A Data-Driven Fleet Service: State of Health Forecasting of Lithium-Ion Batteries

A Data-Driven Fleet Service: State of Health Forecasting of Lithium-Ion Batteries
Author: Friedrich von Bülow
Publisher: Springer Vieweg
Total Pages: 0
Release: 2023-12-06
Genre: Technology & Engineering
ISBN: 9783658431877

Given the limitations of state-of-the-art methods, this book presents a state of health (SOH) forecasting method that is suitable for lithium-ion battery (LIB) systems in real-world battery electric vehicle operation. Its histogram-based features can capture the higher operational variability compared to constant and controlled laboratory operation. Also, the transferability of a trained machine learning model to new LIB cell types and new operational domains is investigated. The presented SOH forecasting method can be provided as a cloud service via a web or smartphone app to fleet managers. Forecasting the SOH enables fleet managers of battery electric vehicle fleets to forecast and plan vehicle replacements.

Behaviour of Lithium-Ion Batteries in Electric Vehicles

Behaviour of Lithium-Ion Batteries in Electric Vehicles
Author: Gianfranco Pistoia
Publisher: Springer
Total Pages: 343
Release: 2018-02-10
Genre: Technology & Engineering
ISBN: 3319699504

This book surveys state-of-the-art research on and developments in lithium-ion batteries for hybrid and electric vehicles. It summarizes their features in terms of performance, cost, service life, management, charging facilities, and safety. Vehicle electrification is now commonly accepted as a means of reducing fossil-fuels consumption and air pollution. At present, every electric vehicle on the road is powered by a lithium-ion battery. Currently, batteries based on lithium-ion technology are ranked first in terms of performance, reliability and safety. Though other systems, e.g., metal-air, lithium-sulphur, solid state, and aluminium-ion, are now being investigated, the lithium-ion system is likely to dominate for at least the next decade – which is why several manufacturers, e.g., Toyota, Nissan and Tesla, are chiefly focusing on this technology. Providing comprehensive information on lithium-ion batteries, the book includes contributions by the world’s leading experts on Li-ion batteries and vehicles.

The Handbook of Lithium-Ion Battery Pack Design

The Handbook of Lithium-Ion Battery Pack Design
Author: John T. Warner
Publisher: Elsevier
Total Pages: 472
Release: 2024-05-14
Genre: Technology & Engineering
ISBN: 0443138087

The Handbook of Lithium-Ion Battery Pack Design: Chemistry, Components, Types and Terminology,?Second Edition provides a clear and concise explanation of EV and Li-ion batteries for readers that are new to the field. The second edition expands and updates all topics covered in the original book, adding more details to all existing chapters and including major updates to align with all of the rapid changes the industry has experienced over the past few years. This handbook offers a layman's explanation of the history of vehicle electrification and battery technology, describing the various terminology and acronyms and explaining how to do simple calculations that can be used in determining basic battery sizing, capacity, voltage, and energy. By the end of this book the reader will have a solid understanding of the terminology around Li-ion batteries and be able to undertake simple battery calculations. The book is immensely useful to beginning and experienced engineers alike who are moving into the battery field. Li-ion batteries are one of the most unique systems in automobiles today in that they combine multiple engineering disciplines, yet most engineering programs focus on only a single engineering field. This book provides the reader with a reference to the history, terminology and design criteria needed to understand the Li-ion battery and to successfully lay out a new battery concept. Whether you are an electrical engineer, a mechanical engineer or a chemist, this book will help you better appreciate the inter-relationships between the various battery engineering fields that are required to understand the battery as an Energy Storage System. It gives great insights for readers ranging from engineers to sales, marketing, management, leadership, investors, and government officials. - Adds a brief history of battery technology and its evolution to current technologies? - Expands and updates the chemistry to include the latest types - Discusses thermal runaway and cascading failure mitigation technologies? - Expands and updates the descriptions of the battery module and pack components and systems?? - Adds description of the manufacturing processes for cells, modules, and packs? - Introduces and discusses new topics such as battery-as-a-service, cell to pack and cell to chassis designs, and wireless BMS?

