  Monitoring the health of lithium-ion batteries' internal components as they
age is crucial for optimizing cell design and usage control strategies.
However, quantifying component-level degradation typically involves aging many
cells and destructively analyzing them throughout the aging test, limiting the
scope of quantifiable degradation to the test conditions and duration.
Fortunately, recent advances in physics-informed machine learning (PIML) for
modeling and predicting the battery state of health demonstrate the feasibility
of building models to predict the long-term degradation of a lithium-ion
battery cell's major components using only short-term aging test data by
leveraging physics. In this paper, we present four approaches for building
physics-informed machine learning models and comprehensively compare them,
considering accuracy, complexity, ease-of-implementation, and their ability to
extrapolate to untested conditions. We delve into the details of each
physics-informed machine learning method, providing insights specific to
implementing them on small battery aging datasets. Our study utilizes long-term
cycle aging data from 24 implantable-grade lithium-ion cells subjected to
varying temperatures and C-rates over four years. This paper aims to facilitate
the selection of an appropriate physics-informed machine learning method for
predicting long-term degradation in lithium-ion batteries, using short-term
aging data while also providing insights about when to choose which method for
general predictive purposes.
