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1. |
Record Nr. |
UNINA990003368660403321 |
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Autore |
Mazziotti, Fabio |
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Titolo |
Diritto del lavoro / Fabio Mazziotti |
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Pubbl/distr/stampa |
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ISBN |
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Edizione |
[2. ed.] |
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Descrizione fisica |
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Disciplina |
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Locazione |
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Collocazione |
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MAZ(2)344.4501B |
MAZ(2)344.4501A |
MAZ(2)344.4501C |
MAZ(2)344.4501D |
A-III-107 |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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2. |
Record Nr. |
UNINA9910132273803321 |
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Autore |
Jiang Jiuchun |
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Titolo |
Fundamentals and applications of lithium-ion batteries in electric drive vehicles / / Jiuchun Jiang and Caiping Zhang |
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Pubbl/distr/stampa |
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Singapore : , : John Wiley & Sons Inc., , 2015 |
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©2015 |
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ISBN |
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1-118-41481-0 |
1-118-41479-9 |
1-118-41480-2 |
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Edizione |
[1st edition] |
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Descrizione fisica |
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1 online resource (299 p.) |
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Disciplina |
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Soggetti |
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Electric vehicles - Batteries |
Lithium ion batteries |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Note generali |
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Description based upon print version of record. |
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Nota di bibliografia |
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Includes bibliographical references and index. |
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Nota di contenuto |
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Title Page; Copyright; Contents; About the Authors; Foreword; Preface; Chapter 1 Introduction; 1.1 The Development of Batteries in Electric Drive Vehicles; 1.1.1 The Goals; 1.1.2 Trends in Development of the Batteries; 1.1.3 Application Issues of LIBs; 1.1.4 Significance of Battery Management Technology; 1.2 Development of Battery Management Technologies; 1.2.1 No Management; 1.2.2 Simple Management; 1.2.3 Comprehensive Management; 1.3 BMS Key Technologies; References; Chapter 2 Performance Modeling of Lithium-ion Batteries; 2.1 Reaction Mechanism of Lithium-ion Batteries |
2.2 Testing the Characteristics of Lithium-ion Batteries 2.2.1 Rate Discharge Characteristics; 2.2.2 Charge and Discharge Characteristics Under Operating Conditions; 2.2.3 Impact of Temperature on Capacity; 2.2.4 Self-Discharge; 2.3 Battery Modeling Method; 2.3.1 Equivalent Circuit Model; 2.3.2 Electrochemical Model; 2.3.3 Neural Network Model; 2.4 Simulation and Comparison of Equivalent Circuit Models; 2.4.1 Model Parameters Identification Principle; 2.4.2 Implementation Steps of Parameter Identification; 2.4.3 Comparison of Simulation of Three Equivalent Circuit Models |
2.5 Battery Modeling Method Based on a Battery Discharging Curve 2.6 |
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Battery Pack Modeling; 2.6.1 Battery Pack Modeling; 2.6.2 Simulation of Battery Pack Model; References; Chapter 3 Battery State Estimation; 3.1 Definition of SOC; 3.1.1 The Maximum Available Capacity; 3.1.2 Definition of Single Cell SOC; 3.1.3 Definition of the SOC of Series Batteries; 3.2 Discussion on the Estimation of the SOC of a Battery; 3.2.1 Load Voltage Detection; 3.2.2 Electromotive Force Method; 3.2.3 Resistance Method; 3.2.4 Ampere-hour Counting Method; 3.2.5 Kalman Filter Method; 3.2.6 Neural Network Method |
3.2.7 Adaptive Neuro-Fuzzy Inference System 3.2.8 Support Vector Machines; 3.3 Battery SOC Estimation Algorithm Application; 3.3.1 The SOC Estimation of a PEV Power Battery; 3.3.2 Power Battery SOC Estimation for Hybrid Vehicles; 3.4 Definition and Estimation of the Battery SOE; 3.4.1 Definition of the Single Battery SOE; 3.4.2 SOE Definition of the Battery Groups; 3.5 Method for Estimation of the Battery Group SOE and the Remaining Energy; 3.6 Method of Estimation of the Actual Available Energy of the Battery; References; Chapter 4 The Prediction of Battery Pack Peak Power |
4.1 Definition of Peak Power 4.1.1 Peak Power Capability of Batteries; 4.1.2 Battery Power Density; 4.1.3 State of Function of Batteries; 4.2 Methods for Testing Peak Power; 4.2.1 Test Methods Developed by Americans; 4.2.2 The Test Method of Japan; 4.2.3 The Chinese Standard Test Method; 4.2.4 The Constant Power Test Method; 4.2.5 Comparison of the Above-Mentioned Testing Methods; 4.3 Peak Power; 4.3.1 The Relation between Peak Power and Temperature; 4.3.2 The Relation between Peak Power and SOC; 4.3.3 Relationship between Peak Power and Ohmic Internal Resistance |
4.4 Available Power of the Battery Pack |
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Sommario/riassunto |
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A theoretical and technical guide to the electric vehicle lithium-ion battery management system Covers the timely topic of battery management systems for lithium batteries. After introducing the problem and basic background theory, it discusses battery modeling and state estimation. In addition to theoretical modeling it also contains practical information on charging and discharging control technology, cell equalisation and application to electric vehicles, and a discussion of the key technologies and research methods of the lithium-ion power battery management system. The author systematically |
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3. |
Record Nr. |
UNINA9910299775103321 |
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Titolo |
Topological and Statistical Methods for Complex Data : Tackling Large-Scale, High-Dimensional, and Multivariate Data Spaces / / edited by Janine Bennett, Fabien Vivodtzev, Valerio Pascucci |
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Pubbl/distr/stampa |
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Berlin, Heidelberg : , : Springer Berlin Heidelberg : , : Imprint : Springer, , 2015 |
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ISBN |
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Edizione |
[1st ed. 2015.] |
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Descrizione fisica |
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1 online resource (297 p.) |
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Collana |
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Mathematics and Visualization, , 2197-666X |
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Disciplina |
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Soggetti |
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Topology |
Statistics |
Mathematics |
Algorithms |
Information visualization |
Manifolds (Mathematics) |
Statistical Theory and Methods |
Applications of Mathematics |
Data and Information Visualization |
Manifolds and Cell Complexes |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Note generali |
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"With 120 Figures, 101 in color." |
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Nota di bibliografia |
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Includes bibliographical references at the end of each chapters and index. |
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Nota di contenuto |
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I. Large-scale data analysis: In-situ and distributed analysis -- II. Large-scale data analysis: Efficient representation of large-functions -- III. Multi-variate data analysis: Structural techniques -- IV. Multi-variate data analysis: Classification and visualization of vector fields -- V. High-dimensional data analysis: Exploration of high-dimensional models -- VI. High-dimensional data analysis: Analysis of large systems. |
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Sommario/riassunto |
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This book contains papers presented at the Workshop on the Analysis of Large-scale, High-Dimensional, and Multi-Variate Data Using Topology and Statistics, held in Le Barp, France, June 2013. It features the work of some of the most prominent and recognized leaders in the |
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field who examine challenges as well as detail solutions to the analysis of extreme scale data. The book presents new methods that leverage the mutual strengths of both topological and statistical techniques to support the management, analysis, and visualization of complex data. It covers both theory and application and provides readers with an overview of important key concepts and the latest research trends. Coverage in the book includes multi-variate and/or high-dimensional analysis techniques, feature-based statistical methods, combinatorial algorithms, scalable statistics algorithms, scalar and vector field topology, and multi-scale representations. In addition, the book details algorithms that are broadly applicable and can be used by application scientists to glean insight from a wide range of complex data sets. |
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