Article, 2024

Accuracy comparison and improvement for state of health estimation of lithium-ion battery based on random partial recharges and feature engineering

JOURNAL OF ENERGY CHEMISTRY, ISSN 2095-4956, Volume 92, Pages 591-604, 10.1016/j.jechem.2024.01.037

Contributors

Li, Xingjun [1] [2] [3] Yu, Dan (Corresponding author) [3] Vilsen, Soren Byg [3] Stroe, Daniel-Ioan [3]

Affiliations

  1. [1] Univ Texas Austin, Walker Dept Mech Engn, Austin, TX 78712 USA
    2. [NORA names: United States; America, North; OECD];
  2. [2] Univ Texas Austin, Walker Dept Mech Engn, Austin, TX 78712 USA
    3. [NORA names: United States; America, North; OECD];
  3. [3] Aalborg Univ, Dept Energy, DK-9220 Aalborg, Denmark
    4. [NORA names: AAU Aalborg University; University; Denmark; Europe, EU; Nordic; OECD]

Abstract

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Keywords

Dynamic forklift aging profile, Feature engineering, Lithium -ion batteries, Machine learning, State of health comparison

Data Provider: Clarivate

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