  Batteries are an essential component in a deeply decarbonized future.
Understanding battery performance and "useful life" as a function of design and
use is of paramount importance to accelerating adoption. Historically, battery
state of health (SOH) was summarized by a single parameter, the fraction of a
battery's capacity relative to its initial state. A more useful approach,
however, is a comprehensive characterization of its state and complexities,
using an interrelated set of descriptors including capacity, energy, ionic and
electronic impedances, open circuit voltages, and microstructure metrics.
Indeed, predicting across an extensive suite of properties as a function of
battery use is a "holy grail" of battery science; it can provide unprecedented
insights toward the design of better batteries with reduced experimental
effort, and de-risking energy storage investments that are necessary to meet
CO2 reduction targets. In this work, we present a first step in that direction
via deep transformer networks for the prediction of 28 battery state of health
descriptors using two cycling datasets representing six lithium-ion cathode
chemistries (LFP, NMC111, NMC532, NMC622, HE5050, and 5Vspinel), multiple
electrolyte/anode compositions, and different charge-discharge scenarios. The
accuracy of these predictions versus battery life (with an unprecedented mean
absolute error of 19 cycles in predicting end of life for an LFP fast-charging
dataset) illustrates the promise of deep learning towards providing deeper
understanding and control of battery health.
