Prognosis of Multivariate Battery State of Performance and Health via Transformers

TL;DR

This paper introduces a deep transformer-based approach to predict multiple battery health descriptors across various chemistries and conditions, aiming to enhance understanding and management of battery lifespan.

Contribution

It presents the first application of transformer networks for multivariate battery health prediction, covering diverse chemistries and operational scenarios.

Findings

Achieved a mean absolute error of 19 cycles in end-of-life prediction for LFP batteries.

Successfully predicted 28 battery health descriptors across six chemistries.

Demonstrated deep learning's potential for comprehensive battery health modeling.

Abstract

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.