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Developing extreme fast charge battery protocols – A review spanning materials to systems

  • Eric J. Dufek
  • , Daniel P. Abraham
  • , Ira Bloom
  • , Bor Rong Chen
  • , Parameswara R. Chinnam
  • , Andrew M. Colclasure
  • , Kevin L. Gering
  • , Matthew Keyser
  • , Sangwook Kim
  • , Weijie Mai
  • , David C. Robertson
  • , Marco Tulio F. Rodrigues
  • , Kandler Smith
  • , Tanvir R. Tanim
  • , Francois L.E. Usseglio-Viretta
  • , Peter J. Weddle

Research output: Contribution to journalArticlepeer-review

80 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 languageEnglish
Article number231129
JournalJournal of Power Sources
Volume526
Early online dateFeb 23 2022
DOIs
StatePublished - Apr 1 2022

Keywords

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

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