• Title, Summary, Keyword: Battery State Estimation

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State-of-charge Estimation for Lithium-ion Batteries Using a Multi-state Closed-loop Observer

  • Zhao, Yulan;Yun, Haitao;Liu, Shude;Jiao, Huirong;Wang, Chengzhen
    • Journal of Power Electronics
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    • v.14 no.5
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    • pp.1038-1046
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    • 2014
  • Lithium-ion batteries are widely used in hybrid and pure electric vehicles. State-of-charge (SOC) estimation is a fundamental issue in vehicle power train control and battery management systems. This study proposes a novel model-based SOC estimation method that applies closed-loop state observer theory and a comprehensive battery model. The state-space model of lithium-ion battery is developed based on a three-order resistor-capacitor equivalent circuit model. The least square algorithm is used to identify model parameters. A multi-state closed-loop state observer is designed to predict the open-circuit voltage (OCV) of a battery based on the battery state-space model. Battery SOC can then be estimated based on the corresponding relationship between battery OCV and SOC. Finally, practical driving tests that use two types of typical driving cycle are performed to verify the proposed SOC estimation method. Test results prove that the proposed estimation method is reasonably accurate and exhibits accuracy in estimating SOC within 2% under different driving cycles.

The State of Charge Estimation for Lithium-Polymer Battery using a PI Observer (PI 상태관측기를 이용한 리튬폴리머 배터리 SOC 추정)

  • Lee, Junwon;Jo, Jongmin;Kim, Sungsoo;Cha, Hanju
    • 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.

State Estimation Technique for VRLA Batteries for Automotive Applications

  • Duong, Van Huan;Tran, Ngoc Tham;Choi, Woojin;Kim, Dae-Wook
    • Journal of Power Electronics
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    • v.16 no.1
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    • pp.238-248
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    • 2016
  • The state-of-charge (SOC) and state-of-health (SOH) estimation of batteries play important roles in managing batteries for automotive applications. However, an accurate state estimation of a battery is difficult to achieve because of certain factors, such as measurement noise, highly nonlinear characteristics, strong hysteresis phenomenon, and diffusion effect of batteries. In certain vehicular applications, such as idle stop-start systems (ISSs), significant errors in SOC/SOH estimation may lead to a failure in restarting a combustion engine after the shut-off period of the engine when the vehicle is at rest, such as at a traffic light. In this paper, a dual extended Kalman filter algorithm with a dynamic equivalent circuit model of a lead-acid battery is proposed to deal with this problem. The proposed algorithm adopts a battery model by taking into account the hysteresis phenomenon, diffusion effect, and parameter variations for accurate state estimations of the battery. The validity of the proposed algorithm is verified through experiments by using an absorbed glass mat valve-regulated lead-acid battery and a battery sensor cable for commercial ISS vehicles.

Battery State of Charge Estimation Considering the Battery Aging (배터리의 노화 상태를 고려한 배터리 SOC 추정)

  • Lee, Seung-Ho;Park, Min-Kee
    • Journal of IKEEE
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    • v.18 no.3
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    • pp.298-304
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    • 2014
  • Proper operation of the battery powered systems depends on the accuracy of the battery SOC(State of Charge) estimation, therefore it is critical for those systems that SOC is accurately determined. The SOC of the battery is related to the battery aging and the SOC estimation methods without considering the aging of the battery are not accurate. In this paper, a new method that accurately estimate the SOC of the battery is proposed considering the aging of the battery. A mathematical model for the Battery SOC-OCV(Open Circuit Voltage) relationship is presented using Boltzmann equation and aging indicator is defined, and then the SOC is estimated combining the mathematical model and aging indicator. The proposed method takes the aging of the battery into consideration, which leads to an accurate estimation of the SOC. The simulations and experiments show the effectiveness of the proposed method for improving the accuracy of the SOC estimation.

A Novel Battery State of Health Estimation Method Based on Outlier Detection Algorithm

  • Piao, Chang-hao;Hu, Zi-hao;Su, Ling;Zhao, Jian-fei
    • Journal of Electrical Engineering and Technology
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    • v.11 no.6
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    • pp.1802-1811
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    • 2016
  • A novel battery SOH estimation algorithm based on outlier detection has been presented. The Battery state of health (SOH) is one of the most important parameters that describes the usability state of the power battery system. Firstly, a battery system model with lifetime fading characteristic was established, and the battery characteristic parameters were acquired from the lifetime fading process. Then, the outlier detection method based on angular distribution was used to identify the outliers among the battery behaviors. Lastly, the functional relationship between battery SOH and the outlier distribution was obtained by polynomial fitting method. The experimental results show that the algorithm can identify the outliers accurately, and the absolute error between the SOH estimation value and true value is less than 3%.

