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Batteries are an essential component in a deeply decarbonized future.Understanding battery performance and "useful life" as a function of design anduse is of paramount importance to accelerating adoption. Historically, batterystate of health (SOH) was summarized by a single parameter, the fraction of abattery'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 andelectronic impedances, open circuit voltages, and microstructure metrics.Indeed, predicting across an extensive suite of properties as a function ofbattery use is a "holy grail" of battery science; it can provide unprecedentedinsights toward the design of better batteries with reduced experimentaleffort, and de-risking energy storage investments that are necessary to meetCO2 reduction targets. In this work, we present a first step in that directionvia deep transformer networks for the prediction of 28 battery state of healthdescriptors using two cycling datasets representing six lithium-ion cathodechemistries (LFP, NMC111, NMC532, NMC622, HE5050, and 5Vspinel), multipleelectrolyte/anode compositions, and different charge-discharge scenarios. Theaccuracy of these predictions versus battery life (with an unprecedented meanabsolute error of 19 cycles in predicting end of life for an LFP fast-chargingdataset) illustrates the promise of deep learning towards providing deeperunderstanding and control of battery health.

Prognosis of Multivariate Battery State of Performance and Health via Transformers