• Journal of Advanced Marine Engineering and Technology
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• v.36 no.7
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• pp.919-926
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• 2012

In order to control a LTI(Linear Time Invariant) system subjected to system noise and measurement noise, first of all, it is necessary to estimate the state of system with reliability. Kalman filtering technique has been widely used to estimate the state of the stochastic LTI system with stationary noise characteristics because of its estimation ability versus algorithm simplicity. However, it often fails to estimate the state of the LTI system of which system parameter uncertainty exists partly and/or input uncertainty exists. In this paper, a new estimation technique based on Kalman filter is suggested for stochastic LTI system under parameter uncertainty and/or input uncertainty. A fuzzy estimation algorithm against uncertainties is introduced so as to compensate the state estimate filtered by Kalman filter. In order to verify the state estimation performance of the suggested technique, several simulations are accomplished.

• The Transactions of the Korean Institute of Power Electronics
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• v.27 no.4
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• pp.332-338
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• 2022

This study proposes a battery state-of-health estimation method by applying a feature extraction technique. The technique that can improve estimation performance is the process of identifying and extracting meaningful data. To apply a data-driven-based aging state estimation method to batteries, health indicators are used as training data. However, limitations occur in extracting health indicators from charge/discharge cycles. This study proposes a deep-learning-based battery state-of-health estimation method that applies feature extraction techniques to compensate for this problem. According to the performance evaluation result of the proposed method, it has a low estimation error of 0.3887% based on an absolute error evaluation method.

• Journal of the Society of Naval Architects of Korea
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• v.40 no.5
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• pp.43-52
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• 2003

Two methods which estimate manoeuvring derivatives in the model of hydrodynamic force and moment acting on a manoeuvring ship using sea trial data were compared. One is the widely used parameter estimation method by using the Extended Kalman Filter (EKF), which estimates state variables of linearized state space model at every instant after dealing with the coefficients as the augmented state variables. The other one is the Estimation-Before-Modeling (EBM) technique, so called the two-step method. In the first step, hydrodynamic force of which dynamic model is assumed the third-order Gauss-Markov process is estimated along with motion variables by the EKF and the modified Bryson-Frazier smoother. Then, in the next step, manoeuvring derivatives are identified through the regression analysis. If the exact structure of hydrodynamic force could be known, which was an ideal case, the EKF method would be regarded as being more superior compared to the EBM technique. However the EBM technique was more robust than the EKF method from a realistic point of view where the assumed model structure was slightly different from the real one.

• 제어로봇시스템학회:학술대회논문집
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• 1991.10a
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• pp.114-119
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• 1991

An input estimation technique for tracking filter(CHP algorithm) suggested by Y.T. Chan et. al. has bad performance for low maneuvering targets. In this paper, two maneuver detection algorithms are applied to Singer's target model. First, an CHP input estimation technique is applied to 9 state target model. Second, we construct a maneuver detection and correction technique using pseudo acceleration measurements, which are derived directly from measurements. These two filters have good performance for even the low maneuvering targets.

• Proceedings of the KIEE Conference
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• 1999.07c
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• pp.1049-1053
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• 1999

It is important to measuring and monitoring about state vectors of power system for precise operation control. All state vectors cannot be measured because it is economically disadvantageous, so that some state vectors are determined using state estimator. Determination of observability is a important precondition of power system state estimation because state estimation can be performed when given power system is observable. Recently as time-synchronization technique progress, using the PMU(Phasor Measurement Unit), state vector can be measured directly so that voltage phasor and current phasor measurements can be used for power system estimation. In this paper, observability algorithm is proposed to determinate the observability with real/reactive injection power measurements and real/reactive lineflow power measurements of existing measurement system and with phasor measurements of PMU. The jacobian matrix is newly composed for state estimation with measurements of added PMU, and state estimation is performed with least square estimatior. Comparison between state estimation result of existing measurement system and that of measurement system added PMU is presented.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.37 no.7
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• pp.427-433
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• 1988

This paper presents a partitioned state estimation algorithm on the basis of network reduction by using a estimation technique of boundary line flows. The network is partitioned into several subnetworks, which generates boundary lines. The accurate estimation of boundary line flows enables us to perform state estimation on each sub-system independently. A precise method to estimate boundary line flows is presented for the partitioned state estimation. The proposed algorithm redices computation time and memory requirements remarkably. The proposed algorithm have been tested for IEEE sample system and verified to be applicable to practical power systems.

• 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.22 no.3
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• pp.49-62
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• 1973

An algorithm for the state estimation and identification of multivariable nonlinear systems with noisy nonlinear observation has been investigated on the basis of the multidimensional Hermitian expansion for the a posteriori probability densities of the predicted observation, the predicted state and the observation conditioned by the state. A new approach for construction of this sequential nonlinear estimator, retaining up to the second order term of the observation error, has been developed, along with the approximation of nonlinear system functions, truncating at the second term. The estimation of the unknown parameters has been established by extending the state estimation technique, regarding the parameters as another state variables. The results of investigation indicate the feasibility of the schemes presented in this paper.

• Proceedings of the KIPE Conference
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• 2013.07a
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• pp.317-318
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• 2013

State-of-Charge (SOC) is one of the most important indicators for the battery management system. Thus its precise estimation is crucial not only for effectively utilizing the energy but also preventing critical situations from happening to the powertrain of the vehicle. However, lead-acid battery is time-variant and highly nonlinear, and the hysteresis phenomenon causes large errors in estimating SOC. This paper proposes a novel SOC estimation technique for the lead-acid battery by using Extended Kalman Filter (EKF) considering hysteresis effect. The validity of the proposed technique is verified through the experiments.

• Proceedings of the KIPE Conference
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• 2017.07a
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• pp.126-127
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• 2017

An accurate State Of Charge (SOC) estimation of battery is the most important technique for Electric Vehicles (EVs) and Energy Storage Systems (ESSs). In this paper a new integrated Unscented Kalman Filter-Particle Filter (UKF-PF) is employed to estimate the SOC of a $LiFePO_4$ battery cell and a significant improvement is obtained as compared to the other methods. The parameters of the battery is modeled by the second order Auto Regressive eXogenous (ARX) model and estimated by using Recursive Least Square (RLS) method to calculate value of each element in the model. The proposed algorithm is established by combining a parameter identification technique using RLS method with ARX model and an SOC estimation technique using UKF-PF.