Optimum Model-Based Design of Diagnostics Experiments (DOE) with Hybrid Pulse Power Characterization (HPPC) for Lithium-Ion Batteries
Name
1-s2.0-S1383586624005999-main.pdf
Description
Published version
Size
1.19 MB
Format
Adobe PDF
Checksum (MD5)
c5bf74f18f27b6d85bbc151e97c3acfe
Author(s)
Rhyu, Jinwook
Zhuang, Debbie
Bazant, Martin Z
Braatz, Richard D
Date Issued
July 1, 2024
Journal
Journal of The Electrochemical Society
Publisher
The Electrochemical Society
Citation
Jinwook Rhyu et al 2024 J. Electrochem. Soc. 171 070544
Version
Final published version
Abstract
Diagnostics of lithium-ion batteries are frequently performed in battery management systems for optimized operation of lithium-ion batteries or for second-life usage. However, attempting to extract dominant degradation information requires long rest times between diagnostic pulses, which compete with the need for efficient diagnostics. Here, we design a set of efficient optimal hybrid pulse power characterization (HPPC) diagnostics using model-based design of experiment (DOE) methods, applying knowledge of degradation effects on pulse kinetics and cell properties. We validate that these protocols are effective through minimization of uncertainty, and robust with Markov Chain Monte Carlo (MCMC) simulations. Contrary to traditional HPPC diagnostics which use fixed pulse magnitudes at uniformly distributed state of charges (SOC), we find that well-designed HPPC protocols using our framework outperform traditional protocols in terms of minimizing both parametric uncertainties and diagnostic time. Trade-offs between minimizing parametric uncertainty and total diagnostic time can be made based on different diagnostics needs.
MIT Department
Massachusetts Institute of Technology. Department of Chemical Engineering
Massachusetts Institute of Technology. Department of Mathematics
Terms of Use
Creative Commons Attribution-NonCommercial-NoDerivs
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1149/1945-7111/ad63ce
