• Title, Summary, Keyword: Battery State Estimation

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SOC Estimation Based on OCV for NiMH Batteries Using an Improved Takacs Model

  • Windarko, Novie Ayub;Choi, Jae-Ho
    • Journal of Power Electronics
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    • v.10 no.2
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    • pp.181-186
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    • 2010
  • This paper presents a new method for the estimation of State of Charge (SOC) for NiMH batteries. Among the conventional methods to estimate SOC, Coulomb Counting is widely used, but this method is not precise due to error integration. Another method that has been proposed to estimate SOC is by using a measurement of the Open Circuit Voltage (OCV). This method is found to be a precise one for SOC estimation. In NiMH batteries, the hysteresis characteristic of OCV is very strong compared to other type of batteries. Another characteristic of NiMH battery to be considered is that the OCV of a NiMH battery under discharging mode is lower than it is under charging mode. In this paper, the OCV is modeled by a simple method based on a hyperbolic function which well known as Takacs’s model. The OCV model is then used for SOC estimation. Although the model is simple, the error is within 10%.

A SOC Coefficient Factor Calibration Method to improve accuracy Of The Lithium Battery Equivalence Model (리튬 배터리 등가모델의 정확도 개선을 위한 SOC 계수 보정법)

  • Lee, Dae-Gun;Jung, Won-Jae;Jang, Jong-Eun;Park, Jun-Seok
    • Journal of the Institute of Electronics and Information Engineers
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    • v.54 no.4
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    • pp.99-107
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    • 2017
  • This paper proposes a battery model coefficient correction method for improving the accuracy of existing lithium battery equivalent models. BMS(battery management system) has been researched and developed to minimize shortening of battery life by keeping SOC(state of charge) and state of charge of lithium battery used in various industrial fields such as EV. However, the cell balancing operation based on the battery cell voltage can not follow the SOC change due to the internal resistance and the capacitor. Various battery equivalent models have been studied for estimation of battery SOC according to the internal resistance of the battery and capacitors. However, it is difficult to apply the same to all the batteries, and it tis difficult to estimate the battery state in the transient state. The existing battery electrical equivalent model study simulates charging and discharging dynamic characteristics of one kind of battery with error rate of 5~10% and it is not suitable to apply to actual battery having different electric characteristics. Therefore, this paper proposes a battery model coefficient correction algorithm that is suitable for real battery operating environments with different models and capacities, and can simulate dynamic characteristics with an error rate of less than 5%. To verify proposed battery model coefficient calibration method, a lithium battery of 3.7V rated voltage, 280 mAh, 1600 mAh capacity used, and a two stage RC tank model was used as an electrical equivalent model of a lithium battery. The battery charge/discharge test and model verification were performed using four C-rate of 0.25C, 0.5C, 0.75C, and 1C. The proposed battery model coefficient correction algorithm was applied to two battery models, The error rate of the discharge characteristics and the transient state characteristics is 2.13% at the maximum.

Research on the Re-Use of Electric Vehicle Battery for Energy Storage Systems (전기자동차 배터리의 에너지 저장장치로의 재사용에 관한 연구)

  • Vuand, Hai-Nam;Choi, Woojin
    • Proceedings of the KIPE Conference
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    • pp.345-346
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    • 2016
  • The grid-connected energy storage systems, which could increase the reliability, efficiency, and cleanliness of the grid is presently restricted by the high cost of batteries. This problems could be solved by batteries retired from automotive services. These batteries can provide a low-cost system for energy storage and other applications such as residential applications and renewable energy integration. This paper gives an overview of technical requirements for the re-use of the electric vehicle batteries in energy storage systems.Firstly, the motivation of research is introduced. Secondly, the technologies needed for the re-use of the battery are introduced such asidentification of the battery characteristics, grading of the aged batteries, identification of the state-of-charge and state-of-health of the battery and suitable power electronic converter topologies. In addition the control strategy to maximize the battery lifespan and bypass the faulty batteries is presented and one-stop solution to implement the above mentioned technologies are also given.

