The Experts below are selected from a list of 65469 Experts worldwide ranked by ideXlab platform
Orhan Ekren - One of the best experts on this subject based on the ideXlab platform.
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simulation based size optimization of a pv wind hybrid energy conversion system with Battery storage under various load and auxiliary energy conditions
Applied Energy, 2009Co-Authors: Banu Yetkin Ekren, Orhan EkrenAbstract:In this paper, size of a PV/wind integrated hybrid energy system with Battery storage is optimized under various loads and unit cost of auxiliary energy sources. The optimization is completed by a simulation based optimization procedure, OptQuest, which integrates various heuristic methods. In the study, the main performance measure is the hybrid energy system cost. And the design parameters are PV size, wind turbine rotor swept area and the Battery Capacity. The case study is realized for Izmir Institute of Technology Campus Area, Urla, Turkey. The simulation model of the system is realized in ARENA 12.0, a commercial simulation software, and is optimized using the OptQuest tool in this software. Consequently, the optimum sizes of PV, wind turbine and Battery Capacity are obtained under various auxiliary energy unit costs and two different loads. The optimum results are confirmed using Loss of Load Probability (LLP) and autonomy analysis. And the investment costs are investigated how they are shared among those four energy sources at the optimum points.
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size optimization of a pv wind hybrid energy conversion system with Battery storage using response surface methodology
Applied Energy, 2008Co-Authors: Orhan Ekren, Banu Yetkin EkrenAbstract:Abstract This paper aims to show the use of the response surface methodology (RSM) in size optimization of an autonomous PV/wind integrated hybrid energy system with Battery storage. RSM is a collection of statistical and mathematical methods which relies on optimization of response surface with design parameters. In this study, the response surface, output performance measure, is the hybrid system cost, and the design parameters are the PV size, wind turbine rotor swept area and the Battery Capacity. The case study is realized in ARENA 10.0, a commercial simulation software, for satisfaction of electricity consumption of the global system for mobile communications (GSM) base station at Izmir Institute of Technology Campus Area, Urla, Turkey. As a result, the optimum PV area, wind turbine rotor swept area, and Battery Capacity are obtained to be 3.95 m2, 29.4 m2, 31.92 kWh, respectively. These results led to $37,033.9 hybrid energy system cost, including auxiliary energy cost. The optimum result obtained by RSM is confirmed using loss of load probability (LLP) and autonomy analysis.
Banu Yetkin Ekren - One of the best experts on this subject based on the ideXlab platform.
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simulation based size optimization of a pv wind hybrid energy conversion system with Battery storage under various load and auxiliary energy conditions
Applied Energy, 2009Co-Authors: Banu Yetkin Ekren, Orhan EkrenAbstract:In this paper, size of a PV/wind integrated hybrid energy system with Battery storage is optimized under various loads and unit cost of auxiliary energy sources. The optimization is completed by a simulation based optimization procedure, OptQuest, which integrates various heuristic methods. In the study, the main performance measure is the hybrid energy system cost. And the design parameters are PV size, wind turbine rotor swept area and the Battery Capacity. The case study is realized for Izmir Institute of Technology Campus Area, Urla, Turkey. The simulation model of the system is realized in ARENA 12.0, a commercial simulation software, and is optimized using the OptQuest tool in this software. Consequently, the optimum sizes of PV, wind turbine and Battery Capacity are obtained under various auxiliary energy unit costs and two different loads. The optimum results are confirmed using Loss of Load Probability (LLP) and autonomy analysis. And the investment costs are investigated how they are shared among those four energy sources at the optimum points.
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size optimization of a pv wind hybrid energy conversion system with Battery storage using response surface methodology
Applied Energy, 2008Co-Authors: Orhan Ekren, Banu Yetkin EkrenAbstract:Abstract This paper aims to show the use of the response surface methodology (RSM) in size optimization of an autonomous PV/wind integrated hybrid energy system with Battery storage. RSM is a collection of statistical and mathematical methods which relies on optimization of response surface with design parameters. In this study, the response surface, output performance measure, is the hybrid system cost, and the design parameters are the PV size, wind turbine rotor swept area and the Battery Capacity. The case study is realized in ARENA 10.0, a commercial simulation software, for satisfaction of electricity consumption of the global system for mobile communications (GSM) base station at Izmir Institute of Technology Campus Area, Urla, Turkey. As a result, the optimum PV area, wind turbine rotor swept area, and Battery Capacity are obtained to be 3.95 m2, 29.4 m2, 31.92 kWh, respectively. These results led to $37,033.9 hybrid energy system cost, including auxiliary energy cost. The optimum result obtained by RSM is confirmed using loss of load probability (LLP) and autonomy analysis.
