Battery State-of-Health Diagnostics During Fast Cycling Using Physics-Informed Deep-Learning

Peter Weddle, Sangwook Kim, Bor-Rong Chen, Zonggen Yi, Paul Gasper, Andrew Colclasure, Kandler Smith, Kevin Gering, Tanvir Tanim, Eric Dufek

Research output: Contribution to journalArticlepeer-review

7 Scopus Citations

Abstract

Rapid, in-situ Li-ion battery state-of-health (SOH) quantification is challenging. Li-ion battery aging can vary significantly with chemistry, operating conditions, cycling demands, electrode design, and operation history. As a cell ages, optimal and safe operating conditions need to be adapted to account for battery degradation by tracking critical aging modes such as loss-of-lithium-inventory (LLI), loss-of-active-material (LAM) in either electrode, and/or impedance rise. This manuscript describes a framework for identifying battery aging modes in-operando using fast-rate voltage charge/discharge responses. The framework uses a physically based Li-ion battery model to produce synthetic high-rate responses at aged states. The aging model is calibrated against experimental data from cells with different electrode loadings and cycled under a variety of fast-charging conditions (1 h, 15 min, 10 min, and 7 min charging). The synthetically generated high-rate responses at aged states are then used to train a deep-learning model to identify real cell state-of-health from fast charge/discharge battery voltage responses. The synthetically trained deep-learning model performance is validated by comparing to standard incremental capacity analysis and half-cell measurements. The framework demonstrates the benefits of using high-rate physics-based models to generate synthetic data for training deep-learning models.

Original languageAmerican English
Article numberArticle No. 233582
Number of pages12
JournalJournal of Power Sources
Volume585
DOIs
StatePublished - 30 Nov 2023

Bibliographical note

Publisher Copyright:
© 2023 Elsevier B.V.

NREL Publication Number

  • NREL/JA-5700-85756

Keywords

  • In-situ diagnostics
  • Li-ion battery model
  • Machine learning
  • Synthetic data

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