• The Transactions of The Korean Institute of Electrical Engineers
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• v.63 no.8
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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.

• Journal of IKEEE
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• v.23 no.4
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• pp.1328-1336
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• 2019

To maintain the safe and optimal performance of batteries, accurate estimation of state of charge (SOC) is critical. In this paper, Long short-term memory network (LSTM) based on the artificial intelligence algorithm is applied to address the problem of the conventional coulomb-counting method. Different discharge cycles are concatenated to form the dataset for training and verification. In oder to improve the quality of input data for learning, preprocessing was performed. In addition, we compared learning ability and SOC estimation performance according to the structure of LSTM model and hyperparameter setup. The trained model was verified with a UDDS profile and achieved estimated accuracy of RMSE 0.82% and MAX 2.54%.

• Journal of the Microelectronics and Packaging Society
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• v.31 no.1
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• pp.16-22
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• 2024

Along with the high interest in electric vehicles and new renewable energy, there is a growing demand to apply lithium-ion batteries in the construction equipment industry. The capacity of heavy construction equipment that performs various tasks at construction sites is rapidly decreasing. Therefore, it is essential to accurately predict the state of batteries such as SOC (State of Charge) and SOH (State of Health). In this paper, the errors between actual electrochemical measurement data and estimated data were compared using the Dual Extended Kalman Filter (DEKF) algorithm that can estimate SOC and SOH at the same time. The prediction of battery charge state was analyzed by measuring OCV at SOC 5% intervals under 0.2C-rate conditions after the battery cell was fully charged, and the degradation state of the battery was predicted after 50 cycles of aging tests under various C-rate (0.2, 0.3, 0.5, 1.0, 1.5C rate) conditions. It was confirmed that the SOC and SOH estimation errors using DEKF tended to increase as the C-rate increased. It was confirmed that the SOC estimation using DEKF showed less than 6% at 0.2, 0.5, and 1C-rate. In addition, it was confirmed that the SOH estimation results showed good performance within the maximum error of 1.0% and 1.3% at 0.2 and 0.3C-rate, respectively. Also, it was confirmed that the estimation error also increased from 1.5% to 2% as the C-rate increased from 0.5 to 1.5C-rate. However, this result shows that all SOH estimation results using DEKF were excellent within about 2%.

• Proceedings of the Korean Society of Computer Information Conference
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• 2024.01a
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• pp.449-452
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• 2024

본 논문에서는 리튬이온 배터리의 SOC(State Of Charge) 초기 정보의 정확도 향상을 위하여 확장칼만필터(EKF) 방법을 적용한 효율적 SOC 추정 알고리즘을 제안한다. 일반적인 전류적산법을 사용하는 방법은 초기 조건이 부정확한 경우에 오차가 발생하고 시간에 따라 누적 오차가 커지는 단점이 있다. 이러한 문제점 해결을 위하여 초기 SOC 추정값에 EKF 방법을 동시에 적용하는 알고리즘을 제안한다. 제안 알고리즘의 평가를 위한 실험을 통하여 제안 방법이 기존 SOC 추정 방법보다 추정 오차가 개선됨을 확인하였다.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.57 no.5
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• pp.839-844
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• 2008

A novel state of health estimation method for hybrid electric vehicle lithium battery using sliding mode observer has been presented. A simple R-C circuit method has been used for the lithium battery modeling for the reduced calculation time and system resources due to the simple matrix operations. The modeling errors of simple model are compensated by the sliding mode observer. The design methodology for state of health estimation using dual sliding mode observer has been presented in step by step. The structure of the proposed system is simple and easy to implement, but it shows robust control property against modeling errors and temperature variations. The convergence of proposed observer system has been proved by the Lyapunov inequality equation and the performance of system has been verified by the sequence of urban dynamometer driving schedule test. The test results show the proposed observer system has superior tracking performance with reduced calculation time under the real driving environments.

• 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.

• Journal of Power Electronics
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• v.10 no.6
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• pp.769-776
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• 2010

The ultracapacitor module has recently been recast for use in hybrid energy storage systems (HESSs). As a result, accurate state-of-charge (SOC) estimation for an ultracapacitor module is as important as that of primary sources in order to be utilized efficiently in an energy storage system (ESS). However, while SOC estimation via the open-circuit voltage (OCV) method is generally used due to its linear characteristics compared with other ESSs, this method results in many errors in cases of highcurrent charging/discharging within a short time period. Accordingly, this paper introduces a dynamic SOC estimation algorithm that is capable of SOC compensation of an ultracapacitor module even when there is a current input and output. A cycle profile that simulates the operating conditions of a mild-HEV was applied to a vehicle simulator to verify the effectiveness of the proposed algorithm.

• The Transactions of the Korean Institute of Power Electronics
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• v.26 no.1
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• pp.1-8
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• 2021

For the safe and reliable operation of lithium-ion batteries in electric vehicles or energy storage systems, having accurate information of the battery, such as the state of charge (SOC), is essential. Many different techniques of battery SOC estimation have been developed, such as the Kalman filter. However, when this filter is applied to multiple batteries, it has difficulty maintaining the accuracy of the estimation over all cells owing to the difference in parameter values of each cell. The difference in the parameter of each cell may increase as the operation time accumulates due to aging. In this paper, a novel deep neural network (DNN)-based SOC estimation method for multi-cell application is proposed. In the proposed method, DNN is implemented to determine the nonlinear relationships of the voltage and current at different SOCs and temperatures. In the training, the voltage and current data obtained at different temperatures during charge/discharge cycles are used. After the comprehensive training with the data obtained from the cycle test with a cell, the resulting algorithm is applied to estimate the SOC of other cells. Experimental results show that the mean absolute error of the estimation is 1.213% at 25℃ with the proposed DNN-based SOC estimation method.

• Journal of the Korea Institute of Military Science and Technology
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• v.16 no.5
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• pp.639-645
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• 2013

The aim of the study is to investigate the method to estimate a storage life of propelling charge on the decrease of muzzle velocity by stochastic gamma process model. It is required to establish criterion for state failure to estimate the storage life and it is defined in this paper as a muzzle velocity difference between reference value and maximum allowable standard deviation multiplied by 6. The relationship between storage time and muzzle velocity is investigated by nonlinear regression analysis. The stochastic gamma process model is used to estimated the state distribution and the life distribution for storage time for 155mm propelling charge KM4A2 because the regression analysis is a deterministic method and it can't describe the distribution of life for storage time.

• Proceedings of the KIPE Conference
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• 2001.07a
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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.