• Journal of Institute of Control, Robotics and Systems
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• v.14 no.9
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• pp.904-908
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• 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.

• 전기의세계
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• v.28 no.3
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• pp.57-64
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• 1979

The static state estimation are divided into two groups; estimation and detection & identification. This paper centers on detection and identification algorithm. Especially, the identification of line errors is focused on and is performed by the extended W.L.S. algorithm with line swithching states. Here, line switching states mean the discrete values of line admittance which are influenced by unexpected line switching. The numerical results are obtained from the assumption that the noise vector is independent zero mean Gaussian random variables.

• Journal of Power Electronics
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• v.18 no.1
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• pp.129-136
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• 2018

An accurate state-of-charge (SOC) estimation ensures the reliable and efficient operation of a lithium-ion battery management system. On the basis of a combined electrochemical model, this study adopts the forgetting factor least squares algorithm to identify battery parameters and eliminate the influence of test conditions. Then, it implements online SOC estimation with high accuracy and low run time by utilizing the low computational complexity of the unscented Kalman filter (UKF) and the rapid convergence of a particle filter (PF). The PF algorithm is adopted to decrease convergence time when the initial error is large; otherwise, the UKF algorithm is used to approximate the actual SOC with low computational complexity. The effect of the number of sampling particles in the PF is also evaluated. Finally, experimental results are used to verify the superiority of the combined method over other individual algorithms.

• Journal of Electrical Engineering and Technology
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• v.11 no.6
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• pp.1802-1811
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• 2016

A novel battery SOH estimation algorithm based on outlier detection has been presented. The Battery state of health (SOH) is one of the most important parameters that describes the usability state of the power battery system. Firstly, a battery system model with lifetime fading characteristic was established, and the battery characteristic parameters were acquired from the lifetime fading process. Then, the outlier detection method based on angular distribution was used to identify the outliers among the battery behaviors. Lastly, the functional relationship between battery SOH and the outlier distribution was obtained by polynomial fitting method. The experimental results show that the algorithm can identify the outliers accurately, and the absolute error between the SOH estimation value and true value is less than 3%.

• Proceedings of the KIEE Conference
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• 2000.07a
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• pp.116-118
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• 2000

The modern power system including lots of measuring devices and controller is large and complex total information system. A Lot of data and system information are transmitted to operators, and analysing these information and system management is very important. Recently, GUI(Graphic Users Interface) is emphasized as a method that operators carry out their duties, effectively. In this paper. a simulator that can show state estimation and detection of bad measuring devices is introduced for domestic 154kV/22.9kV distribution substations. C language and Visual Basic is used for this simulator. and TCP/IP is adopted to consider connection with a Power system.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.35 no.7
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• pp.261-271
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• 1986

This paper describes a method for the state estimation considering current measurement component and detection of the bad data. The state values are estimated by weighted least square method in which measurement vector included bus injection current and line current. The bad data are detected using standardized variable of normal distribution and identified using sensitivity coefficients. When the bad data were occured by the bad measurement values. The results of the application to the model power system reveal the effectiveness of the presented algorithms.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.61 no.8
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• pp.1140-1147
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• 2012

This paper proposes the power management technology for stand-alone PV system to extend installation environment and coverage. The proposed power management technology in this paper can protect battery safeness from overcharge/discharge with keeping the proper SOC(State of Charge) and extend using time of system through estimation of operating power. The proposed power management technology in this paper is applied to Infra-free Variable Message Sign. And performance of power management technology in this paper was verified using simulation scenario.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.58 no.10
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• pp.2038-2040
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• 2009

This paper studies the state estimation problem for the current of DC/DC boost converters with parasitic inductor resistance. The parasitic resistance increases the system uncertainty when the output load variation occurs. In order to enhance the observation performance of the Luenberger observer this paper includes the integral of the estimation error signal to the estimation algorithm. By using the proposed PI observer the converter current signal is successfully reconstructed with the voltage measurement regardless of the load uncertainty. Computer simulation has been carried out by using Simulink/Sim Power System. Simulation results show the proposed method maintains robust estimation performance against the model uncertainty.

• Proceedings of the KIEE Conference
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• 1984.07a
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• pp.134-137
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• 1984

• The Transactions of the Korean Institute of Power Electronics
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• v.26 no.4
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• pp.241-248
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• 2021

A battery state-of-health (SOH) estimation algorithm using a machine learning-based linear regression method is proposed for estimating battery aging. The proposed algorithm analyzes the change trend of the open-circuit voltage (OCV) curve, which is a parameter related to SOH. At this time, a section with high linearity of the SOH and OCV curves is selected and used for SOH estimation. The SOH of the aged battery is estimated according to the selected interval using a machine learning-based linear regression method. The performance of the proposed battery SOH estimation algorithm is verified through experiments and simulations using battery packs for electric vehicles.