Developing Extreme Fast Charge Battery Protocols - A Review Spanning Materials to Systems

Eric Dufek, Daniel Abraham, Ira Bloom, Bor-Rong Chen, Parameswara Chinnam, Andrew Colclasure, Kevin Gering, Matthew Keyser, Sangwook Kim, Weijie Mai, David Robertson, Marco-Tulio Rodrigues, Kandler Smith, Tanvir Tanim, Francois Usseglio-Viretta, Peter Weddle

Research output: Contribution to journalArticlepeer-review

28 Scopus Citations

Abstract

Extreme fast charging (XFC) has become a focal research point in the lithium-battery community over the last several years. As adoption of electric vehicles increases, fast charging has become a key driver in enhancing consumer recharge experience. Recently, the research community has made significant improvements in developing charge protocols to support XFC. New charge protocol designs derived using a combination of advanced, physically derived models, and electrochemical and secondary characterization methods, increase charge acceptance and decrease aging. By coordinating these methods and modifying protocols to account for different material constraints, including lithium plating and cathode particle degradation, novel charge protocols have increased the energy accepted during charging by over 25% in 10 min and increased the charge acceptance prior to a constant-voltage step by approximately 3x. Here, we review several charge-protocol advances, aging factors which are enhanced by XFC and advances which will enable adoption of XFC capable vehicles. These advances include implementing machine learning and other detection algorithms to reduce and classify lithium plating, which is known to significantly degrade cell performance and reduce cell life. The review concludes by discussing full-system fast charge requirements, including electric vehicle service equipment needs for implementing XFC protocols.

Original languageAmerican English
Article numberArticle No. 231129
Number of pages14
JournalJournal of Power Sources
Volume526
DOIs
StatePublished - 1 Apr 2022

Bibliographical note

Publisher Copyright:
© 2022 Elsevier B.V.

NREL Publication Number

  • NREL/JA-5700-81414

Keywords

  • Aging
  • Charge protocol
  • Extreme fast charging
  • Li-ion battery
  • Machine learning

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