• The Transactions of the Korean Institute of Power Electronics
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• v.20 no.2
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• pp.175-181
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• 2015

In this study, a lithium polymer battery (LiPB) is simply expressed by a primary RC equivalent model. The PI state observer is designed in Matlab/Simulink. The non-linear relationship with the OCV-SOC is represented to be linearized with 0.1 pu intervals by using battery parameters obtained by constant-current pulse discharge. A state equation is configured based on battery parameters. The state equation, which applied Peukert's law, can estimate SOC more accurately. SOC estimation capability was analyzed by utilizing reduced Federal Test Procedure (FTP-72) current profile and using a bi-directional DC-DC converter at temperature ($25^{\circ}C$). The PI state observer, which is designed in this study, indicated a SOC estimation error rate of ${\pm}2%$ in any of the initial SOC states. The PI state observer confirms a strong SOC estimation performance despite disturbances, such as modeling errors and noise.

• 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.

• Journal of Institute of Convergence Technology
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• v.11 no.1
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• pp.19-23
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• 2021

These days, sales of battery electric vehicles have been rapidly increasing due to the strict CO₂ regulations. However, since it take too long to measure the energy economy of electric vehicles, it has been required to improve the procedure of energy economy measurement. In order to improve this problem, the present study analyzed the battery charge/discharge pattern according to the changes in battery SOC (state of charge). In general, the energy economy test is started with a battery SOC charged to 100 %. However, it was identified that when the battery is fully charged, it can actually be charged over the 100 % (e.g., 100.5 %). This can induce errors in the energy economy measurement. Therefore, the present study recommend to start the test at SOC 99.9 %. The regenerative braking was partly restricted for the SOC over 90 %. This made it difficult to estimate the overall energy economy of the electric vehicle. However, it was identified that there was no change in the battery charge/discharge characteristics under the SOC 90 %. Therefore, the energy economy test can be shortened by predicting the overall energy economy through a short mileage test.

• Proceedings of the KIPE Conference
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• 2011.07a
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• pp.228-229
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• 2011

본 논문은 SPKF(Sigma-point Kalman Filter)를 이용한 리튬 폴리머 배터리(LiPB)의 충전 상태(SOC: State of Charge) 추정 방법을 제안한다. 배터리 모델은 단순화된 테브난 등가회로 모델과 Runtime 모델이 결합되어 있고, Runtime 모델의 양단 전압을 이용하여 SOC를 추정한다. 제안된 알고리즘은 시뮬레이션을 통해 그 타당성이 검증된다.

• Journal of IKEEE
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• v.24 no.1
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• pp.333-338
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• 2020

This paper proposes a method of estimating the SOC(State of Charge) of a battery cell using a neural network algorithm. To this, we implement a battery SOC estimation simulator and derive input and output data for neural network learning through charge and discharge experiments at various temperatures. Finally, the performance of the battery SOC estimation is analyzed by comparing with the experimental value by Ah-counting using Matlab/Simulink program and confirmed that the error rate can be reduced to less than 3%.

• Journal of Power Electronics
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• v.12 no.1
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• pp.1-9
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• 2012

This study investigates an application of the Hamming network-dual extended Kalman filter (DEKF) based on pattern recognition for high accuracy state-of-charge (SOC)/capacity estimation and state-of-health (SOH) prediction at various temperatures. The averaged nine discharging/charging voltage-temperature (DCVT) patterns for ten fresh Li-Ion cells at experimental temperatures are measured as representative patterns, together with cell model parameters. Through statistical analysis, the Hamming network is applied to identify the representative pattern that matches most closely with the pattern of an arbitrary cell measured at any temperature. Based on temperature-checking process, model parameters for a representative DCVT pattern can then be applied to estimate SOC/capacity and to predict SOH of an arbitrary cell using the DEKF. This avoids the need for repeated parameter measuremet.

• Proceedings of the KIPE Conference
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• 2016.07a
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• pp.309-310
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• 2016

Sealed flooded lead acid batteries are becoming popular in the industry because of their low cost as compared to their counterparts. State of Charge (SOC) estimation has always been an important factor in battery management systems. For the accurate SOC estimation, open circuit voltage (OCV) hysteresis should be modelled accurately. The hysteresis phenomenon of the sealed flooded lead acid battery is discussed in detail and its ultimate modeling is proposed based on the conventional parallelogram method. The SOC estimation is performed by using Unscented Kalman Filter (UKF) while the parameters of the battery are estimated using Auto Regressive with external input (ARX) method. The validity of the proposed method is verified by the experimental results. The SOC estimation error by the proposed method is less than 3 % all wing the 125hr test.

• Proceedings of the KIPE Conference
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• 2018.07a
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• pp.156-158
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• 2018

The inconsistencies between paralleled battery cells are becoming more considerable issue in high capacity battery applications like electric vehicles. Due to differences in state-of-charge (SOC) and internal resistance within individual cells in parallel, charging or discharging current is not appropriately balanced to each cell in terms of SOC, which may shorten the lifetime or sometimes cause safety issues. In this paper, an intelligent cell-balancing algorithm is proposed to overcome the inconsistency issue especially for paralleled battery cells. In this scheme, SOC information collected in the sub-BMS module is sent to the main-BMS module, where the number of parallel cells to be connected to DC bus is continuously updated based on the suggested SOC comparison rule. To verify the method, operation of the algorithm on 4 paralleled battery cells are simulated on Matlab/Simulink. The simulation result shows that the SOCs of paralleled cells are evenly redistributed. It is expected that the proposed algorithm provides high reliable and prolong the life cycle and working capacity of the battery pack.

• 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.3
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• pp.629-638
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• 2016

This paper proposes a State-of-Charge (SOC) balancing control of Battery Power Modules (BPMs) for a modularized battery for Electric Vehicles (EVs) without additional balancing circuits. The BPMs are substituted with the single converter in EVs located between the battery and the inverter. The BPM is composed of a two-phase interleaved boost converter with battery modules. The discharge current of each battery module can be controlled individually by using the BPM to achieve a balanced state as well as increased utilization of the battery capacity. Also, an SOC balancing method is proposed to reduce the equalization time, which satisfies the regulation of a constant DC-link voltage and a demand of the output power. The proposed system and the SOC balancing method are verified through simulation and experiment.