• Title, Summary, Keyword: Battery State Estimation

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Development of Battery Monitoring System Using the Extended Kalman Filter (확장 칼만 필터를 이용한 배터리 모니터링 시스템 개발)

  • Jo, Sung-Woo;Jung, Sun-Kyu;Kim, Hyun-Tak
    • Journal of the Korea Convergence Society
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    • v.11 no.6
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    • pp.7-14
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    • 2020
  • A Battery Monitoring System capable of State-of-Charge(SOC) estimation using the Extended Kalman Filter(EKF) is described in this paper. In order to accurately estimate the SOC of the battery, the battery cells were modeled as the Thevenin equivalent circuit model. The Thevenin model's parameters were measured in experiments. For the Battery Monitoring System, we designed a battery monitoring device that can calculate the SOC estimation using the EKF and a monitoring server that controls multiple battery monitoring devices. We also develop a web-based dashboard for controlling and monitoring batteries. Especially the computation of the monitoring server could be reduced by calculating the battery SOC estimation at each Battery Monitoring Device.

Online Estimation of SOC and Parameters of Battery Using Augmented Sigma-Point Kalman Filter and RLS

  • Hoang, Thi Quynh Chi;Nguyen, Hoang Vu;Lee, Dong-Choon
    • Proceedings of the KIPE Conference
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    • pp.542-543
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    • 2014
  • In this paper, an estimation scheme based on an augmented sigma-point Kalman filter to estimate the state of charge (SOC) of the battery is presented, where the battery parameters of the series resistance ($R_o$), diffusion capacitance ($C_1$) and resistance ($R_1$) are also estimated through the recursive least squares (RLS) for better accuracy of the SOC. The effectiveness of the proposed method is verified by simulation results.

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A Study on the Configuration of BOP and Implementation of BMS Function for VRFB (VRFB를 위한 BOP 구성 및 BMS 기능구현에 관한 연구)

  • Choi, Jung-Sik;Oh, Seung-Yeol;Chung, Dong-Hwa;Park, Byung-Chul
    • Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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    • v.28 no.12
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    • pp.74-83
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    • 2014
  • This paper proposes a study on the configuration of balancing of plant(BOP) and implementation of battery management system(BMS) functions for vanadium redox flow battery(VRFB) and propose a method consists of sensor and required design specifications BOP system configuration. And it proposes an method of the functions implementation and control algorithm of the BMS for flow battery. Functions of BMS include temperature control, the charge and discharge control, flow control, level control, state of charge(SOC) estimation and a battery protection through the sensor signal of BOP. Functions of BMS is implemented by the sensor signal, so it is recognized as a very important factor measurement accuracy of the data. Therefore, measuring a mechanical signal(flow rate, temperature, level) through the BOP test model, and the measuring an electrical signal(cell voltage, stack voltage and stack current) through the VRFB charge-discharge system and analyzes the precision of data in this paper. Also it shows a good charge-discharge test results by the SOC estimation algorithm of VRFB. Proposed BOP configuration and BMS functions implementation can be used as a reference indicator for VRFB system design.

Absolute Capacity Estimation Method with Temperature Effect for a Small Lithium-polymer Battery (온도의 영향성을 고려한 리튬폴리머 전지의 절대용량 추정 방법)

  • Kim, Hankyong;Kwak, Kiho
    • Journal of the Korea Institute of Military Science and Technology
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    • v.19 no.1
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    • pp.26-34
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    • 2016
  • Military devices and systems powered by batteries need to operate at extreme temperature and estimate the available capacity of the battery at different temperature conditions. However, accurate estimation of battery capacity is challenging due to the temperature-sensitive nature of electrochemical energy storage. In this paper, Peukert's equation with temperature factor is derived, and methods for estimating the absolute capacity of lithium-polymer battery and the state-of-charge(SOC) with respect to varying currents and temperatures are presented. The proposed estimation method is experimentally verified under three different discharge currents(0.5 A, 1 A, 3 A) and six different temperatures ranging from -30 to 45 deg. C. The results show the proposed method reduces the Peukert's estimation error by up to 30 % under or at extreme condition.

Condition Monitoring of Lithium Polymer Batteries Based on a Sigma-Point Kalman Filter

  • Seo, Bo-Hwan;Nguyen, Thanh Hai;Lee, Dong-Choon;Lee, Kyo-Beum;Kim, Jang-Mok
    • Journal of Power Electronics
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    • v.12 no.5
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    • pp.778-786
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    • 2012
  • In this paper, a novel scheme for the condition monitoring of lithium polymer batteries is proposed, based on the sigma-point Kalman filter (SPKF) theory. For this, a runtime-based battery model is derived, from which the state-of-charge (SOC) and the capacity of the battery are accurately predicted. By considering the variation of the serial ohmic resistance ($R_o$) in this model, the estimation performance is improved. Furthermore, with the SPKF, the effects of the sensing noise and disturbance can be compensated and the estimation error due to linearization of the nonlinear battery model is decreased. The effectiveness of the proposed method is verified by Matlab/Simulink simulation and experimental results. The results have shown that in the range of a SOC that is higher than 40%, the estimation error is about 1.2% in the simulation and 1.5% in the experiment. In addition, the convergence time in the SPKF algorithm can be as fast as 300 s.