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A State-of-Charge estimation using extended Kalman filter for battery of electric vehicle (확장칼만필터를 이용한 전기자동차용 배터리 SOC 추정)

  • Ryu, Kyung-Sang;Kim, Byungki;Kim, Dae-Jin;Jang, Moon-seok;Ko, Hee-sang;Kim, Ho-Chan
    • Journal of the Korea Academia-Industrial cooperation Society
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    • v.18 no.10
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    • pp.15-23
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    • 2017
  • This paper reports a SOC(State-of-Charge) estimation method using the extended Kalman filter(EKF) algorithm, which can allow real-time implementation and reduce the error of the model and be robust against noise, to accurately estimate and evaluate the charging/discharging state of the EV(Electric Vehicle) battery. The battery was modeled as the first order Thevenin model for the EKF algorithm and the parameters were derived through experiments. This paper proposes the changed method, which can have the SOC to 0% ~ 100% regardless of the aging of the battery by replacing the rated capacity specified in the battery with the maximum chargeable capacity. In addition, This paper proposes the EKF algorithm to estimate the non-linearity interval of the battery and simulation result based on Ah-counting shows that the proposed algorithm reduces the estimation error to less than 5% in all intervals of the SOC.

A Novel SOC Estimation Method for Multiple Number of Lithium Batteries Using Deep Neural Network (딥 뉴럴 네트워크를 이용한 새로운 리튬이온 배터리의 SOC 추정법)

  • Khan, Asad;Ko, Young-hwi;Choi, Woojin
    • Proceedings of the KIPE Conference
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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.

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The Battery Management System for UPS Lead-Acid Battery (UPS용 납축전지를 위한 배터리관리시스템)

  • Seo, Cheol-Sik;Moon, Jong-Hyun;Park, Jae-Wook;Kim, Geum-Soo;Kim, Dong-Hee
    • 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.

Development of Low Cost, High-Performance Miniaturized Lithium-ion Battery Tester Using Raspberry Pi Zero

  • La, Phuong-Ha;Im, Hwi-Yeol;Choi, Sung-Jin
    • Proceedings of the KIPE Conference
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    • pp.47-48
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    • 2017
  • This paper presents a low-cost portable lithium battery parameter measuring and estimating the solution. In this method, lithium battery characteristics are monitored during discharging and charging cycles. The battery profile is analyzed, and its key parameters are estimated by GNU Octave running on Raspberry Pi Zero, a mini computer. The proposed method can measure and estimate the battery parameters for SOC and DOD estimation with reasonable accuracy as well as portability features.

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A Nonlinear Observer Design for Estimating State-of-Charge of Lithium Polymer Battery (리튬폴리머 배터리 잔존충전용량 추정을 위한 비선형 관측기 설계)

  • Yoo, Seog-Hwan
    • Journal of 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.

Modeling and Characteristic Analysis of HEV Li-ion Battery Using Recursive Least Square Estimation (최소 자승법을 이용한 하이브리드용 리튬이온 배터리 모델링 및 특성분석)

  • Kim, Ho-Gi;Heo, Sang-Jin;Kang, Gu-Bae
    • Transactions of the Korean Society of Automotive Engineers
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    • v.17 no.1
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    • pp.130-136
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    • 2009
  • A lumped parameter model of Li-ion battery in hybrid electric vehicle(HEV) is constructed and system parameters are identified by using recursive least square estimation for different C-rates, SOCs and temperatures. The system characteristics of pole and zero in frequency domain are analyzed with the parameters obtained from different conditions. The parameterized model of Li-ion battery indicates highly dependant of temperatures. The system pole and internal resistance changes 6.6 and 18 times at $-20^{\circ}C$, comparing with those at $25^{\circ}C$, respectively. These results will be utilized on constructing model-based state observer or an on-line identification and an adaptation of the model parameters in battery management systems for hybrid electric vehicle applications.

A Study on the Battery Management System for the optimum conditions of the battery in UPS (UPS용 배터리 최적화를 위한 배터리관리시스템에 관한 연구)

  • Moon, Jong-Hyun;Seo, Cheol-Sik;Park, Jae-Wook;Kim, Geum-Soo;Kim, Dong-Hee
    • Proceedings of the Korean Institute of IIIuminating and Electrical Installation Engineers Conference
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    • pp.321-324
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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 controls the over and under currents of battery for protecting and it was applied algorithm for optimum conditions to estimate the State Of Charge(SOC) in charge or discharge mode. It approved the performance and the algorithm for the estimation of SOC, through the experiments which using the charge and discharge tester and the field tests.

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