them using Extended Kalman filter(EKF). The non-linear characteristics of the Li-Ion RC battery model used in this study is also solved through EKF. The proposed method is simulated in a Matlab environment with a Li-Ion Kokam battery (3.7V, 1,500mAh). Results showed that there is an improvement in the estimation error when using the proposed model compared to the conventional current integral method.

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