估计
电池(电)
国家(计算机科学)
计算机科学
健康状况
人工智能
机器学习
工程类
物理
算法
功率(物理)
系统工程
量子力学
作者
Daniel‐Ioan Stroe,Xin Sui
出处
期刊:Elsevier eBooks
[Elsevier]
日期:2024-01-01
卷期号:: 383-430
标识
DOI:10.1016/b978-0-323-85622-5.00010-9
摘要
Over the years, lithium–ion batteries have developed as a key enabling technology for the green transition. Although many of these batteries’ characteristics, such as energy density, power capability, and cost, have gradually improved, uncertainties remain concerning their performance over their lifetimes. Thus, to ensure reliable and efficient battery operation, the battery's available performance, known as its state of health (SOH), must be known at every moment. This chapter introduces the most common battery SOH estimation methods, from direct measurements to deep neural networks, discussing their key performance metrics, advantages, and drawbacks.
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