Article, 2024

Adaptive Kalman filter and self-designed early stopping strategy optimized convolutional neural network for state of energy estimation of lithium-ion battery in complex temperature environment

JOURNAL OF ENERGY STORAGE, ISSN 2352-152X, Volume 83, 10.1016/j.est.2024.110750

Contributors

Li, Jin [1] Wang, Shunli (Corresponding author) [1] [2] Chen, Lei [1] Wang, Yangtao [1] Zhou, Heng [1] Guerrero, Josep M. [3]

Affiliations

  1. [1] Southwest Univ Sci & Technol, Sch Informat Engn, Mianyang 621010, Peoples R China
    2. [NORA names: China; Asia, East];
  2. [2] Inner Mongolia Univ Technol, Elect Power Coll, Hohhot 010000, Inner Mongolia, Peoples R China
    3. [NORA names: China; Asia, East];
  3. [3] Aalborg Univ, Dept Energy Technol, PontoppidanstrAede 111, DK-9220 Aalborg, Denmark
    4. [NORA names: AAU Aalborg University; University; Denmark; Europe, EU; Nordic; OECD]

Abstract

Abstract not displayed. As this article is not marked as Open Access, it is unclear if we are allowed to show the abstract. Please use the link in the sidebar to view the data provider version of the article including abstract.

Keywords

Adaptive Kalman filter, Convolutional neural network, Lithium-ion batteries, Self-designed early stopping strategy, State of Energy

Data Provider: Clarivate

LINKS
-

Matching Records in NORA

SUBJECTS
+

METRICS
+