  To plan and optimize energy storage demands that account for Li-ion battery
aging dynamics, techniques need to be developed to diagnose battery internal
states accurately and rapidly. This study seeks to reduce the computational
resources needed to determine a battery's internal states by replacing
physics-based Li-ion battery models -- such as the single-particle model (SPM)
and the pseudo-2D (P2D) model -- with a physics-informed neural network (PINN)
surrogate. The surrogate model makes high-throughput techniques, such as
Bayesian calibration, tractable to determine battery internal parameters from
voltage responses. This manuscript is the first of a two-part series that
introduces PINN surrogates of Li-ion battery models for parameter inference
(i.e., state-of-health diagnostics). In this first part, a method is presented
for constructing a PINN surrogate of the SPM. A multi-fidelity hierarchical
training, where several neural nets are trained with multiple physics-loss
fidelities is shown to significantly improve the surrogate accuracy when only
training on the governing equation residuals. The implementation is made
available in a companion repository (https://github.com/NREL/pinnstripes). The
techniques used to develop a PINN surrogate of the SPM are extended in Part II
for the PINN surrogate for the P2D battery model, and explore the Bayesian
calibration capabilities of both surrogates.
