• Title/Summary/Keyword: Battery model

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Impedance-based generalized and phenomenon-reflective simulation model of Li-ion battery for railway traction applications

  • Abbas, Mazhar;Cho, Inho;Kim, Jonghoon
    • Proceedings of the KIPE Conference
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    • 2019.07a
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    • pp.459-460
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    • 2019
  • The performance dynamics of battery is very sensitive to operating conditions (i.e temperature, load current, and state of charge). A model developed based on certain conditions may perform well under the similar conditions but can not accurately predict the performance for changing conditions. Thus, a generalized model is needed which can accurately emulate the battery dynamic behavior under all conditions. In addition, the components of the model should relate to the physicochemical processes that occur inside the battery. Electrochemical impedance curve shows better visible reflection of the processes inside battery as compared to voltage curve. The model trained for parameterization using neural network has better generalization than simple curve fitting. Thus, this study proposes recurrent neural network based parameterization of the Lithium ion battery model followed by impedance based identification.

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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.

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.

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.

A Study on the Basic Model for Simulating Performance of Thermal-Batteries (열전지 성능 시뮬레이션을 위한 기초 모델에 대한 연구)

  • Ji, Hyun-Jin
    • Journal of the Korea Institute of Military Science and Technology
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    • v.11 no.1
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    • pp.102-111
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    • 2008
  • This paper describes the basic model and simulation results of thermal battery. Voltage and thermal analysis is a critical part of thermal-battery design because of the need to maintain the inner temperature above the electrolyte melting point. Traditionally, battery design has depended on an empirical approach, in which prototype batteries are outfitted with thermocouples and the design of subsequent batteries is refined accordingly. We have developed the basic model that allows the design engineer to configure or modify a battery, quickly conduct a thermal analysis, and efficiently review the results. Based on performance tests, the thermal-battery model was established and the effect of design parameters on battery performance was analyzed.

A Study on Battery Simulator Including Aging and Dynamic Impedance Model (노화 및 동특성 임피던스 모델을 포함한 배터리 시뮬레이터에 관한 연구)

  • Lee, Jong-Hak;Kim, Soo-Bin;Oh, Sang-Keun;Song, Seung-Ho
    • The Transactions of the Korean Institute of Power Electronics
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    • v.25 no.3
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    • pp.171-180
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    • 2020
  • This paper presents the implementation and control methods of a battery simulator. The proposed battery simulator can emulate the dynamic characteristics of any actual battery using the second RC ladder model of the equivalent circuit. Moreover, it can emulate the variation of impedance, which is the result of the change of battery characteristics due to the aging effect. The parameters of the battery simulator can be derived from the sequence of tests of the actual battery or only from the data supplied by the battery manufacturer. Proposed methods for the battery simulator are tested by extensive experiments. Test results show that the proposed battery simulator can emulate not only the dynamic characteristics but also the aging effects of the actual battery in real time.

A Study on the PSCAD/EMTDC Simulation Model of Battery Energy Storage with Simplified Battery Model and IUIa Charging Method (간략화된 배터리 모델이 적용된 IUIa 충전 방식의 에너지 저장장치의 PSCAD/EMTDC 시뮬레이션 모델에 관한 연구)

  • Kim, Sung-Hyun;Lee, Kye-Byung;Hong, Jun-Hee;Son, Kwang-Myung
    • Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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    • v.24 no.12
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    • pp.84-90
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    • 2010
  • In order to level electric power of the photovoltaic and wind-turbine system and ensure fast response of the fuel-cell and micro-turbine, the energy storage is required in the microgrid system. In this paper, a simplified simulation model of the battery energy storage for charging method with IUIa is developed using PSCAD/EMTDC. The model consists of e.m.f.(electromotive force), internal resistor and overvoltage capacitor. A method for deciding parameters of the model on a case-by-case basis is proposed. The developed model can be used in the simulation of a complicated system such as a microgrid system.

THE OPEN-CIRCUIT VOLTAGE STATE ESTIMATION OF THE BATTERY

  • LEE, SHINWON
    • Journal of applied mathematics & informatics
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    • v.39 no.5_6
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    • pp.805-811
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    • 2021
  • Currently, batteries use commonly as energy sources for mobile electric devices. Due to the high density of energy, the energy storage state of a battery is very important information. To know the battery's energy storage state, it is necessary to find out the open state voltage of the battery. The open state voltage calculates with a mathematical model, but the computation of the real time state is complicated and requires many calculations. Therefore, the state observer designs to estimate in real time the battery open-circuit voltage as disturbance including model error. Using the estimated open voltage and applying it to the state estimation algorithm, we can estimate the charge. In this study, we first estimate the open-circuit voltage and design an estimation algorithm for estimating the state of battery charge. This includes errors in the system model and has a robust characteristic to noise. It is possible to increase the precision of the charge state estimation.

Battery Response Characteristics According to System Modeling and Driving Environment of Electric Vehicles (전기자동차 시스템 모델링 및 주행 환경에 따른 배터리 응답 특성 연구)

  • Chu, Yong-Ju;Park, Jun-Young;Park, Gwang-Min;Lee, Seung-Yop
    • IEMEK Journal of Embedded Systems and Applications
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    • v.17 no.2
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    • pp.85-92
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    • 2022
  • Currently, various researches on electric vehicle battery systems have been conducted from the viewpoint of safety and performance for SoC, SoH, etc. However, it is difficult to build a precise electrical model of a battery system based on the chemical reaction and SoC prediction. Experimental measurements and predictions of the battery SoC were usually performed using dynamometers. In this paper, we construct a simulation model of an electric vehicle system using Matlab Simulink, and confirm the response characteristics based on the vehicle test driving profiles. In addition, we show that it is possible to derive the correlation between the SoC, voltage, and current of the battery according to the driving time of the electric vehicle in conjunction with the BMS model.

Empirical Capacity Degradation Model for a Lithium-Ion Battery Based on Various C-Rate Charging Conditions

  • Dong Hyun Kim;Juhyung Lee;Kyungseop Shin;Kwang-Bum Kim;Kyung Yoon Chung
    • Journal of Electrochemical Science and Technology
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    • v.15 no.3
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    • pp.414-420
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    • 2024
  • Lithium-ion batteries are widely used in many applications due to their high energy density, high efficiency, and excellent cycle ability. Once an unknown Li-ion battery is reusable, it is important to measure its lifetime and state of health. The most favorable measurement method is the cycle test, which is accurate but time- and capacity-consuming. In this study, instead of a cycle test, we present an empirical model based on the C-rate test to understand the state of health of the battery in a short time. As a result, we show that the partially accelerated charge/discharge condition of the Li-ion battery is highly effective for the degradation of battery capacity, even when half of the charge/discharge conditions are the same. This observation provides a measurable method for predicting battery reuse and future capacity degradation.