Article, Early Access, 2024

Health Prediction for Lithium-Ion Batteries Under Unseen Working Conditions

IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS, ISSN 0278-0046, 10.1109/TIE.2024.3379664

Contributors

Che, Yunhong 0000-0002-7350-0001 [1] Forest, Florent 0000-0001-6878-8752 [2] [3] Zheng, Yusheng 0000-0003-4901-1846 (Corresponding author) [1] Xu, Le [4] Teodorescu, Remus 0000-0002-2617-7168 [1]

Affiliations

  1. [1] Aalborg Univ, Dept Energy, DK-9220 Aalborg, Denmark
    2. [NORA names: AAU Aalborg University; University; Denmark; Europe, EU; Nordic; OECD];
  2. [2] Ecole Polytech Fed Lausanne, Lab Intelligent Maintenance & Operat Syst, CH-1015 Lausanne, Switzerland
    3. [NORA names: Switzerland; Europe, Non-EU; OECD];
  3. [3] Ecole Polytech Fed Lausanne, Lab Intelligent Maintenance & Operat Syst, CH-1015 Lausanne, Switzerland
    4. [NORA names: Switzerland; Europe, Non-EU; OECD];
  4. [4] Stanford Univ, Dept Energy Sci & Engn, Stanford, CA 94305 USA
    5. [NORA names: United States; America, North; OECD]

Abstract

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Keywords

Battery, domain adaptation (DA), health and trajectory prediction, multi-task learning, transfer learning

Data Provider: Clarivate

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