人工智能在动力电池健康状态预估中的研究综述

Translated title of the contribution: Overview of Artificial Intelligence in Health Prediction of Power Battery

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

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)

Abstract

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.

Translated title of the contributionOverview of Artificial Intelligence in Health Prediction of Power Battery
Original languageChinese (Traditional)
Pages (from-to)391-408
Number of pages18
JournalJixie Gongcheng Xuebao/Chinese Journal of Mechanical Engineering
Volume60
Issue number4
DOIs
Publication statusPublished - Feb 2024

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