储能科学与技术 ›› 2024, Vol. 13 ›› Issue (1): 336-338.doi: 10.19799/j.cnki.2095-4239.2023.0920

• 储能系统与工程 • 上一篇    下一篇

基于数据预处理和计算机VMD-LSTM-GPR的储能系统离子电池剩余寿命预测

田凌浒1(), 袁炳夏2()   

  1. 1.中国石油新疆油田分公司，新疆 克拉玛依 834000
    2.惠州学院网络与信息中心，广东 惠州 516007
  • 收稿日期:2023-12-19 修回日期:2023-12-28 出版日期:2024-01-05 发布日期:2024-01-22
  • 通讯作者: 袁炳夏 E-mail:tianlh@petrochina.com.cn;13928319050@139.com
  • 作者简介:田凌浒（1995—），本科，工程师，主要研究方向为节能研究，E-mail：tianlh@petrochina.com.cn；

Prediction of ion battery remaining life of energy storage system based on data preprocessing and computer VMD-LSTM-GPR

Linghu TIAN1(), Bingxia YUAN2()   

  1. 1.CNPC Xinjiang Oilfield Company, Karamay 834000, Xinjiang, China
    2.Huizhou University, Network and Information Center, Huizhou 516007, Guangdong, China
  • Received:2023-12-19 Revised:2023-12-28 Online:2024-01-05 Published:2024-01-22
  • Contact: Bingxia YUAN E-mail:tianlh@petrochina.com.cn;13928319050@139.com

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摘要：

离子电池剩余寿命影响储能系统运行能力，准确预测电池寿命，有助于判断系统的实时运行状态，为获得较为可靠的预测结果，提出基于数据预处理和计算机VMD-LSTM-GPR的储能系统离子电池剩余寿命预测方法。针对储能系统离子电池剩余寿命预测的相关理论问题进行研究，并联合储能数据预处理标准与计算机VMD-LSTM-GPR模型，计算锂离子电池的容量退化能力，从而评估剩余电池寿命，实现基于数据预处理和计算机VMD-LSTM-GPR的储能系统离子电池剩余寿命预测。

关键词: 数据预处理, 计算机VMD-LSTM-GPR, 储能系统, 离子电池, 剩余寿命

Abstract:

The remaining life of ion battery affects the operation ability of energy storage system, and accurate prediction of battery life is helpful to judge the real-time operation state of the system. In order to obtain reliable prediction results, a prediction method of ion battery remaining life of energy storage system based on data preprocessing and computer VMD-LSTM-GPR is proposed. Research on the related introduction to the remaining life prediction of ion batteries in the energy storage system, and combine the energy storage data preprocessing standard with the computer VMD-LSTM-GPR model to calculate the capacity degradation capability of lithium-ion batteries, so as to evaluate the remaining battery life. The remaining life prediction of ion battery of energy storage system based on data preprocessing and computer VMD-LSTM-GPR was realized.

Key words: data preprocessing, computer VMD-LSTM-GPR, energy storage system, ion battery, residual life

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