The Experts below are selected from a list of 69477 Experts worldwide ranked by ideXlab platform
Stephan Silvestre - One of the best experts on this subject based on the ideXlab platform.
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Dynamic Battery Model for photovoltaic applications
Progress in Photovoltaics: Research and Applications, 2003Co-Authors: Daniel Guasch, Stephan SilvestreAbstract:The main purpose of this paper is to offer a useful Battery Model for simulation of stand-alone photovoltaic applications. At present, many Models for Battery behaviour simulation are available. Owing to the complex response of this element, these Models are mainly concerned with stationary working point conditions. This paper presents an enhancement of a generic Battery Model, achieving a dynamic Battery Model for photovoltaic applications. It includes the use of automatic parameter extraction techniques and the solving of numerical calculation problems. It also introduces some new concepts, such as the maximum available capacity and the level of energy. Finally, a Battery state of health estimation, has been added to complete the Battery behaviour description, including non-ideal effects, such as Battery capacity reduction and self-discharging current. Copyright (C) 2003 John Wiley Sons, Ltd.
Gabriel A. Rincon-mora - One of the best experts on this subject based on the ideXlab platform.
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Accurate electrical Battery Model capable of predicting runtime and I-V performance
IEEE Transactions on Energy Conversion, 2006Co-Authors: Min Chen, Gabriel A. Rincon-moraAbstract:Low power dissipation and maximum Battery runtime are crucial in portable electronics. With accurate and efficient circuit and Battery Models in hand, circuit designers can predict and optimize Battery runtime and circuit performance. In this paper, an accurate, intuitive, and comprehensive electrical Battery Model is proposed and implemented in a Cadence environment. This Model accounts for all dynamic characteristics of the Battery, from nonlinear open-circuit voltage, current-, temperature-, cycle number-, and storage time-dependent capacity to transient response. A simplified Model neglecting the effects of self-discharge, cycle number, and temperature, which are nonconsequential in low-power Li-ion-supplied applications, is validated with experimental data on NiMH and polymer Li-ion batteries. Less than 0.4% runtime error and 30-mV maximum error voltage show that the proposed Model predicts both the Battery runtime and I-V performance accurately. The Model can also be easily extended to other Battery and power sourcing technologies.
Wei Qiao - One of the best experts on this subject based on the ideXlab platform.
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An Enhanced Hybrid Battery Model
IEEE Transactions on Energy Conversion, 2019Co-Authors: Taesic Kim, Wei QiaoAbstract:A high-fidelity Battery Model capable of accurately predicting real-time Battery behavior for various operating conditions is crucial for the design and operation of Battery-powered systems. Based upon a prior work of the authors, this paper proposes an enhanced hybrid Battery Model to provide accurate predictions for the runtime behaviors of rechargeable electrochemical Battery cells under various ambient temperatures and charge/discharge current conditions. The proposed Battery Model is validated by simulation and experimental studies for lithium-ion Battery cells in various current and temperature conditions. The proposed Battery Model is computationally effective for design, simulation, condition monitoring, and power management of various Battery systems.
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A hybrid Battery Model capable of capturing dynamic circuit characteristics and nonlinear capacity effects
2012 IEEE Power and Energy Society General Meeting, 2012Co-Authors: Taesic Kim, Wei QiaoAbstract:Summary form only given. A high-fidelity Battery Model capable of accurately predicting Battery performance is required for proper design and operation of Battery-powered systems. However, the existing Battery Models have at least one of the following drawbacks: 1) requiring intensive computation due to high complexity, 2) not applicable for electrical circuit design and simulation, and 3) not capable of accurately capturing the state of charge (SOC) and predicting runtime of the Battery due to neglecting the nonlinear capacity effects. This paper proposes a novel hybrid Battery Model, which takes the advantages of an electrical circuit Battery Model to accurately predicting the dynamic circuit characteristics of the Battery and an analytical Battery Model to capturing the nonlinear capacity effects for accurate SOC tracking and runtime prediction of the Battery. The proposed Battery Model is validated by simulation and experimental studies for single-cell and multicell polymer lithium-ion batteries as well as for a lead-acid Battery. The proposed Model is applicable to other types and sizes of electrochemical Battery cells. The proposed Battery Model is computational effective for simulation, design, and real-time management of Battery-powered systems.
