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Neural Network-Based State-Of-Charge and State-Of-Health Estimation

Contributor(s): Qi, Huang (Author), Shunli, Wang (Author), Wang, Yujie (Author)

ISBN: 9781527552173

Publisher: Cambridge Scholars Publishing

Hardcover
$101.95
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Pub Date: November 17, 2023

Lexile Code: 0000

Target Age Group: NA to NA

Physical Info: 0.00" H x 0.00" L x 0.00" W ( 0.00 lbs) 164 pages

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Description: To deal with environmental deterioration and energy crises, developing clean and sustainable energy resources has become the strategic goal of the majority of countries in the global community. Lithium-ion batteries are the modes of power and energy storage in the new energy industry, and are also the main power source of new energy vehicles. State-of-charge (SOC) and state-of-health (SOH) are important indicators to measure whether a battery management system (BMS) is safe and effective. Therefore, this book focuses on the co-estimation strategies of SOC and SOH for power lithium-ion batteries. The book describes the key technologies of lithium-ion batteries in SOC and SOH monitoring and proposes a collaborative optimization estimation strategy based on neural networks (NN), which provide technical references for the design and application of a lithium-ion battery power management system. The theoretical methods in this book will be of interest to scholars and engineers engaged in the field of battery management system research.

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