A Study for BMS Operation Algorithm of Electric Vehicles (전기자동차용 전지관리장치의 전지잔존량 연산알고리즘에 관한 연구)

  • Lee J.Moon;Choi Uk-Don;Lee Jong-Phil;Lee Jong-Chan
    • Proceedings of the KIPE Conference
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    • pp.114-117
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    • 2001
  • In the Electric Vehicle(EV) driving system, the Battery Management System(BMS) is very important and an essential equipment. Particularly, BMS monitors the State of Charge(SOC), voltage, current, and temperature of the battery modules when Electric Vehicle is in the state of motoring or charging. Major roles of BMS are like these the first, estimation of State of Charge(SOC), the second, detection of the unbalance of the voltage between battery modules, the third, control of the available limit of the voltage and temperature of batteries by monitoring the batteries status during motoring or charging. In this research, We have focused on estimating SOC of battery according to the status of Electric Vehicle and the BMS operation algorithm. The result for algorithm of SOC estimation is presented. It have been modified, compensated, and verified by means of the experiment.

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State of Charge Estimator using Sliding Mode Observer for Hybrid Electric Vehicle Lithium Battery (슬라이딩모드 관측기를 이용한 하이브리드 자동차용 리튬배터리 충전량 예측방법)

  • Kim, Il-Song
    • The Transactions of the Korean Institute of Power Electronics
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    • v.12 no.4
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    • pp.324-331
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    • 2007
  • This paper studies new estimation method for state of charge (SOC) of the hybrid electric vehicle lithium battery using sliding mode observer. A simple R-C Lithium battery modeling technique is established and the errors caused by simple modeling was compensated by the sliding mode observer. The structure of the sliding mode observer is simple, but it shows robust control property against modeling errors and uncertainties. The performance of the system has been verified by the UUDS test. The test results of the proposed observer system shows robust tracking performance under real driving environments.

A novel OCV Hysteresis Modeling for SOC estimation of Lithium Iron Phosphate battery (리튬인산철 배터리를 위한 새로운 히스테리시스 모델링)

  • Nguyen, Thanh Tung;Khan, Abdul Basit;Choi, Woojin
    • Proceedings of the KIPE Conference
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    • pp.75-76
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    • 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.

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Enhanced Equivalent Circuit Modeling for Li-ion Battery Using Recursive Parameter Correction

  • Ko, Sung-Tae;Ahn, Jung-Hoon;Lee, Byoung Kuk
    • Journal of Electrical Engineering and Technology
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    • v.13 no.3
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    • pp.1147-1155
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    • 2018
  • This paper presents an improved method to determine the internal parameters for improving accuracy of a lithium ion battery equivalent circuit model. Conventional methods for the parameter estimation directly using the curve fitting results generate the phenomenon to be incorrect due to the influence of the internal capacitive impedance. To solve this phenomenon, simple correction procedure with transient state analysis is proposed and added to the parameter estimation method. Furthermore, conventional dynamic equation for correction is enhanced with advanced RC impedance dynamic equation so that the proposed modeling results describe the battery dynamic characteristics more exactly. The improved accuracy of the battery model by the proposed modeling method is verified by single cell experiments compared to the other type of models.

Multiple Model Adaptive Estimation of the SOC of Li-ion battery for HEV/EV (다중모델추정기법을 이용한 HEV/EV용 리튬이온전지의 잔존충전용량 추정)

  • Jung, Hae-Bong;Kim, Young-Chol
    • The Transactions of The Korean Institute of Electrical Engineers
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    • v.60 no.1
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    • pp.142-149
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    • 2011
  • This paper presents a new state of charge(SOC) estimation of large capacity of Li-ion battery (LIB) based on the multiple model adaptive estimation(MMAE) method. We first introduce an equivalent circuit model of LIB. The relationship between the terminal voltage and the open circuit voltage(OCV) is nonlinear and may vary depending on the changes of temperature and C-rate. In this paper, such behaviors are described as a set of multiple linear time invariant impedance models. Each model is identified at a temperature and a C-rate. These model set must be obtained a priori for a given LIB. It is shown that most of impedances can be modeled by first-order and second-order transfer functions. For the real time estimation, we transform the continuous time models into difference equations. Subsequently, we construct the model banks in the manner that each bank consists of four adjacent models. When an operating point of cell temperature and current is given, the corresponding model bank is directly determined so that it is included in the interval generated by four operating points of the model bank. The MMAE of SOC at an arbitrary operating point (T $^{\circ}C$, $I_{bat}$[A]) is performed by calculating a linear combination of voltage drops, which are obtained by four models of the selected model bank. The demonstration of the proposed method is shown through simulations using DUALFOIL.