Battery State Estimation Algorithm for High-Capacity Lithium Secondary Battery for EVs Considering Temperature Change Characteristics

  • Park, Jinho;Lee, Byoungkuk;Jung, Do-Yang;Kim, Dong-Hee
    • Journal of Electrical Engineering and Technology
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    • v.13 no.5
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    • pp.1927-1934
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    • 2018
  • In this paper, we studied the state of charge (SOC) estimation algorithm of a high-capacity lithium secondary battery for electric vehicles (EVs) considering temperature characteristics. Nonlinear characteristics of high-capacity lithium secondary batteries are represented by differential equations in the mathematical form and expressed by the state space equation through battery modeling to extract the characteristic parameters of the lithium secondary battery. Charging and discharging equipment were used to perform characteristic tests for the extraction of parameters of lithium secondary batteries at various temperatures. An extended Kalman filter (EKF) algorithm, a state observer, was used to estimate the state of the battery. The battery capacity and internal resistance of the high-capacity lithium secondary battery were investigated through battery modeling. The proposed modeling was applied to the battery pack for EVs to estimate the state of the battery. We confirmed the feasibility of the proposed study by comparing the estimated SOC values and the SOC values from the experiment. The proposed method using the EKF is expected to be highly applicable in estimating the state of the high-capacity rechargeable lithium battery pack for electric vehicles.

Virtual Environment Modeling for Battery Management System

  • Piao, Chang-Hao;Yu, Qi-Fan;Duan, Chong-Xi;Su, Ling;Zhang, Yan
    • Journal of Electrical Engineering and Technology
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    • v.9 no.5
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    • pp.1729-1738
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    • 2014
  • The offline verification of state of charge estimation, power estimation, fault diagnosis and emergency control of battery management system (BMS) is one of the key technologies in the field of electric vehicle battery system. It is difficult to test and verify the battery management system software in the early stage, especially for algorithms such as system state estimation, emergency control and so on. This article carried out the virtual environment modeling for verification of battery management system. According to the input/output parameters of battery management system, virtual environment is determined to run the battery management system. With the integration of the developed BMS model and the external model, the virtual environment model has been established for battery management system in the vehicle's working environment. Through the virtual environment model, the effectiveness of software algorithm of BMS was verified, such as battery state parameters estimation, power estimation, fault diagnosis, charge and discharge management, etc.

Secondary Battery SOC Estimation Technique for an Autonomous System Based on Extended Kalman Filter (자율이동체를 위한 2차 전지의 확장칼만필터에 기초한 SOC 추정 기법)

  • Jeon, Chang-Wan;Lee, Yu-Mi
    • 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.

A Novel Sliding Mode Observer for State of Charge Estimation of EV Lithium Batteries

  • Chen, Qiaoyan;Jiang, Jiuchun;Liu, Sijia;Zhang, Caiping
    • Journal of Power Electronics
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    • v.16 no.3
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    • pp.1131-1140
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    • 2016
  • A simple design for a sliding mode observer is proposed for EV lithium battery SOC estimation in this paper. The proposed observer does not have the limiting conditions of existing observers. Compared to the design of previous sliding mode observers, the new observer does not require a solving matrix equation and it does not need many observers for all of the state components. As a result, it is simple in terms of calculations and convenient for engineering applications. The new observer is suitable for both time-variant and time-invariant models of battery SOC estimation, and the robustness of the new observer is proved by Liapunov stability theorem. Battery tests are performed with simulated FUDS cycles. The proposed observer is used for the SOC estimation on both unchanging parameter and changing parameter models. The estimation results show that the new observer is robust and that the estimation precision can be improved base on a more accurate battery model.

Implementation of Battery 'State of Charge' Estimation algorithm (배터리 'State of Charge' 예측 알고리즘 구현)

  • Kim, Yong-Ho;Kim, Dae-Hwan
    • Journal of The Institute of Information and Telecommunication Facilities Engineering
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    • v.10 no.1
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    • pp.27-32
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    • 2011
  • These days more electric devices are implemented in car, and more accurate estimation of SoC is required. OCV with current integration and Internal Resistance is essential method of Battery SoC Estimation. In this paper we propose OCV with current integration method and compare with Internal Resistance method. In OCV with current integration method estimation error was less than average 2%, but requires more than 5 minutes to stabilize OCV. If Stop and Running conditions are change frequently, estimation error will increase. In Internal resistance Modeling method, in high SoC state, estimation error was more than 15%, and in low SoC state, estimation error was less than 8%.

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