• Proceedings of the KIPE Conference
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• 2019.11a
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• pp.70-72
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• 2019

For the safe and reliable operation of Lithium-ion batteries in Electric Vehicles (EVs) or Energy Storage Systems (ESSs), it is essential to have accurate information of the battery such as State of Charge (SOC). Many kinds of different techniques to estimate the SOC of the batteries have been developed so far such as the Kalman Filter. However, when it is applied to the multiple number of batteries it is difficult to maintain the accuracy of the estimation over all cells due to the difference in parameter value of each cell. Moreover the difference in the parameter of each cell may become larger 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 learn non-linear relationship of the voltage and current of the lithium-ion battery at different SOCs and different temperatures. In the training the voltage and current data of the Lithium battery at charge and discharge cycles obtained at different temperatures are used. After the comprehensive training with the data obtained with a cell resulting estimation algorithm is applied to the other cells. The experimental results show that the Mean Absolute Error (MAE) of the estimation is 0.56% at 25℃, and 3.16% at 60℃ with the proposed SOC estimation algorithm.

• 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.

• The Transactions of the Korean Institute of Power Electronics
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• v.25 no.4
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• pp.303-310
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• 2020

Estimating the accurate internal state of lithium ion batteries to increase their safety and efficiency is crucial. Various algorithms are used to estimate the internal state of a lithium ion battery, such as the extended Kalman filter and sliding mode observer. A state-space model is essential in using algorithms to estimate the internal state of a battery. Two principal methods are used to express the state-space model, namely, continuous time and discrete time. In this work, the extended Kalman filter is employed to estimate the internal state of a battery. Moreover, this work presents and analyzes the estimation performance of algorithms consisting of a continuous time state-space model and a discrete time state-space model through static and dynamic profiles.

• The Transactions of the Korean Institute of Power Electronics
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• v.24 no.3
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• pp.139-146
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• 2019

As the number of electric vehicles (EVs) in Jejudo Island increases, the secondary use of EV batteries is becoming increasingly mandatory not only in reducing greenhouse gas emissions but also in promoting resource conservation. For the secondary use of EV batteries, their capacity and performance at the end of automotive service should be evaluated properly. In this study, the battery state information from the on-board diagnostics or OBD2 port was acquired in real time while driving three distinct routes in Jejudo Island, and then the battery operating characteristics were assessed with the driving routes. The route with higher altitude led to higher current output, i.e., higher C-rate, which would reportedly deteriorate state of health (SOH) faster. In addition, the SOH obtained from the battery management system (BMS) of a 2017 Kia Soul EV with a mileage of 55,000 km was 100.2%, which was unexpectedly high. This finding was confirmed by the SOH estimation based on the ratio of the current integral to the change in state of charge. The SOH larger than 100% can be attributed to the rated capacity that was lower than the nominal capacity in EV application. Therefore, considering the driving environment and understanding the SOH estimation process will be beneficial and necessary in evaluating the capacity and performance of retired batteries for post-vehicle applications.

• Journal of Power Electronics
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• v.16 no.2
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• pp.643-653
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• 2016

The conventional methods used to evaluate battery state-of-charge (SOC) cannot accommodate the chemistry nonlinearities, measurement inaccuracies and parameter perturbations involved in estimation systems. In this paper, an impedance-based equivalent circuit model has been constructed with respect to a LiFePO₄ battery by approximating the electrochemical impedance spectrum (EIS) with RC circuits. The efficiencies of approximating the EIS with RC networks in different series-parallel forms are first discussed. Additionally, the typical hysteresis characteristic is modeled through an empirical approach. Subsequently, a methodology incorporating an H-infinity observer designated for open-circuit voltage (OCV) observation and a hysteresis model developed for OCV-SOC mapping is proposed. Thereafter, evaluation experiments under FUDS and UDDS test cycles are undertaken with varying temperatures and different current-sense bias. Experimental comparisons, in comparison with the EKF based method, indicate that the proposed SOC estimator is more effective and robust. Moreover, test results on a group of Li-ion batteries, from different manufacturers and of different chemistries, show that the proposed method has high generalization capability for all the three types of Li-ion batteries.

• Journal of Power Electronics
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• v.11 no.2
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• pp.237-243
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• 2011

Lithium batteries are widely used in mobile electronic devices due to their higher voltage and energy density, lighter weight and longer life cycle when compared to other secondary batteries. In particular, a high demand for lithium batteries is expected for electric cars. In the case of the lithium batteries used in electric cars, driving distance must be calculated accurately and discharging should not be done below a level that makes it impossible to crank. Therefore, accurate information on the state-of-charge (SOC) becomes an essential element for reliable driving. In this paper, a novel method for estimating the SOC of lithium polymer batteries using AC impedance is proposed. In the proposed method, the parameters are extracted by fitting the measured impedance spectrum on an equivalent impedance model and the variation in the parameter values at each SOC is used to estimate the SOC. Also to shorten the long length of time required for the measurement of the impedance spectrum, a novel method is proposed that can extract the equivalent impedance model parameters of lithium polymer batteries with the impedance measured at only two specific frequencies. Experiments are conducted on lithium polymer batteries, with similar capacities, made by different manufacturers to prove the validity of the proposed method.

• Journal of the Korean Institute of Intelligent Systems
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• v.22 no.3
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• pp.300-304
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• 2012

This paper presents a nonlinear observer design method for SOC(state-of-charge) estimation of Lithium polymer battery cell. The dynamic equation of the battery cell is modeled as a simple RC electrical circuit with a nonlinear voltage source and the parameters are obtained via nonlinear optimization. Using the sum of squares decomposition, the observer gain is designed such that the error dynamics is asymptotically stable and the decay rate is below the prescribed value. In order to illustrate the performance of the observer, a computer simulation is performed using the experimental data with the UDDS(urban dynamometer driving schedule) current profile.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.22 no.6
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• pp.127-133
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• 2008

This paper presents the battery management system(BMS) for the optimum conditions of the lead-Acid battery in UPS. The proposed system control the currents and voltages of battery for optimum conditions to estimate the State Of Charge(SOC) in charge or discharge mode. It proved the performance and the algorithm for the estimation of SOC, through the experiments which using the charge and discharge tester and the field tests.

• Proceedings of the KIPE Conference
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• 2014.07a
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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.

• Proceedings of the KIPE Conference
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• 2019.07a
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• pp.204-206
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• 2019

본 논문은 이중 확장 칼만 필터를 통한 SOC (State of charge) 및 용량 추정과 배터리 모델 파라미터를 이용한 폐배터리의 최대 출력을 추정하는 방법을 연구 및 제안한다. 배터리의 단순 전압 측정을 통해 상태를 진단할 경우, 부하 조건에 따라 급격한 전압 상승 및 강하로 인해 정밀한 안전 진단 및 운용에 어려움이 따르지만, 폐배터리는 일반 배터리에 비해 전압 변동율이 크기 때문에 상태 진단에 큰 어려움이 존재한다. 따라서 본 논문에서는 폐배터리의 정밀한 안전진단을 하기 위해 SOC 영역 및 충/방전에 따른 최대 출력을 계산하여 사전에 배터리의 상태를 진단할 수 있는 알고리즘을 제안한다. 또한, 배터리의 노화도에 따른 최대 출력을 실험 및 시뮬레이션을 통해 결과를 제시하여 유효한 방식임을 검증한다.