Edge AI for Battery Management: Efficient SoC and SoH Prediction for Li-Ion Batteries with Neural Networks

This work was submitted to and accepted for presentation at the European Conference on Edge AI Technologies and Applications (21–23 October 2024). It was presented at the conference but has not been formally published in proceedings or journals. This version is made available on Zenodo to ensure open access and citability, in line with open science and dissemination requirements.

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Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2024-09-02
DOI
https://doi.org/10.5281/zenodo.17036369
Primary Topic
Geochemistry and Geologic Mapping
Type
article
Field-Weighted Citation Impact
0.00
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article

Edge AI for Battery Management: Efficient SoC and SoH Prediction for Li-Ion Batteries with Neural Networks

Zenodo (CERN European Organization for Nuclear Research)
Geochemistry and Geologic Mapping
article

Edge AI for Battery Management: Efficient SoC and SoH Prediction for Li-Ion Batteries with Neural Networks

article en

Abstract

This work was submitted to and accepted for presentation at the European Conference on Edge AI Technologies and Applications (21–23 October 2024). It was presented at the conference but has not been formally published in proceedings or journals. This version is made available on Zenodo to ensure open access and citability, in line with open science and dissemination requirements.

Zenodo (CERN European Organization for Nuclear Research)
Silicon Austria Labs (Austria) (AT)
Openalex Percentile: Top 49%
Geochemistry and Geologic Mapping
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Edge AI for Battery Management: Efficient SoC and SoH Prediction for Li-Ion Batteries with Neural Networks · Zenodo (CERN European Organization for Nuclear Research) (2024) | TGRS Research Map | TGRS