The Experts below are selected from a list of 51552 Experts worldwide ranked by ideXlab platform
Sohel Anwar - One of the best experts on this subject based on the ideXlab platform.
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model based Condition monitoring in lithium ion batteries
Journal of Power Sources, 2014Co-Authors: Amardeep Singh, Afshin Izadian, Sohel AnwarAbstract:Abstract In this paper, a model based Condition monitoring technique is developed for lithium-ion battery Condition monitoring. Here a number of lithium-ion batteries are cycled using two separate over discharge test regimes and the resulting shift in battery parameters is recorded. The battery models are constructed using the equivalent circuit methodology. The Condition monitoring setup consists of a model bank representing the different degree of parameter shift due to overdischarge in the lithium ion battery. Extended Kalman filters (EKF) are used to maintain increased robustness of the Condition monitoring setup while estimating the terminal voltage of the battery cell. The information carrying residuals are generated and evaluation process is carried out in real-time using multiple model adaptive estimation (MMAE) methodology. The Condition evaluation function is used to generate probabilities that indicate the presence of a particular Operational Condition. Using the test data, it is shown that the performance shift in lithium ion batteries due to over discharge can be accurately detected.
Tadeusz Uhl - One of the best experts on this subject based on the ideXlab platform.
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Condition monitoring and fault detection in wind turbines based on cointegration analysis of scada data
Renewable Energy, 2018Co-Authors: Phong B Dao, Wieslaw J Staszewski, Tomasz Barszcz, Tadeusz UhlAbstract:Abstract This paper presents a new methodology – based on cointegration analysis of Supervisory Control And Data Acquisition (SCADA) data – for Condition monitoring and fault diagnosis of wind turbines. Analysis of cointegration residuals – obtained from cointegration process of wind turbine data – is used for Operational Condition monitoring and automated fault and/or abnormal Condition detection. The proposed method is validated using the experimental data acquired from a wind turbine drivetrain with a nominal power of 2 MW under varying environmental and Operational Conditions. A two-stage cointegration-based procedure is performed on six process parameters of the wind turbine, where data trends have nonlinear characteristics. The method is tested using two case studies with known faults. The results demonstrate that the proposed method can effectively analyse nonlinear data trends, continuously monitor the wind turbine and reliably detect abnormal problems.
Jerome P Lynch - One of the best experts on this subject based on the ideXlab platform.
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three tier modular structural health monitoring framework using environmental and Operational Condition clustering for data normalization validation on an Operational wind turbine system
Proceedings of the IEEE, 2016Co-Authors: Moritz Hackell, Raimund Rolfes, Michael B Kane, Jerome P LynchAbstract:This paper proposes a three-tier algorithmic framework as the basis for the flexible design of data-driven structural health monitoring (SHM) systems. The three major functions of the SHM system, including data normalization, feature extraction, and hypothesis testing (HT), are mapped to the three layers of the framework. The first tier of the framework is devoted to data normalization. Machine learning (ML) methods are adopted to normalize available data sets by binning data sets to similar environmental and Operational Conditions (EOCs) of the system. Specifically, affinity propagation clustering is used to delineate data into groups of similar EOC. Once data are normalized by EOC, the second tier of the framework extracts features from the data to serve as Condition parameters (CPs) for damage assessment. To ascertain the health state of the structure, the third tier of the framework is devoted to statistical analysis of the CP through HT. An intrinsic goal of the study is to explore the modularity of the three tier framework as a means of offering SHM system designers opportunity to explore and test different computational block sets at each layer to maximize the detection capability of the SHM system. Various realizations of the three-tier modular framework are presented and applied to acceleration and EOC data collected from an Operational 3-kW wind turbine. In total, 354 data sets are collected from the turbine, including tower lateral accelerations in two orthogonal directions at six heights, wind speed and wind direction; 317 of the data sets correspond to the wind turbine in a healthy state and 37 with the wind turbine in a damage state. Using quantitative metrics derived from receiver operating characteristic (ROC) curves, the damage classification capabilities of the framework are validated and shown to accurately identify intentionally introduced damage in the turbine.
Amardeep Singh - One of the best experts on this subject based on the ideXlab platform.
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model based Condition monitoring in lithium ion batteries
Journal of Power Sources, 2014Co-Authors: Amardeep Singh, Afshin Izadian, Sohel AnwarAbstract:Abstract In this paper, a model based Condition monitoring technique is developed for lithium-ion battery Condition monitoring. Here a number of lithium-ion batteries are cycled using two separate over discharge test regimes and the resulting shift in battery parameters is recorded. The battery models are constructed using the equivalent circuit methodology. The Condition monitoring setup consists of a model bank representing the different degree of parameter shift due to overdischarge in the lithium ion battery. Extended Kalman filters (EKF) are used to maintain increased robustness of the Condition monitoring setup while estimating the terminal voltage of the battery cell. The information carrying residuals are generated and evaluation process is carried out in real-time using multiple model adaptive estimation (MMAE) methodology. The Condition evaluation function is used to generate probabilities that indicate the presence of a particular Operational Condition. Using the test data, it is shown that the performance shift in lithium ion batteries due to over discharge can be accurately detected.
Dirk Uwe Sauer - One of the best experts on this subject based on the ideXlab platform.
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influence of Operational Condition on lithium plating for commercial lithium ion batteries electrochemical experiments and post mortem analysis
Applied Energy, 2017Co-Authors: Madeleine Ecker, Pouyan Shafiei Sabet, Dirk Uwe SauerAbstract:Abstract The lifetime and safety of lithium-ion batteries are key requirements for successful market introduction of electro mobility. Especially charging at low temperature and fast charging, known to provoke lithium plating, is an important issue for automotive engineers. Lithium plating, leading both to ageing as well as safety risks, is known to play a crucial role in system design of the application. To gain knowledge of different influence factors on lithium plating, low-temperature ageing tests are performed in this work. Commercial lithium-ion batteries of various types are tested under various Operational Conditions such as temperature, current, state of charge, charging strategy as well as state of health. To analyse the ageing behaviour, capacity fade and resistance increase are tracked over lifetime. The results of this large experimental survey on lithium plating provide support for the design of operation strategies for the implementation in battery management systems. To further investigate the underlying degradation mechanisms, differential voltage curves and impedance spectra are analysed and a post-mortem analysis of anode degradation is performed for a selected technology. The results confirm the deposition of metallic lithium or lithium compounds in the porous structure and suggest a strongly inhomogeneous deposition over the electrode thickness with a dense deposition layer close to the separator for the considered cell. It is shown that this inhomogeneous deposition can even lead to loss of active material. The plurality of the investigated technologies demonstrates large differences between different technologies concerning low-temperature behaviour and gives insight to the impact of cell properties. For the sample of cells considered in this work, cells rated to provide high power are found to be subject to faster degradation at low temperatures compared to high-energy cells, probably due to little self-heating. For application this result shows that cells designed for high current rates are not necessarily providing a good low-temperature performance.