Electric Vehicles: Prospects and Challenges

Electric Vehicles: Prospects and Challenges
Author: Tariq Muneer
Publisher: Elsevier
Total Pages: 588
Release: 2017-07-11
Genre: Science
ISBN: 0128030402

Electric Vehicles: Prospects and Challenges looks at recent design methodologies and technological advancements in electric vehicles and the integration of electric vehicles in the smart grid environment, comprehensively covering the fundamentals, theory and design, recent developments and technical issues involved with electric vehicles. Considering the prospects, challenges and policy status of specific regions and vehicle deployment, the global case study references make this book useful for academics and researchers in all engineering and sustainable transport areas. - Presents a systematic and integrated reference on the essentials of theory and design of electric vehicle technologies - Provides a comprehensive look at the research and development involved in the use of electric vehicle technologies - Includes global case studies from leading EV regions, including Nordic and European countries China and India

Mapping of Lithium-ion Batteries for Vehicles

Mapping of Lithium-ion Batteries for Vehicles
Author: Lisbeth Dahllöf
Publisher:
Total Pages: 54
Release: 2019
Genre: Denmark
ISBN: 9789289362948

The number of electric vehicles (cars, buses, e-bikes, electric scooters and electric motorcycles) sold in the Nordic countries is currently increasing quickly. That means that more electricity is used for driving, and also that more of some important metals are being used than earlier. This report regards the fate of the lithium-ion batteries used in vehicles in the Nordic countries. Currently the "Battery Directive" (EC, 2006) which is a producer's responsibility directive, is under revision and this study is a knowledge base intended for use by the Nordic Environmental Protection Agencies for their referral response in the revision process. This report focuses on the aspect of metal resources, but it does not elaborate on a broader range of environmental impacts, as these were outside the scope of this study.

Battery Management Systems

Battery Management Systems
Author: Gregory L. Plett
Publisher: Artech House Publishers
Total Pages: 0
Release: 2015
Genre: Technology & Engineering
ISBN: 9781630810276

State-Of-The-Art applications of equivalent-circuit methods as they pertain to solving problems in battery management and control.

Integrated System Health Management

Integrated System Health Management
Author: Jiuping Xu
Publisher: Academic Press
Total Pages: 473
Release: 2017-05-18
Genre: Technology & Engineering
ISBN: 012813268X

ISHM is an innovative combination of technologies and methods that offers solutions to the reliability problems caused by increased complexities in design, manufacture, use conditions, and maintenance. Its key strength is in the successful integration of reliability (quantitative estimation of successful operation or failure), "diagnosibility" (ability to determine the fault source), and maintainability (how to maintain the performance of a system in operation). It draws on engineering issues such as advanced sensor monitoring, redundancy management, probabilistic reliability theory, artificial intelligence for diagnostics and prognostics, and formal validation methods, but also "quasi-technical" techniques and disciplines such as quality assurance, systems architecture and engineering, knowledge capture, information fusion, testability and maintainability, and human factors. This groundbreaking book defines and explains this new discipline, providing frameworks and methodologies for implementation and further research. Each chapter includes experiments, numerical examples, simulations and case studies. It is the ideal guide to this crucial topic for professionals or researchers in aerospace systems, systems engineering, production engineering, and reliability engineering. - Solves prognostic information selection and decision-level information fusion issues - Presents integrated evaluation methodologies for complex aerospace system health conditions and software system reliability assessment - Proposes a framework to perform fault diagnostics with a distributed intelligent agent system and a data mining approach for multistate systems - Explains prognostic methods that combine both the qualitative system running state prognostics and the quantitative remaining useful life prediction

Gaussian Processes for Machine Learning

Gaussian Processes for Machine Learning
Author: Carl Edward Rasmussen
Publisher: MIT Press
Total Pages: 266
Release: 2005-11-23
Genre: Computers
ISBN: 026218253X

A comprehensive and self-contained introduction to Gaussian processes, which provide a principled, practical, probabilistic approach to learning in kernel machines. Gaussian processes (GPs) provide a principled, practical, probabilistic approach to learning in kernel machines. GPs have received increased attention in the machine-learning community over the past decade, and this book provides a long-needed systematic and unified treatment of theoretical and practical aspects of GPs in machine learning. The treatment is comprehensive and self-contained, targeted at researchers and students in machine learning and applied statistics. The book deals with the supervised-learning problem for both regression and classification, and includes detailed algorithms. A wide variety of covariance (kernel) functions are presented and their properties discussed. Model selection is discussed both from a Bayesian and a classical perspective. Many connections to other well-known techniques from machine learning and statistics are discussed, including support-vector machines, neural networks, splines, regularization networks, relevance vector machines and others. Theoretical issues including learning curves and the PAC-Bayesian framework are treated, and several approximation methods for learning with large datasets are discussed. The book contains illustrative examples and exercises, and code and datasets are available on the Web. Appendixes provide mathematical background and a discussion of Gaussian Markov processes.