Front Cover: Data‐Driven Entropy Coefficient Estimation for Large Format Lithium‐Ion Batteries (Batteries & Supercaps 1/2026)

Batteries & Supercaps 2026

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Authors: Svenas Burba, Johannes Bakkelund, Rikesh Kumar, Ankit Singh, Mukul Parmananda, Williams Agyei Appiah
DOI: 10.1002/batt.70224
Cited by: 0

Abstract

The Front Cover illustrates entropy coefficient estimation from the charge/discharge data of large-format (100 Ah) lithium-ion prismatic cell at various current densities and temperatures using empirical and data-driven methods. The method accelerates the estimation of entropy coefficients of batteries, resulting in the improvement of the predictability of physics-based electrochemical models at high current densities where reversible heat generation due to entropy is significant. More information can be found in the Research Article by W. A. Appiah and co-workers (DOI: 10.1002/batt.202500533).

Fields of Study & Subjects

Advanced Battery Technologies Research Machine Learning in Materials Science Advanced Battery Materials and Technologies

Publication details

Journal / source
Batteries & Supercaps
Volume
9
Issue
1
Publication date
1 January 2026
Language
EN