Physics-Informed Design of Hybrid Pulse Power Characterization Tests for Rechargeable Batteries
Name
Zhuang_2024_J._Electrochem._Soc._171_050510.pdf
Description
Published version
Size
1.18 MB
Format
Adobe PDF
Checksum (MD5)
d198d8d3a148b299f56bc73b730b5daa
Author(s)
Zhuang, Debbie
Li, Michael L
Lam, Vivek N
Braatz, Richard D
Chueh, William C
Bazant, Martin Z
Date Issued
May 1, 2024
Journal
Journal of The Electrochemical Society
Publisher
The Electrochemical Society
Citation
Debbie Zhuang et al 2024 J. Electrochem. Soc. 171 050510
Version
Final published version
Abstract
Industry-standard diagnostic methods for rechargeable batteries, such as hybrid pulse power characterization (HPPC) tests for hybrid electric vehicles, provide some indications of state of health (SoH), but lack a physical basis to guide protocol design and identify degradation mechanisms. We develop a physics-based theoretical framework for HPPC tests, which are able to accurately determine specific mechanisms for battery degradation in porous electrode simulations. We show that voltage pulses are generally preferable to current pulses, since voltage-resolved linearization more rapidly quantifies degradation without sacrificing accuracy or allowing significant state changes during the measurement. In addition, asymmetric amounts of information gain between charge /discharge pulses are found from differences in electrode kinetic scales. We demonstrate our approach of physics-informed HPPC on simulated Li-ion batteries with nickel-rich cathodes and graphite anodes. Multivariable optimization by physics-informed HPPC rapidly determines kinetic parameters that correlate with degradation phenomena at the anode, such as solid-electrolyte interphase (SEI) growth and lithium plating, as well as at the cathode, such as oxidation-induced cation disorder. If validated experimentally, standardized voltage protocols for HPPC tests could play a pivotal role in expediting battery SoH assessment and accelerating materials design by providing new electrochemical features for interpretable machine learning of battery degradation.
MIT Department
Massachusetts Institute of Technology. Department of Chemical Engineering
Massachusetts Institute of Technology. Department of Mathematics
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DOI of Published Version
https://doi.org/10.1149/1945-7111/ad4394