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A hybrid Battery Model capable of capturing dynamic circuit characteristics and nonlinear capacity effects
IEEE Transactions on Energy Conversion, 2011Co-Authors: Taesic Kim, Wei QiaoAbstract:A high-fidelity Battery Model capable of accurately predicting Battery performance is required for proper design and operation of Battery-powered systems. However, the existing Battery Models have at least one of the following drawbacks: 1) requiring intensive computation due to high complexity; 2) not applicable for electrical circuit design and simulation; and 3) not capable of accurately capturing the state of charge (SOC) and predicting runtime of the Battery due to neglecting the nonlinear capacity effects. This paper proposes a novel hybrid Battery Model, which takes the advantages of an electrical circuit Battery Model to accurately predicting the dynamic circuit characteristics of the Battery and an analytical Battery Model to capturing the nonlinear capacity effects for the accurate SOC tracking and runtime prediction of the Battery. The proposed Battery Model is validated by the simulation and experimental studies for the single-cell and multicell polymer lithium-ion batteries, as well as for a lead-acid Battery. The proposed Model is applicable to other types and sizes of electrochemical Battery cells. The proposed Battery Model is computational effective for simulation, design, and real-time management of Battery-powered systems.
King Jet Tseng - One of the best experts on this subject based on the ideXlab platform.
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A high-fidelity hybrid lithium-ion Battery Model for SOE and runtime prediction
Conference Proceedings - IEEE Applied Power Electronics Conference and Exposition - APEC, 2017Co-Authors: Kaiyuan Li, Boon-hee Soong, King Jet TsengAbstract:The state of energy (SOE) of Li-ion batteries is a key indicator for the energy optimization and management of energy storage devices (ESDs) in electric vehicles (EVs) and smart grids. To improve the SOE estimation accuracy, a hybrid Li-ion Battery Model is presented in this study against the dynamic loads and the rate energy effects of the Battery. Firstly, in order to take the advantages of both Models, a 2nd order RC Battery Model is merged with an analytical kinetic Battery Model for accurately predicting the Battery voltage characteristics and capturing the nonlinear rate energy effects to realize the high-fidelity SOE and runtime prediction. Secondly, a new method to separate the fast and slow dynamics of the 2nd order RC Model is developed and presented with high-performance accuracy. Thirdly, commercial Li-ion batteries are tested at dynamic loads under various temperature to validate the effectiveness of the proposed Model. The experimental results show high accuracy and reliability of the proposed Battery Model on the estimation of the Battery SOE and the Battery terminal voltage responses under dynamic loads.
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Adaptive estimation of state of charge and capacity with online identified Battery Model for vanadium redox flow Battery
Journal of Power Sources, 2016Co-Authors: Zhongbao Wei, Nyunt Wai, Tuti Mariana Lim, King Jet Tseng, Maria Skyllas-kazacosAbstract:Reliable state estimate depends largely on an accurate Battery Model. However, the parameters of Battery Model are time varying with operating condition variation and Battery aging. The existing co-estimation methods address the Model uncertainty by integrating the online Model identification with state estimate and have shown improved accuracy. However, the cross interference may arise from the integrated framework to compromise numerical stability and accuracy. Thus this paper proposes the decoupling of Model identification and state estimate to eliminate the possibility of cross interference. The Model parameters are online adapted with the recursive least squares (RLS) method, based on which a novel joint estimator based on extended Kalman Filter (EKF) is formulated to estimate the state of charge (SOC) and capacity concurrently. The proposed joint estimator effectively compresses the filter order which leads to substantial improvement in the computational efficiency and numerical stability. Lab scale experiment on vanadium redox flow Battery shows that the proposed method is highly authentic with good robustness to varying operating conditions and Battery aging. The proposed method is further compared with some existing methods and shown to be superior in terms of accuracy, convergence speed, and computational cost.
Daniel Guasch - One of the best experts on this subject based on the ideXlab platform.
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Dynamic Battery Model for photovoltaic applications
Progress in Photovoltaics: Research and Applications, 2003Co-Authors: Daniel Guasch, Stephan SilvestreAbstract:The main purpose of this paper is to offer a useful Battery Model for simulation of stand-alone photovoltaic applications. At present, many Models for Battery behaviour simulation are available. Owing to the complex response of this element, these Models are mainly concerned with stationary working point conditions. This paper presents an enhancement of a generic Battery Model, achieving a dynamic Battery Model for photovoltaic applications. It includes the use of automatic parameter extraction techniques and the solving of numerical calculation problems. It also introduces some new concepts, such as the maximum available capacity and the level of energy. Finally, a Battery state of health estimation, has been added to complete the Battery behaviour description, including non-ideal effects, such as Battery capacity reduction and self-discharging current. Copyright (C) 2003 John Wiley Sons, Ltd.
