UID:
edocfu_9961191486602883
Format:
1 online resource (xvi, 359 pages) :
,
illustrations (chiefly colour)
ISBN:
0-443-16160-7
,
0-443-16161-5
Content:
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.
Note:
Introduction /
,
Characteristic analysis of power lithium-ion batteries /
,
Aging characteristics of lithium-ion batteries /
,
Lithium-ion battery hysteresis characteristics and modeling /
,
Lithium-ion battery aging mechanism and multiple regression model /
,
Equivalent modeling and parameter identification of power lithium-ion batteries /
,
Equivalent modeling study of aviation lithium-ion batteries /
,
Battery state-of-charge measurement and control model based on the Internet platform /
,
High energy density lithium-ion battery state of charge prognosis /
,
State of charge estimation strategy based on fractional-order model /
,
State-of-charge estimation method for large unmanned aerial vehicle /
,
Construction of state of charge estimation method for automotive ternary batteries /
,
Estimation strategies for state of charge and state of power of lithium-ion batteries /
,
Collaborative energy and peak power status estimation /
,
State of health estimation based on improved double-extended Kalman filter /
,
Collaborative state of charge and state of health estimation based on improved adaptive unscented Kalman-unscented particle filter algorithm /
Language:
English
DOI:
10.1016/C2022-0-02506-X
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