Optimizing Fast Charging and Wetting in Lithium-Ion Batteries with Optimal Microstructure Patterns Identified by Genetic Algorithm: Article No. 120506

Research output: Contribution to journalArticlepeer-review

6 Scopus Citations

Abstract

To sustain the high-rate current required for fast charging electric vehicle batteries, electrodes must exhibit sufficiently high effective ionic diffusion. Additionally, to reduce battery manufacturing costs, wetting time must decrease. Both of these issues can be addressed by structuring the electrodes with mesoscale pore channels. However, their optimal spatial distribution, or patterns, is unknown. Herein, a genetic algorithm has been developed to identify these optimal patterns using a CPU-cheap proxy distance-based model to evaluate the impact of the added pore networks. Both coin-cell and pouch cell form factors have been considered for the wetting analysis, with their respective electrolyte infiltration mode. Regular hexagonal and mud-crack-like patterns, respectively, for fast charging and fast wetting were found to be optimal and have been compared with pre-determined, easier to manufacture, patterns. The model predicts that using cylindrical channels arranged in a regular hexagonal pattern is ~6.25 times more efficient for fast charging as compared to grooved lines with both structuring strategies being restricted to a 5% electrode total volume loss. The model also shows that only a very limited electrode volume loss (1%-2%) is required to dramatically improve the wetting (5-20 times) compared to an unstructured electrode.
Original languageAmerican English
Number of pages22
JournalJournal of the Electrochemical Society
Volume170
Issue number12
DOIs
StatePublished - 2023

NREL Publication Number

  • NREL/JA-5700-86663

Keywords

  • electrolyte wetting
  • fast charging
  • genetic algorithm
  • lithium-ion battery
  • secondary pore network
  • topology optimization

Fingerprint

Dive into the research topics of 'Optimizing Fast Charging and Wetting in Lithium-Ion Batteries with Optimal Microstructure Patterns Identified by Genetic Algorithm: Article No. 120506'. Together they form a unique fingerprint.

Cite this