• Title/Summary/Keyword: Battery model

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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.

Parameter Identification of 3R-C Equivalent Circuit Model Based on Full Life Cycle Database

  • Che, Yanbo;Jia, Jingjing;Yang, Yuexin;Wang, Shaohui;He, Wei
    • Journal of Electrical Engineering and Technology
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    • v.13 no.4
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    • pp.1759-1768
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    • 2018
  • The energy density, power density and ohm resistance of battery change significantly as results of battery aging, which lead to decrease in the accuracy of the equivalent model. A parameter identification method of the equivale6nt circuit model with 3 R-C branches based on the test database of battery life cycle is proposed in this paper. This database is built on the basis of experiments such as updating of available capacity, charging and discharging tests at different rates and relaxation characteristics tests. It can realize regular update and calibration of key parameters like SOH, so as to ensure the reliability of parameters identified. Taking SOH, SOC and T as independent variables, lookup table method is adopted to set initial value for the parameter matrix. Meanwhile, in order to ensure the validity of the model, the least square method based on variable forgetting factor is adopted for optimizing to complete the identification of equivalent model parameters. By comparing the simulation data with measured data for charging and discharging experiments of Li-ion battery, the effectiveness of the full life cycle database and the model are verified.

Learning Data Model Definition and Machine Learning Analysis for Data-Based Li-Ion Battery Performance Prediction (데이터 기반 리튬 이온 배터리 성능 예측을 위한 학습 데이터 모델 정의 및 기계학습 분석 )

  • Byoungwook Kim;Ji Su Park;Hong-Jun Jang
    • KIPS Transactions on Software and Data Engineering
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    • v.12 no.3
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    • pp.133-140
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    • 2023
  • The performance of lithium ion batteries depends on the usage environment and the combination ratio of cathode materials. In order to develop a high-performance lithium-ion battery, it is necessary to manufacture the battery and measure its performance while varying the cathode material ratio. However, it takes a lot of time and money to directly develop batteries and measure their performance for all combinations of variables. Therefore, research to predict the performance of a battery using an artificial intelligence model has been actively conducted. However, since measurement experiments were conducted with the same battery in the existing published battery data, the cathode material combination ratio was fixed and was not included as a data attribute. In this paper, we define a training data model required to develop an artificial intelligence model that can predict battery performance according to the combination ratio of cathode materials. We analyzed the factors that can affect the performance of lithium-ion batteries and defined the mass of each cathode material and battery usage environment (cycle, current, temperature, time) as input data and the battery power and capacity as target data. In the battery data in different experimental environments, each battery data maintained a unique pattern, and the battery classification model showed that each battery was classified with an error of about 2%.

A Modeling for Li-Ion Battery Performance Analysis of GEO Satellite (정지궤도 인공위성 리튬-이온 배터리 성능 해석을 위한 모델링)

  • Koo, Ja-Chun;Ra, Sung-Woong
    • Journal of the Korean Society for Aeronautical & Space Sciences
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    • v.42 no.2
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    • pp.150-157
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    • 2014
  • Li-Ion battery is used in the most satellites now due to advantages such as weight, thermal dissipation and self discharge compared to the previous generations of electrochemical batteries. The performance analysis model of the Li-Ion battery is needed to aid the design of new satellite electrical power subsystem. This paper develops the performance analysis model of the Li-Ion battery to apply to the electrical power subsystem design and energy balance analysis on geostationary orbit. The analysis model receives the satellite bus power, solar array power and battery temperature and gives the battery voltage, charge and discharge currents, taper index, state of charge and power dissipation. The results from the performance analysis are compared and analyzed with the flight data to verify the model. The compared results show satisfactory without significant difference with the flight data.

A CHARGER/DISCHARGER FOR MODELING OF SERIAL/PARALLEL CONNECTED NI-MH BATTERY

  • Heo, Min-Ho;Ahn, Jae-Young;Kim, Kwang-Heon
    • Proceedings of the KIPE Conference
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    • 1998.10a
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    • pp.554-559
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    • 1998
  • Equalizing the state of charge of cell that affects the charge/discharge quality and efficiency of the battery through the charge/discharge characteristic experiments of battery source, we develope the high efficiency charge/discharge system which would be used in serial HEV with the constant engine-generator output. For this, establishes the electrical model of Ni-MH battery appropriate to the high efficiency charge/discharge conditions. There is no model of Ni-MH cell, so we used Ni-Cd model and obtain the Ni-MH model through the experiment. A reason that each cell has the same charge/discharge property for applying the cell model to serial/parallel connected battery source extensively is needed. Therefore, in this paper, propose the Ni-MH charger/discharger has the equalization charging function and selectable cut-off function.