Melani Sullivan - One of the best experts on this subject based on the ideXlab platform.
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online estimation of lithium ion Battery Capacity using sparse bayesian learning
Journal of Power Sources, 2015Co-Authors: Gaurav Jain, Craig L Schmidt, Carrie Strief, Melani SullivanAbstract:Abstract Lithium-ion (Li-ion) rechargeable batteries are used as one of the major energy storage components for implantable medical devices. Reliability of Li-ion batteries used in these devices has been recognized as of high importance from a broad range of stakeholders, including medical device manufacturers, regulatory agencies, patients and physicians. To ensure a Li-ion Battery operates reliably, it is important to develop health monitoring techniques that accurately estimate the Capacity of the Battery throughout its life-time. This paper presents a sparse Bayesian learning method that utilizes the charge voltage and current measurements to estimate the Capacity of a Li-ion Battery used in an implantable medical device. Relevance Vector Machine (RVM) is employed as a probabilistic kernel regression method to learn the complex dependency of the Battery Capacity on the characteristic features that are extracted from the charge voltage and current measurements. Owing to the sparsity property of RVM, the proposed method generates a reduced-scale regression model that consumes only a small fraction of the CPU time required by a full-scale model, which makes online Capacity estimation computationally efficient. 10 years' continuous cycling data and post-explant cycling data obtained from Li-ion prismatic cells are used to verify the performance of the proposed method.
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online estimation of lithium ion Battery Capacity using sparse bayesian learning
Design Automation Conference, 2015Co-Authors: Gaurav Jain, Craig L Schmidt, Carrie Strief, Melani SullivanAbstract:Lithium-ion (Li-ion) rechargeable batteries are used as one of the major energy storage components for implantable medical devices. Reliability of Li-ion batteries used in these devices has been recognized as of high importance from a broad range of stakeholders, including medical device manufacturers, regulatory agencies, patients and physicians. To ensure a Li-ion Battery operates reliably, it is important to develop health monitoring techniques that accurately estimate the Capacity of the Battery throughout its life-time. This paper presents a sparse Bayesian learning method that utilizes the charge voltage and current measurements to estimate the Capacity of a Li-ion Battery used in an implantable medical device. Relevance Vector Machine (RVM) is employed as a probabilistic kernel regression method to learn the complex dependency of the Battery Capacity on the characteristic features that are extracted from the charge voltage and current measurements. Owing to the sparsity property of RVM, the proposed method generates a reduced-scale regression model that consumes only a small fraction of the CPU time required by a full-scale model, which makes online Capacity estimation computationally efficient. 10 years’ continuous cycling data and post-explant cycling data obtained from Li-ion prismatic cells are used to verify the performance of the proposed method.Copyright © 2015 by ASME
Dietmar Goehlich - One of the best experts on this subject based on the ideXlab platform.
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electrification of a city bus network an optimization model for cost effective placing of charging infrastructure and Battery sizing of fast charging electric bus systems
International Journal of Sustainable Transportation, 2017Co-Authors: Alexander Kunith, Roman Mendelevitch, Dietmar GoehlichAbstract:ABSTRACTThe deployment of Battery-powered electric bus systems within the public transportation sector plays an important role in increasing energy efficiency and abating emissions. Rising attention is given to bus systems using fast charging technology. This concept requires a comprehensive infrastructure to equip bus routes with charging stations. The combination of charging infrastructure and bus batteries needs a reliable energy supply to maintain a stable bus operation even under demanding conditions. An efficient layout of the charging infrastructure and an appropriate dimensioning of Battery Capacity are crucial to minimize the total cost of ownership and to enable an energetically feasible bus operation. In this work, the central issue of jointly optimizing the charging infrastructure and Battery Capacity is described by a capacitated set covering problem. A mixed-integer linear optimization model is developed to determine the minimum number and location of required charging stations for a bus net...
