State Estimation Strategies in Lithium-ion Battery Management Systems

Shunli Wang, Kailong Liu, Yujie Wang, Daniel-Ioan Stroe, Carlos Fernandez, Josep M. Guerrero

Research output: Book/ReportBookResearchpeer-review

Abstract

State Estimation Strategies in Lithium-ion Battery Management Systems presents key technologies and methodologies in modeling and monitoring charge, energy, power and health of lithium-ion batteries. Sections introduce core state parameters of the lithium-ion battery, reviewing existing research and the significance of the prediction of core state parameters of the lithium-ion battery and analyzing the advantages and disadvantages of prediction methods of core state parameters. Characteristic analysis and aging characteristics are then discussed. Subsequent chapters elaborate, in detail, on modeling and parameter identification methods and advanced estimation techniques in different application scenarios.
Offering a systematic approach supported by examples, process diagrams, flowcharts, algorithms, and other visual elements, this book is of interest to researchers, advanced students and scientists in energy storage, control, automation, electrical engineering, power systems, materials science and chemical engineering, as well as to engineers, R&D professionals, and other industry personnel.
Original languageEnglish
PublisherElsevier
Edition1st
Number of pages359
ISBN (Electronic)9780443161605
DOIs
Publication statusPublished - 14 Jul 2023

Fingerprint

Dive into the research topics of 'State Estimation Strategies in Lithium-ion Battery Management Systems'. Together they form a unique fingerprint.

Cite this