Cover Feature: Combining a Data Driven and Mechanistic Model to Predict Capacity and Potential Curve‐Degradation (Batteries & Supercaps 10/2024)

The Cover Feature illustrates lithium-ion battery degradation. It demonstrates how individual aging modes—the loss of accessible active material from an electrode or the depletion of cyclable lithium ions—affect the capacities and balancing between the electrodes. These changes are visualized by color-coded surfaces that represent electrode potentials in the full cell′s cyclation window, transitioning from green to red to indicate degradation. Such alterations lead to a measurable capacity fade and changes in the full cell′s potential curve, as depicted by the differential voltage curve. The underlying work combines this mechanistic model with a data-driven model approach of the individual aging modes to predict both capacity fade and changes to the potential curve under various aging conditions. This will help to enhance understanding and prediction of battery degradation and can be the basis for a more precise onboard state-of-charge and state-of-health estimation of degraded batteries. More information can be found in the Research Article by J. Stadler and co-workers (DOI: 10.1002/batt.202400211).

Authors

Institutions

Publication Details

Journal
Batteries & Supercaps
Published
2024-10-01
DOI
https://doi.org/10.1002/batt.202481003
Primary Topic
Advanced Battery Technologies Research
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Cover Feature: Combining a Data Driven and Mechanistic Model to Predict Capacity and Potential Curve‐Degradation (Batteries & Supercaps 10/2024)

Arnulf Latz, Jochen Stadler, Johannes Philipp Fath, Madeleine Ecker
Batteries & Supercaps
Advanced Battery Technologies Research
article

Cover Feature: Combining a Data Driven and Mechanistic Model to Predict Capacity and Potential Curve‐Degradation (Batteries & Supercaps 10/2024)

Arnulf Latz, Jochen Stadler, Johannes Philipp Fath, Madeleine Ecker
article en

Abstract

The Cover Feature illustrates lithium-ion battery degradation. It demonstrates how individual aging modes—the loss of accessible active material from an electrode or the depletion of cyclable lithium ions—affect the capacities and balancing between the electrodes. These changes are visualized by color-coded surfaces that represent electrode potentials in the full cell′s cyclation window, transitioning from green to red to indicate degradation. Such alterations lead to a measurable capacity fade and changes in the full cell′s potential curve, as depicted by the differential voltage curve. The underlying work combines this mechanistic model with a data-driven model approach of the individual aging modes to predict both capacity fade and changes to the potential curve under various aging conditions. This will help to enhance understanding and prediction of battery degradation and can be the basis for a more precise onboard state-of-charge and state-of-health estimation of degraded batteries. More information can be found in the Research Article by J. Stadler and co-workers (DOI: 10.1002/batt.202400211).

Batteries & SupercapsVol. 7(10)
Mercedes-Benz (Germany) (DE), Universität Ulm (DE), Helmholtz-Institute Ulm (DE)
Openalex Percentile: Top 68%
Advanced Battery Technologies Research
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.