Lithium-ion batteries (LIBs) have become indispensable components in diverse
energy applications driven by their high energy density, long cycle life, and
low self-discharge. These excellent characteristics are directly influenced by
their manufacturing processes, where variations in battery design and processing
parameters will lead to significant differences in performance. Therefore,
reliable and efficient evaluation of battery performance across manufacturing
processes is essential for quality assurance and process improvement.
Traditional methods rely on formation cycling and associated electrochemical
tests, which are time and cost intensive. Different from them, a
simulation-based approach for manufacturing performance evaluation is proposed
in this study. The method employs the pseudo two dimensions (P2D)
electrochemical model within the PyBaMM framework, where model parameters such
as electrode type, electrode size, and particle size are derived from
manufacturing data and built-in parameter data. The model predicts key
performance indicators including capacity, resistance, and loss of lithium
inventory (LLI) under specified tests conditions. A LGM50T cell was tested under
varying operational scenarios, demonstrating the feasibility of the approach.
Results indicate that low temperature condition significantly accelerates
degradation and Lithium plating, with the capacity degradation at 5 °C reaching
about three times that at 25 °C, while moderate variations in charge current
rate induce only minor differences of about 0.5%. Simultaneously, depth of
discharge (DOD) and average state of charge (SOC) have similar effects on
capacity degradation and LLI. By replacing extensive reality tests with
physics-based simulations, this method enables rapid evaluation of manufactured
or unprocessed battery process formulas, substantially reducing time and
material costs while providing mechanistic insights into process performance
interactions.