Cantitate/Preț
Produs
Total produse
Vezi coșul
AI for Status Monitoring of Utility Scale Batteries de Shunli Wang

AI for Status Monitoring of Utility Scale Batteries: Energy Engineering

Autor Shunli Wang, Kailong Liu, Yujie Wang
en Limba Engleză Hardback – 19 ian 2023
Utility-scale Li-ion batteries are poised to play key roles for the clean energy system, but their failure has severe effects. AI can help with their monitoring and management. This work covers machine learning, neural networks, and deep learning, for battery modeling.
Citește tot Restrânge

Din seria Energy Engineering

Preț: 84174 lei

Preț vechi: 102652 lei
-18% Nou

Puncte Express: 1263
Preț estimativ în valută:
16111€ • 16603$ • 13601£

Carte disponibilă

Livrare economică 10-24 februarie

 Adaugă în coș

Wish list
Gift list
Am citit!
Adaugă în listă
Preluare comenzi: 021 569.72.76

Specificații

ISBN-13: 9781839537387
ISBN-10: 1839537388
Pagini: 495
Dimensiuni: 163 x 241 x 30 mm
Greutate: 0.98 kg
Editura: Institution of Engineering and Technology
Colecția Energy Engineering
Seria Energy Engineering

Descriere

Batteries are a necessary part of a low emission energy system, as they can store renewable electricity and assist the grid. Utility scale batteries, with capacities of several to hundreds of MWh, are particularly important for condominiums, local grid nodes, and EV charging arrays. However, such batteries are expensive and need to be monitored and managed well to maintain capacity and reliability. Artificial intelligence offers a solution for effective monitoring and management of utility scale batteries.

This book systematically describes AI-based technologies for battery state estimation and modeling for utility-scale Li-ion batteries. Chapters cover utility-scale lithium-ion battery system characteristics, AI-based equivalent modeling, parameter identification, state of charge estimation, battery parameter estimation, offer samples and case studies for utility-scale battery operation, and conclude with a summary and prospect for AI-based battery status monitoring. The book provides practical references for the design and application of large-scale lithium-ion battery systems.

AI for Status Monitoring of Utility Scale Batteries is an invaluable resource for researchers in battery R&D, including battery management systems and related power electronics, battery manufacturers, and advanced students.

Notă biografică

Shunli Wang is a professor at Southwest University of Science and Technology, Sichuan, China. He is an expert in the field of new energy research. He is the head of NELab, conducting modeling and state estimation strategy research for lithium-ion batteries. He has undertaken over 40 projects and 30 patents, published over 100 research papers, and won 20 awards such as the Young Scholar, and Science & Technology Progress Awards.