Abstract
The accuracy of single particle (SP) models for lithium-ion batteries at high C-rates is constrained by lithium concentration gradients in the electrolyte, which affect ionic conductivity, overpotential, and reaction rates. This study addresses these limitations using extreme gradient boosting machine learning (ML). By training our ML model with data from a comprehensive electrochemical (P2D) model and performing sensitivity analysis on key battery parameters, we enhance predictive accuracy. Compared to conventional SP and P2D models under constant current loading, our ML-based SP model achieves similar predictive accuracy to P2D, with significant improvements in computational efficiency. Additionally, the ML-based SP model demonstrates improved predictive accuracy under dynamic loading conditions, providing a practical framework for improving battery management and safety.
Recommended Citation
E. Olugbade and J. Park, "Boosting Predictive Accuracy of Single Particle Models for Lithium-Ion Batteries using Machine Learning," Applied Physics Letters, vol. 125, no. 14, article no. 143903, American Institute of Physics, Sep 2024.
The definitive version is available at https://doi.org/10.1063/5.0230376
Department(s)
Mechanical and Aerospace Engineering
Publication Status
Available Access
International Standard Serial Number (ISSN)
0003-6951
Document Type
Article - Journal
Document Version
Final Version
File Type
text
Language(s)
English
Rights
© 2024 American Institute of Physics, All rights reserved.
Publication Date
30 Sep 2024
Comments
National Science Foundation, Grant None