Perspective—Com… (2021-03-01T00:00:00.000000Z)

TL;DR

Considering the nature of battery data and end-user applications, several architectures for integrating physics-based and machine learning models that can improve the ability to forecast battery lifetime are outlined.

Abstract

Forecasting the health of a battery is a modeling effort that is critical to driving improvements in and adoption of electric vehicles. Purely physics-based models and purely data-driven models have advantages and limitations of their own. Considering the nature of battery data and end-user applications, we outline several architectures for integrating physics-based and machine learning models that can improve our ability to forecast battery lifetime. We discuss the ease of implementation, advantages, limitations, and viability of each architecture, given the state of the art in the battery and machine learning ﬁ elds. © 2021 The Author(s). Published on behalf of The Electrochemical Society by IOP Publishing Limited. This is an open access

Authors

Patrick K. Herring

3 papers

Muratahan Aykol

4 papers

R. Braatz

2 papers

TL;DR

Abstract

Authors

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