J-STAGE Data
Browse
IMAGE
advpub_23-00116.jpg (300.61 kB)
DOCUMENT
Supporting Information.pdf (84.05 kB)
1/0
2 files

Comparison of Multi-step Prediction Models for Voltage Difference of Energy Storage Battery Pack Based on Unified Computing Operation Platform (Supporting Information)

Version 2 2024-01-16, 01:59
Version 1 2024-01-15, 04:23
dataset
posted on 2024-01-16, 01:59 authored by Weisen ZHAO, Jinsong WANG, Peng LIU, Dazhong WANG, Lanfang LIU, Xiangjun LI

The voltage difference of battery pack is a very important index for the state evaluation of energy storage battery. When the voltage difference is too large inside the battery pack, it may cause a series of safety problems. By predicting the voltage difference of battery pack, potential dangerous situations can be detected as early as possible, and necessary measures can be taken to ensure the safety of the energy storage battery, so as to realize the reliability improvement, efficiency improvement, and safety guarantee of the energy storage system. Through the multi-step prediction for the voltage difference of the energy storage battery pack, the variation trend of the voltage difference can be predicted in advance, so as to warn the possible voltage difference over-limit fault. At present, there are many methods for multi-step prediction of time series data, but which one is most suitable for predicting the voltage difference of the energy storage battery pack is still lack of research. In this paper, the stationarity and correlation of energy storage battery pack’s voltage difference data are analyzed and processed, and different multi-step prediction algorithms are used to predict the voltage difference of energy storage battery pack. The prediction results generated by different models are compared and analyzed, and the most suitable model selection for predicting the voltage difference of energy storage battery pack is discussed.

Funding

National Natural Science Foundation of China

National Key Research and Development Program of China

Gigawatt Hour Level Lithium-ion Battery Energy Storage System Technology

Integrated and Intelligent Management and Demonstration Application of Gigawatt Hour Level Energy Storage Power Station

History

Corresponding author email address

li_xiangjun@126.com

Copyright

© 2024 The Author(s).

Usage metrics

    Electrochemistry

    Categories

    Licence

    Exports

    RefWorks
    BibTeX
    Ref. manager
    Endnote
    DataCite
    NLM
    DC