Article, 2023

Lithium-ion battery degradation trajectory early prediction with synthetic dataset and deep learning

JOURNAL OF ENERGY CHEMISTRY, ISSN 2095-4956, Volume 85, Pages 534-546, 10.1016/j.jechem.2023.06.036

Contributors

Lin, Mingqiang 0000-0001-6637-2702 [1] [2] [3] You, Yuqiang [1] [2] [3] Meng, Jin-Hao 0000-0003-3490-5089 (Corresponding author) [4] Wang, Wei 0000-0001-6257-6564 [4] Wu, Ji [5] Stroe, Daniel-Ioan 0000-0002-2938-8921 [6]

Affiliations

  1. [1] Chinese Acad Sci, Quanzhou Inst Equipment Mfg, Fujian Inst Res Struct Matter, Quanzhou 362200, Fujian, Peoples R China
    2. [NORA names: China; Asia, East];
  2. [2] Fujian Agr & Forestry Univ, Sch Mech & Elect Engn, Fuzhou 350100, Fujian, Peoples R China
    3. [NORA names: China; Asia, East];
  3. [3] Chinese Acad Sci, Quanzhou Inst Equipment Mfg, Fujian Inst Res Struct Matter, Quanzhou 362200, Fujian, Peoples R China
    4. [NORA names: China; Asia, East];
  4. [4] Xi An Jiao Tong Univ, Sch Elect Engn, Xian 710049, Shaanxi, Peoples R China
    5. [NORA names: China; Asia, East];
  5. [5] Hefei Univ Technol, Sch Automot & Transportat Engn, Hefei 230009, Anhui, Peoples R China
    6. [NORA names: China; Asia, East];

Abstract

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Keywords

Degradation trajectory, Lithium -ion battery, Long -term prediction, Transferred convolutional neural network

Data Provider: Clarivate

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