人工智能在动力电池健康状态预估中的研究综述

Guohong Dai, Daohan Zhang, Simin Peng*, Yifan Miao, Yue Zhuo, Ruixin Yang, Quanqing Yu

*此作品的通讯作者

科研成果: 期刊稿件文章同行评审

1 引用 (Scopus)

摘要

The development and application of advanced electric vehicles has become the key technology to achieve “decarbonization”. Accurate state of health(SOH) prediction of battery can effectively characterize its operation performance. It is of great significance to the maintenance and life management of battery in electric vehicle. In recent years, a new generation of artificial intelligence technology represented by deep learning, reinforcement learning and big data technology has become a research hotspot in the application of battery state prediction. The basic theory of artificial intelligence technology and SOH and SOH influence factors is briefly introduced. Several main artificial intelligence algorithms in SOH prediction are summarized and discussed from the perspective of battery cell and battery system respectively. Finally, combined with emerging technologies such as big data, cloud computing and regional chain, some battery SOH prediction problems are discussed, which provides some ideas for breaking through the bottleneck of current power battery full life cycle management technology.

投稿的翻译标题Overview of Artificial Intelligence in Health Prediction of Power Battery
源语言繁体中文
页(从-至)391-408
页数18
期刊Jixie Gongcheng Xuebao/Chinese Journal of Mechanical Engineering
60
4
DOI
出版状态已出版 - 2月 2024

关键词

  • artificial intelligence
  • battery system
  • state of health
  • status
  • trend

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