摘要
锂电池储能在清洁能源使用、电动汽车、移动设备以及再生能源存储领域都有极其重要的作用。一旦出现故障很容易引起一系列问题,所以对其进行故障分析,了解其实时健康状态具有重要意义。本文对深度学习机制下的锂电池故障分析技术进行综述。在深入了解包括多层感知、循环神经网络等深度学习诊断理论的基础上,阐述了最新的锂电池故障诊断模型(LSTM)的评估框架及流程。通过实际应用可以判定基于深度学习的锂电池故障分析模型具有可循环性高、精确度好等优点,值得更深入的研究探讨。
Lithium ion battery energy storage plays an extremely important role in the fields of clean energy use,electric vehicles,mobile devices,and renewable energy storage.Once a malfunction occurs,it is easy to cause a series of problems,so conducting fault analysis to understand its actual health status is of great significance.This article provides an overview of lithium battery fault analysis techniques under deep learning mechanisms.Based on a deep understanding of deep learning diagnostic theories such as multi-layer perception and recurrent neural networks,this paper elaborates on the evaluation framework and process of the latest lithium battery fault diagnosis model(LSTM).Through practical applications,it can be determined that the deep learning based lithium battery fault analysis model has advantages such as high recyclability and good accuracy,and is worthy of further research and exploration.
作者
时海欧
SHI Haiou(Puyang Vocational and Technical College;Puyang Institute of Technology,Henan University,Puyang 457000,Henan,China)
出处
《储能科学与技术》
CAS
CSCD
北大核心
2024年第6期2054-2056,共3页
Energy Storage Science and Technology
基金
2024年度濮阳职业技术学院校级课题(2024PZYKY28)。
关键词
深度学习
锂电池
评估模型
deep learning
lithium batteries
evaluation model
