Reprint

Battery Modelling, Applications, and Technology

Edited by
March 2024
252 pages
  • ISBN978-3-7258-0605-8 (Hardback)
  • ISBN978-3-7258-0606-5 (PDF)

This book is a reprint of the Special Issue Battery Modelling, Applications, and Technology that was published in

Chemistry & Materials Science
Engineering
Environmental & Earth Sciences
Physical Sciences
Summary

Batteries, among the various energy storage systems, are electrochemical storage devices that have always been attractive for both stationary and mobile applications. Different kinds of technology have been developed through the years (lead–acid, nickel–cadmium, nickel–metal hydride, lithium ion, etc.), and other novel technologies (metal–air, quasi-solid state battery, all-solid state battery, etc.) are still being studied. The most important features for these devices to have include high power, energy density, and efficiency, in addition to a long lifecycle. In particular, the latter can be increased by developing novel technologies in the construction of the batteries themselves and/or in controlling them to operate in their optimal working conditions. To achieve this, the modeling of batteries and the estimation of their parameters becomes a very important challenge. Indeed, through the latter, it is possible to study, analyze, and predict the behavior of single battery cells or whole battery packs with different aims. On the one hand, battery models can be used for analyses of the batteries themselves to improve their efficiency and lifecycle, to build battery management systems, or for sizing battery packs. On the other hand, the same models can be used to analyze the behavior of entire systems in which the battery is one part. This Special Issue collected many articles on battery chemical, electric, thermal, and aging models, integrated battery models and their composition, battery parameter estimation methods, and novel applications and technologies of batteries.

Format
  • Hardback
License
© 2022 by the authors; CC BY-NC-ND license
Keywords
cable; thermal analysis; skin and proximity effects; battery storage; ampacity; lithium-ion battery; state of charge; state of health; deep learning; cloud; field application; lithium-ion battery; battery management system; machine learning; cloud; artificial intelligence; state of charge; state of health; safety; field; real-world application; lithium-ion battery; machine learning; SoH; battery degradation; prognostics; lithium-ion batteries; calendar aging; OCV curve; state of charge estimation; ICEVs; BEVs; mobility; electricity generation; wind energy; solar photovoltaic energy; renewable energy sources; vehicle fleet; pollutant emissions; second-life battery; electricity grid application; electrochemical modeling; degradation prediction; battery operational strategy; lithium-ion iron phosphate (LFP) battery; hybrid pulse power characterization (HPPC); Thevenin equivalent circuit; battery energy storage system; renewables; market service stacking; lithium-ion batteries; state of charge; machine learning; artificial neural networks; data augmentation; lithium-ion batteries; electrode microstructure; heterogeneous physical model; mechanical degradation; electrochemical impedance spectroscopy; battery; fuel-cell vehicle; load capacity; hybrid powertrain; electric propulsion system; energy balance assessment; external load; chassis dynamometer; lithium-ion battery; battery degradation; prognostics; machine learning; SoH; lithium batteries; Kalman filters; sliding innovation filter; interacting multiple model; state of health; state of charge; battery monitoring system; B005 battery dataset