This book -- the third and final volume in a series describing battery-management systems – shows you how to use physics-based models of battery cells in a computationally efficient way for optimal battery-pack management and control to maximize battery-pack performance and extend life. It covers the foundations of electrochemical model-based battery management system while introducing and teaching the state of the art in physics-based methods for battery management.
Building upon the content in volumes I and II, the book helps you identify parameter values for physics-based models of a commercial lithium-ion battery cell without requiring cell teardown; shows you how to estimate the internal electrochemical state of all cells in a battery pack in a computationally efficient way during operation using these physics-based models; demonstrates the use the models plus state estimates in a battery management system to optimize fast-charge of battery packs to minimize charge time while also maximizing battery service life; and takes you step-by-step through the use models to optimize the instantaneous power that can be demanded from the battery pack while also maximizing battery service life.
The book also demonstrates how to overcome the primary roadblocks to implementing physics-based method for battery management: the computational-complexity roadblock, the parameter-identification roadblock, and the control-optimization roadblock. It also uncovers the fundamental flaw in all present “state of art” methods and shows you why all BMS based on equivalent-circuit models must be designed with over-conservative assumptions. This is a strong resource for battery engineers, chemists, researchers, and educators who are interested in advanced battery management systems and strategies based on the best available understanding of how battery cells operate.
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Gregory L. Plett received his B.Eng. degree in Computer Systems Engineering from Carleton University and his M.S. and Ph.D. degrees in Electrical Engineering from Stanford University. He is currently a professor for the Department of Electrical and Computer Engineering at the University of Colorado, Colorado Springs and a senior member of the IEEE and life member of the Electrochemical Society.
M. Scott Trimboli received his B.S in Engineering Science from the United States Air Force Academy in 1980, his M.S in Engineering Mechanics from Columbia University in 1981 and his Ph.D. in Control Engineering from the University of Oxford. He previously served as an exchange scientist with the German Aerospace Research Establishment (DLR) in Göttingen, Germany. He is an Associate Professor of Electrical and Computer Engineering at the University of Colorado Colorado Springs.
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Hardcover. Condizione: new. Hardcover. This book -- the third and final volume in a series describing battery-management systems shows you how to use physics-based models of battery cells in a computationally efficient way for optimal battery-pack management and control to maximize battery-pack performance and extend life. It covers the foundations of electrochemical model-based battery management system while introducing and teaching the state of the art in physics-based methods for battery management. Building upon the content in volumes I and II, the book helps you identify parameter values for physics-based models of a commercial lithium-ion battery cell without requiring cell teardown; shows you how to estimate the internal electrochemical state of all cells in a battery pack in a computationally efficient way during operation using these physics-based models; demonstrates the use the models plus state estimates in a battery management system to optimize fast-charge of battery packs to minimize charge time while also maximizing battery service life; and takes you step-by-step through the use models to optimize the instantaneous power that can be demanded from the battery pack while also maximizing battery service life. The book also demonstrates how to overcome the primary roadblocks to implementing physics-based method for battery management: the computational-complexity roadblock, the parameter-identification roadblock, and the control-optimization roadblock. It also uncovers the fundamental flaw in all present state of art methods and shows you why all BMS based on equivalent-circuit models must be designed with over-conservative assumptions. This is a strong resource for battery engineers, chemists, researchers, and educators who are interested in advanced battery management systems and strategies based on the best available understanding of how battery cells operate. Demonstrates how to use physics-based models of battery cells in a computationally efficient framework for optimal battery-pack management to maximize battery performance and extend life. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. Codice articolo 9781630819040
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