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Development and Empirical Validation of an Electric Vehicle Battery Consumption Analysis Model (전기차 배터리 소모량 분석모형 개발 및 실증)

  • In-Seon Suh;Young-Mi Lee;Sang-Yul Oh;Myeong-Chang Gwak;Hyeon-Ji Lee
    • Journal of Environmental Science International
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    • v.33 no.7
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    • pp.523-532
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    • 2024
  • In popular tourist destinations such as Jeju and Gangwon, electric rental cars are increasingly adopted. However, sudden battery drain due to weather conditions can pose safety issues. To address this, we developed a battery consumption analysis model that considers resistive energy factors such as acceleration, rolling resistance, and aerodynamic drag. Focusing on the effects of ambient temperature and wind speed, the model's performance was evaluated during an empirical validation period from November to December 2023. Comparing predicted and actual state of charge (SoC) across different routes identified ambient temperature, wind speed, and driving time as major sources of error. The mean absolute error (MAE) increased with lower temperatures due to reduced battery efficiency. Higher wind speeds on routes 1 and 6 resulted in larger errors, indicating the model's limitation in considering only tailwinds for aerodynamic drag calculations. Additionally, longer driving times led to higher actual SoC than predicted, suggesting the need to account for varying driver habits influenced by road conditions. Our model, providing more accurate SoC predictions to prevent battery depletion incidents, shows high potential for application in navigation apps for electric vehicle users in tourist areas. Future research should endeavor to the model by including wind direction, HVAC system usage, and braking frequency to improve prediction accuracy further.

Heat transfer analysis in the battery tray for electirc vehicle (전기자동차 배터리 트레이 내에서의 열전달 해석)

  • Lim Jongsoo;shin Dongshin
    • Proceedings of the KSME Conference
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    • 2002.08a
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    • pp.651-654
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    • 2002
  • Study of electric vehicle is popular with automobile company. However, battery cooling problem has delayed development of electric vehicle. Lifetime of electric vehicle's battery depends on the cooling effect for the battery tray. One model was simulated by 3-D, steady state, incompressible, k-e turbulent model simulation. It is found that flow inlet, outlet and inlet position are very important design parameters.

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Mobile Device Battery Consumption Analysis Techniques: Evaluation and Future Direction (모바일 디바이스 배터리 소모 분석 기법: 평가 및 발전 방향 제고)

  • Song, Jiyoung;Cho, Chiwoo;Jung, Youlim;Jee, Eunkyoung;Bae, Doo-Hwan
    • Journal of Software Engineering Society
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    • v.27 no.1
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    • pp.1-7
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    • 2018
  • The consumption of mobile device batteries which are limited resources is an important criterion when circuit designers analyze and evaluate circuits. For this reason, researchers conducted researches with different models of battery consumption to analyze power consumption of mobile devices. The battery consumption model generation techniques have various characteristics depending on availability of sensors, run-time model generation, and models for using in verification and testing. However, there is lack of comparison and analysis between varied battery consumption model generation methods. In this research, we compare and evaluate the analysis methods which have been studied so far to support the circuit investigation for circuit designers. Finally, we suggest the direction of researches in battery consumption analysis using the comparison result.

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Two Machine Learning Models for Mobile Phone Battery Discharge Rate Prediction Based on Usage Patterns

  • Chantrapornchai, Chantana;Nusawat, Paingruthai
    • Journal of Information Processing Systems
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    • v.12 no.3
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    • pp.436-454
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    • 2016
  • This research presents the battery discharge rate models for the energy consumption of mobile phone batteries based on machine learning by taking into account three usage patterns of the phone: the standby state, video playing, and web browsing. We present the experimental design methodology for collecting data, preprocessing, model construction, and parameter selections. The data is collected based on the HTC One X hardware platform. We considered various setting factors, such as Bluetooth, brightness, 3G, GPS, Wi-Fi, and Sync. The battery levels for each possible state vector were measured, and then we constructed the battery prediction model using different regression functions based on the collected data. The accuracy of the constructed models using the multi-layer perceptron (MLP) and the support vector machine (SVM) were compared using varying kernel functions. Various parameters for MLP and SVM were considered. The measurement of prediction efficiency was done by the mean absolute error (MAE) and the root mean squared error (RMSE). The experiments showed that the MLP with linear regression performs well overall, while the SVM with the polynomial kernel function based on the linear regression gives a low MAE and RMSE. As a result, we were able to demonstrate how to apply the derived model to predict the remaining battery charge.

A Study on Mathematical Modeling of Battery Energy Storage Systems using PSCAD/EMIDC (PSCAD/EMTDC를 이용한 전지전력저장시스템의 수리모형에 관한 연구)

  • Kim, Eung-Sang;Kim, Jae-Eun;Rho, Dae-Seok;Kim, Jae-Chul
    • Proceedings of the KIEE Conference
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    • 1997.07c
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    • pp.1035-1037
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    • 1997
  • This paper deals with the mathematical modeling of battery energy storage systems interconnected with the distribution system. This battery model takes account of self-discharge, battery storage capacity, internal resistance and overvoltage. The model components are decided by using an approximation technique and experimental results. This model can be used to evaluate battery performance of battery energy storage systems interconnected with distribution system.

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