• Title, Summary, Keyword: Battery State Estimation

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Battery Cell SOC Estimation Using Neural Network (뉴럴 네트워크를 이용한 배터리 셀 SOC 추정)

  • Ryu, Kyung-Sang;Kim, Ho-Chan
    • 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%.

Review on State of Charge Estimation Methods for Li-Ion Batteries

  • Zhang, Xiaoqiang;Zhang, Weiping;Li, Hongyu;Zhang, Mao
    • Transactions on Electrical and Electronic Materials
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    • v.18 no.3
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    • pp.136-140
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    • 2017
  • The state of charge (SOC) is an important parameter in a battery-management system (BMS), and is very significant for accurately estimating the SOC of a battery. Li-ion batteries boast of excellent performance, and can only remain at their best working state by means of accurate SOC estimation that gives full play to their performances and raises their economic benefits. This paper summarizes some measures taken in SOC estimation, including the discharge experiment method, the ampere-hour integral method, the open circuit voltage method, the Kalman filter method, the neural network method, and electrochemical impedance spectroscopy (EIS. The principles of the various SOC estimation methods are introduced, and their advantages and disadvantages, as well as the working conditions adopted during these methods, are discussed and analyzed.

Modeling and State Observer Design of HEV Li-ion Battery (하이브리드 전기자동차용 리튬이온 배터리 모델링 및 상태 관측기 설계)

  • Kim, Ho-Gi;Heo, Sang-Jin;Kang, Gu-Bae
    • The Transactions of the Korean Institute of Power Electronics
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    • v.13 no.5
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    • pp.360-368
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    • 2008
  • 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 the frequency domain are analyzed with the parameters obtained from different conditions. The parameterized model of a Li-ion battery indicates highly dependent of temperatures. To estimate SOC and polarization voltage, a Luenberger state observer is utilized. The P- or PI-gains of observer based on a suitable natural frequency and damping ratio is adopted for the state estimation. Satisfactory estimation accuracy of output voltage and SOC is especially obtained by a PI-gain. The feasibility of the proposed estimation method is verified through experiment under the conditions of different C-rates, SOCs and temperatures.

EV Battery State Estimation using Real-time Driving Data from Various Routes (전기차 주행 데이터에 의한 경로별 배터리 상태 추정)

  • Yang, Seungmoo;Kim, Dong-Wan;Kim, Eel-Hwan
    • 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.

LiPB Battery SOC Estimation Using Extended Kalman Filter Improved with Variation of Single Dominant Parameter

  • Windarko, Novie Ayub;Choi, Jae-Ho
    • Journal of Power Electronics
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    • v.12 no.1
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    • pp.40-48
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    • 2012
  • This paper proposes the State-of-charge (SOC) estimator of a LiPB Battery using the Extended Kalman Filter (EKF). EKF can work properly only with an accurate model. Therefore, the high accuracy electrical battery model for EKF state is discussed in this paper, which is focused on high-capacity LiPB batteries. The battery model is extracted from a single cell of LiPB 40Ah, 3.7V. The dynamic behavior of single cell battery is modeled using a bulk capacitance, two series RC networks, and a series resistance. The bulk capacitance voltage represents the Open Circuit Voltage (OCV) of battery and other components represent the transient response of battery voltage. The experimental results show the strong relationship between OCV and SOC without any dependency on the current rates. Therefore, EKF is proposed to work by estimating OCV, and then is converted it to SOC. EKF is tested with the experimental data. To increase the estimation accuracy, EKF is improved with a single dominant varying parameter of bulk capacitance which follows the SOC value. Full region of SOC test is done to verify the effectiveness of EKF algorithm. The test results show the error of estimation can be reduced up to max 5%SOC.

State-of-charge Estimation for Lithium-ion Battery using a Combined Method

  • Li, Guidan;Peng, Kai;Li, Bin
    • 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.

SOC Observer based on Piecewise Linear Modeling for Lithium-Polymer Battery (구간선형 모델링 기반의 리튬-폴리머 배터리 SOC 관측기)

  • Chung, Gyo-Bum
    • The Transactions of the Korean Institute of Power Electronics
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    • v.20 no.4
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    • pp.344-350
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    • 2015
  • A battery management system requires accurate information on the battery state of charge (SOC) to achieve efficient energy management of electric vehicle and renewable energy systems. Although correct SOC estimation is difficult because of the changes in the electrical characteristics of the battery attributed to ambient temperature, service life, and operating point, various methods for accurate SOC estimation have been reported. On the basis of piecewise linear (PWL) modeling technique, this paper proposes a simple SOC observer for lithium-polymer batteries. For performance evaluation, the SOC estimated by the PWL SOC observer, the SOC measured by the battery-discharging experiment and the SOC estimated by the extended Kalman filter (EKF) estimator were compared through a PSIM simulation study.

Continuous Time and Discrete Time State Equation Analysis about Electrical Equivalent Circuit Model for Lithium-Ion Battery (리튬 이온 전지의 전기적 등가 회로에 관한 연속시간 및 이산시간 상태방정식 연구)

  • Han, Seungyun;Park, Jinhyeong;Park, Seongyun;Kim, Seungwoo;Lee, Pyeong-Yeon;Kim, Jonghoon
    • 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.

Dual EKF-Based State and Parameter Estimator for a LiFePO4 Battery Cell

  • Pavkovic, Danijel;Krznar, Matija;Komljenovic, Ante;Hrgetic, Mario;Zorc, Davor
    • Journal of Power Electronics
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    • v.17 no.2
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    • pp.398-410
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    • 2017
  • This work presents the design of a dual extended Kalman filter (EKF) as a state/parameter estimator suitable for adaptive state-of-charge (SoC) estimation of an automotive lithium-iron-phosphate ($LiFePO_4$) cell. The design of both estimators is based on an experimentally identified, lumped-parameter equivalent battery electrical circuit model. In the proposed estimation scheme, the parameter estimator has been used to adapt the SoC EKF-based estimator, which may be sensitive to nonlinear map errors of battery parameters. A suitable weighting scheme has also been proposed to achieve a smooth transition between the parameter estimator-based adaptation and internal model within the SoC estimator. The effectiveness of the proposed SoC and parameter estimators, as well as the combined dual estimator, has been verified through computer simulations on the developed battery model subject to New European Driving Cycle (NEDC) related operating regimes.

Accurate State of Charge Estimation of LiFePO4 Battery Based on the Unscented Kalman Filter and the Particle Filter (언센티드 칼만 필터와 파티클 필터에 기반한 리튬 인산철 배터리의 정확한 충전 상태 추정)

  • Nguyen, Thanh-Tung;Awan, Mudassir Ibrahim;Choi, Woojin
    • Proceedings of the KIPE Conference
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    • pp.126-127
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    • 2017
  • An accurate State Of Charge (SOC) estimation of battery is the most important technique for Electric Vehicles (EVs) and Energy Storage Systems (ESSs). In this paper a new integrated Unscented Kalman Filter-Particle Filter (UKF-PF) is employed to estimate the SOC of a $LiFePO_4$ battery cell and a significant improvement is obtained as compared to the other methods. The parameters of the battery is modeled by the second order Auto Regressive eXogenous (ARX) model and estimated by using Recursive Least Square (RLS) method to calculate value of each element in the model. The proposed algorithm is established by combining a parameter identification technique using RLS method with ARX model and an SOC estimation technique using UKF-PF.

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