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electrification of a city bus network an optimization model for cost effective placing of charging infrastructure and Battery sizing of fast charging electric bus systems
2016Co-Authors: Alexander Kunith, Roman Mendelevitch, Dietmar GoehlichAbstract:The deployment of Battery-powered electric bus systems within the public transportation sector plays an important role to increase energy efficiency and to abate emissions. Rising attention is given to bus systems using fast charging technology. This concept requires a comprehensive infrastructure to equip bus routes with charging stations. The combination of charging infrastructure and bus batteries needs a reliable energy supply to maintain a stable bus operation even under demanding conditions. An efficient layout of the charging infrastructure and an appropriate dimensioning of Battery Capacity are crucial to minimize the total cost of ownership and to enable an energetically feasible bus operation. In this work, the central issue of jointly optimizing the charging infrastructure and Battery Capacity is described by a capacitated set covering problem. A mixed-integer linear optimization model is developed to determine the minimum number and location of required charging stations for a bus network as well as the adequate Battery Capacity for each bus line of the network. The bus energy consumption for each route segments is determined based on individual route, bus type, traffic and other information. Different scenarios are examined in order to assess the influence of charging power, climate and changing operating conditions. The findings reveal significant differences in terms of needed infrastructure depending on the scenarios considered. Moreover, the results highlight a trade-off between Battery size and charging infrastructure under different operational and infrastructure conditions. The paper addresses upcoming challenges for transport authorities during the electrification process of the bus fleets and sharpens the focus on infrastructural issues related to the fast charging concept.
Gaurav Jain - One of the best experts on this subject based on the ideXlab platform.
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online estimation of lithium ion Battery Capacity using sparse bayesian learning
Journal of Power Sources, 2015Co-Authors: Gaurav Jain, Craig L Schmidt, Carrie Strief, Melani SullivanAbstract:Abstract Lithium-ion (Li-ion) rechargeable batteries are used as one of the major energy storage components for implantable medical devices. Reliability of Li-ion batteries used in these devices has been recognized as of high importance from a broad range of stakeholders, including medical device manufacturers, regulatory agencies, patients and physicians. To ensure a Li-ion Battery operates reliably, it is important to develop health monitoring techniques that accurately estimate the Capacity of the Battery throughout its life-time. This paper presents a sparse Bayesian learning method that utilizes the charge voltage and current measurements to estimate the Capacity of a Li-ion Battery used in an implantable medical device. Relevance Vector Machine (RVM) is employed as a probabilistic kernel regression method to learn the complex dependency of the Battery Capacity on the characteristic features that are extracted from the charge voltage and current measurements. Owing to the sparsity property of RVM, the proposed method generates a reduced-scale regression model that consumes only a small fraction of the CPU time required by a full-scale model, which makes online Capacity estimation computationally efficient. 10 years' continuous cycling data and post-explant cycling data obtained from Li-ion prismatic cells are used to verify the performance of the proposed method.
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online estimation of lithium ion Battery Capacity using sparse bayesian learning
Design Automation Conference, 2015Co-Authors: Gaurav Jain, Craig L Schmidt, Carrie Strief, Melani SullivanAbstract:Lithium-ion (Li-ion) rechargeable batteries are used as one of the major energy storage components for implantable medical devices. Reliability of Li-ion batteries used in these devices has been recognized as of high importance from a broad range of stakeholders, including medical device manufacturers, regulatory agencies, patients and physicians. To ensure a Li-ion Battery operates reliably, it is important to develop health monitoring techniques that accurately estimate the Capacity of the Battery throughout its life-time. This paper presents a sparse Bayesian learning method that utilizes the charge voltage and current measurements to estimate the Capacity of a Li-ion Battery used in an implantable medical device. Relevance Vector Machine (RVM) is employed as a probabilistic kernel regression method to learn the complex dependency of the Battery Capacity on the characteristic features that are extracted from the charge voltage and current measurements. Owing to the sparsity property of RVM, the proposed method generates a reduced-scale regression model that consumes only a small fraction of the CPU time required by a full-scale model, which makes online Capacity estimation computationally efficient. 10 years’ continuous cycling data and post-explant cycling data obtained from Li-ion prismatic cells are used to verify the performance of the proposed method.Copyright © 2015 by ASME
