• 전기학회논문지
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• 제63권8호
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• pp.1085-1091
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• 2014

A model based SOC estimation scheme using parameter identification is described and applied to a Lithium-ion battery module that can be installed in electric vehicles. Simulation studies are performed to verify the effect of sensor faults on the SOC estimation results for terminal voltage sensor and load current sensor. The sensor faults should be detected and isolated as soon as possible because the SOC estimation error due to any sensor fault seriously affects the overall performance of the BMS. A new fault detection and isolation(FDI) scheme by which the fault of terminal voltage sensor and load current sensor can be detected and isolated is proposed to improve the reliability of the BMS. The proposed FDI scheme utilizes the parameter estimation of an input-output model and two fuzzy predictors for residual generation; one for terminal voltage and the other for load current. Recently developed dual polarization(DP) model is taken to develope and evaluate the performance of the proposed FDI scheme. Simulation results show the practical feasibility of the proposed FDI scheme.

• Korean Chemical Engineering Research
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• 제60권3호
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• pp.363-370
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• 2022

본 논문에서는 전기자동차용 배터리 충/방전 상태 추정의 정확도를 개선하기 위해 칼만 필터(Kalman Filter, KF) 알고리즘과 등가회로모델(Equivalent Circuit Model)을 활용한 State Of Charge (SOC) 추정 방법을 적용하였다. 특히 노화된 배터리 용량을 함께 추정 가능한 관측기(observer)를 설계하였다. 우선 노화가 없는 경우, 칼만 필터를 이용하여 SOC를 단일 추정하면, 관측기 없이 모델로 계산된 경우와 비교하여 평균 절대 오차율이 1.43%(관측기 미사용)에서 0.27%(관측기 사용)로 감소하였다. 차량 주행상태에서는 전류가 고정되지 않아 SOC와 배터리 용량을 모두 추정하는 것에 일반적인 KF 혹은 Extended KF 알고리즘을 이용할 수 없다. 배터리 노화에 의한 용량 변화는 단시간에 일어나지는 않다는 점에 착안하여, 충전 시 배터리 용량 추정을 주기적으로 실시하는 전략을 제시하였다. 충전 모드에서는 일정 구간마다 전류가 고정되기에, 해당 상황에서 배터리 노화 용량을 SOC와 함께 추정 전략을 제시하였다. 전류가 고정된 상태에서 SOC 추정의 평균 절대 오차율은 0.54% 였으며, 용량 추정의 평균 절대 오차율은 2.24%로 나타났다. 충전상태에서 전류가 고정됨으로 일반적인 EKF를 활용하여 배터리 용량과 SOC 동시 추정이 가능하도록 하였다. 이를 통하여 배터리 충전 시 주기적인 배터리 용량 보정을 수행할 수 있다. 그리고, 방전 시에는 해당 용량으로 고정한 채 SOC를 추정하는, 배터리 관리 시스템에서 활용 가능한 추정 알고리즘을 제안하였다.

• 전력전자학회:학술대회논문집
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• 전력전자학회 2014년도 추계학술대회 논문집
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• pp.60-61
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• 2014

Lithium-Ion batteries have become the best tradeoff between energy, power density and cost of the energy storage system in many portable high electric power applications. In order to manage the battery efficiently State of Charge (SOC) of the battery needs to be estimated accurately. In this paper a model-based approach to estimate the SOC of the Lithium-Ion battery based on the estimation of the battery impedance is proposed. The validity and feasibility of the proposed algorithm is verified by the experimental results.

• 한국조명전기설비학회:학술대회논문집
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• 한국조명전기설비학회 2008년도 춘계학술대회 논문집
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• pp.317-320
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• 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) and State Of Health(SOH) in charge or discharge mode. It approved the performance and the algorithm for the estimation of SOH, through the experiments which using the charge and discharge tester and the field tests.

• 한국정보기술학회논문지
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• 제18권5호
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• pp.55-64
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• 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.

• Journal of Power Electronics
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• 제13권4호
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• pp.516-527
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• 2013

This paper presents a new overall system for state-of-available-power (SoAP) prediction for a lithium-ion battery pack. The essential part of this method is based on an adaptive network architecture which utilizes both fuzzy model (FIS) and artificial neural network (ANN) into the framework of adaptive neuro-fuzzy inference system (ANFIS). While battery aging proceeds, the system is capable of delivering accurate power prediction not only for room temperature, but also at lower temperatures at which power prediction is most challenging. Due to design property of ANN, the network parameters are adapted on-line to the current battery states (state-of-charge (SoC), state-of-health (SoH), temperature). SoC is required as an input parameter to SoAP module and high accuracy is crucial for a reliable on-line adaptation. Therefore, a reasonable way to determine the battery state variables is proposed applying a combination of several partly different algorithms. Among other SoC boundary estimation methods, robust extended Kalman filter (REKF) for recalibration of amp hour counters was implemented. ANFIS then achieves the SoAP estimation by means of time forward voltage prognosis (TFVP) before a power pulse occurs. The trade-off between computational cost of batch-learning and accuracy during on-line adaptation was optimized resulting in a real-time system with TFVP absolute error less than 1%. The verification was performed on a software-in-the-loop test bench setup using a 53 Ah lithium-ion cell.

• Journal of Power Electronics
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• 제18권5호
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• pp.1585-1594
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• 2018

The estimation of $LiFePO_4$/graphite battery states suffers from the prominent hysteresis phenomenon between the respective open-circuit voltage curves towards charging and discharging. A lot of hysteresis models have been documented to investigate the hysteresis mechanism. This paper reviews and deeply interprets four non-electrochemical hysteresis models and some improvements. These models can be conveniently incorporated into commonly used equivalent circuit models to reproduce battery behaviors. Through simulation and experimental comparisons of voltage predictions and state-of-charge estimations, the pros and cons of these models are presented.

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

• 전력전자학회:학술대회논문집
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• 전력전자학회 2014년도 전력전자학술대회 논문집
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• pp.401-402
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• 2014

State of Charge (SOC) and State of Health (SOH) are the key issues for the application of Absorbent Glass Mat (AGM) type battery in Idle Start Stop (ISS) system which is popularly integrated in Electric Vehicles (EVs). However, battery parameters strongly depend on SOC, current rate and temperature and significantly change over the battery life cycles. In this research, a novel method for SOC, SOH estimation which combines the Auto Regressive with external input (ARX) method using for online parameters prediction and Dual Extended Kalman Filter (DEKF) algorithm considering hysteresis is proposed. The validity of the proposed algorithm is verified by the simulation and experiments.

• 전력전자학회:학술대회논문집
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• 전력전자학회 2016년도 추계학술대회 논문집
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• pp.59-60
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• 2016

Flooded lead acid batteries are still very popular in the industry because of their low cost as compared to their counterparts. State of Charge (SOC) estimation is of great importance for a flooded lead acid battery to ensure its safe working and to prevent it from over-charging or over-discharging. Different types of Kalman Filters are widely used for SOC estimation of batteries. The values of process and measurement noise covariance of a filter are usually calculated by trial and error method and taken as constant throughout the estimation process. While in practical cases, these values can vary as well depending upon the dynamics of the system. Therefore an Adaptive Unscented Kalman Filter (AUKF) is introduced in which the values of the process and measurement noise covariance are updated in each iteration based on the residual system error. A comparison of traditional and Adaptive Unscented Kalman Filter is presented in the paper. The results show that SOC estimation error by the proposed method is further reduced by 3 % as compared to traditional Unscented Kalman Filter.