+Advanced Search
Home > Archive>Volume 47, Issue 1, 2024 >71-78
PDF HTML XML Export Cite reminder
SOC estimation of ternary lithium-ion battery based on ASSA-RBF joint algorithm
CSTR:
[cstr]
Author:
Affiliation:

Key Laboratory of Magnetic Suspension Technology and Maglev Vehicle, Ministry of Education (School of Electrical Engineering,Southwest Jiaotong University)，Chengdu 611756, China

Clc Number:

TM912

  • Article
    • | |
  • Metrics
    • |
  • Reference
    • |
  • Related
    • |
  • Cited by
    • | |
  • Comments
    Abstract:

    Accurately estimating the state of charge (SOC) of ternary lithium batteries is the foundation for ensuring the safe and stable operation of electric vehicles. In response to the problem of low estimation accuracy of traditional BP neural networks and the tendency of RBF neural networks to fall into local optima, this paper proposes a ternary lithium battery SOC estimation method based on the combination of adaptive sparrow search algorithm and RBF neural networks. Firstly, the standard sparrow search algorithm is improved by using the elite chaos reverse mechanism to initialize the sparrow population, and the Cauchy Gaussian mutation strategy is used to optimize the follower position update formula in the sparrow population. Then, the improved sparrow search algorithm is used to optimize the initial weight and width parameters of the RBF neural network to improve the algorithm′s estimation accuracy of SOC. Finally, the model was validated based on the charging and discharging experimental data of ternary lithium batteries. The results show that under dynamic stress testing conditions, the proposed joint algorithm model has a root mean square error of 0.694% and an average percentage error of 3.15% in SOC estimation, which can be well applied to SOC estimation of ternary lithium batteries.

    Reference
    Related
    Cited by
Get Citation

Copy
Share
Article Metrics
  • Abstract:
  • PDF:
  • HTML:
  • Cited by:
History
  • Online: April 24,2024
Article QR Code
You are the first191539 Visitors
Governing Body：Beijing Electronic Holding Co., LTD
Organizer：Beijing Radio Technology Research Institute
Address：No.79 Beiheyan Street, Dongcheng District, Beijing China, 100009
Phone：13810562337
Electric Measurement Technology ® 2025 All Rights Reserved