Aging-Aware Equivalent Circuit Model for SOH Estimation in Lithium-Ion Batteries

Li-ion battery internal impedance is a crucial pa-rameter for state of health estimation. In this paper, a 3RC equivalent circuit model of the battery is considered. This model is also able to simulate the aging of the battery by extracting the battery parameter dependencies with aging. By observing the changes in the model parameters, it is possible to extract the battery state of heath. This means that starting from an impedance measurement it is possible to estimate the state of health of the battery. This could be a very attractive solution for the battery swapping industry, where keeping track of the aging can help identify early signs of degradation or potential safety hazards, thereby increasing customer satisfaction, optimizing operational efficiency and maximizing the return of investment. In this work, a neural network for state of health estimation from internal resistance is developed and it is able to correctly estimate state of health with a mean absolute relative error of 4.25%

Authors

Institutions

Publication Details

Published
2024-08-04
DOI
https://doi.org/10.1109/intelec60315.2024.10678967
Citations
4
Primary Topic
Advanced Battery Technologies Research
Type
article
Field-Weighted Citation Impact
0.81
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Aging-Aware Equivalent Circuit Model for SOH Estimation in Lithium-Ion Batteries

Pallavi Bharadwaj, Remus Teodorescu, Roberta Di Fonso
4 citations
Advanced Battery Technologies Research
0.81
article

Aging-Aware Equivalent Circuit Model for SOH Estimation in Lithium-Ion Batteries

Pallavi Bharadwaj, Remus Teodorescu, Roberta Di Fonso
article en
4 citations

Abstract

Li-ion battery internal impedance is a crucial pa-rameter for state of health estimation. In this paper, a 3RC equivalent circuit model of the battery is considered. This model is also able to simulate the aging of the battery by extracting the battery parameter dependencies with aging. By observing the changes in the model parameters, it is possible to extract the battery state of heath. This means that starting from an impedance measurement it is possible to estimate the state of health of the battery. This could be a very attractive solution for the battery swapping industry, where keeping track of the aging can help identify early signs of degradation or potential safety hazards, thereby increasing customer satisfaction, optimizing operational efficiency and maximizing the return of investment. In this work, a neural network for state of health estimation from internal resistance is developed and it is able to correctly estimate state of health with a mean absolute relative error of 4.25%

Indian Institute of Technology Gandhinagar (IN), Aalborg University (DK)
Affordable and clean energy
Openalex Percentile: Top 29%
Advanced Battery Technologies Research
0.81
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.

Aging-Aware Equivalent Circuit Model for SOH Estimation in Lithium-Ion Batteries — Pallavi Bharadwaj, Remus Teodorescu, et al. · (2024) | TGRS Research Map | TGRS