From Sparse Privacy-Compliant Data to Accurate Assessment: A Self-Supervised Paradigm for EV Battery Capacity Estimation

Reliable assessment of EV battery capacity is pivotal for addressing user anxieties regarding EV performance and reliability, yet developing models that work in real-world scenarios faces two critical barriers: strict privacy regulations that restrict

Authors

Naranhvwar Tvlg

Institutions

Mongolian University of Science and Technology (MN)

Publication Details

Published: 2025-10-13
DOI: https://doi.org/10.22541/au.176038142.20913359/v1
Primary Topic: Advanced Battery Technologies Research
Type: article
Field-Weighted Citation Impact: 0.00

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article

From Sparse Privacy-Compliant Data to Accurate Assessment: A Self-Supervised Paradigm for EV Battery Capacity Estimation

Naranhvwar Tvlg

Oct 13, 2025

Advanced Battery Technologies Research

article

From Sparse Privacy-Compliant Data to Accurate Assessment: A Self-Supervised Paradigm for EV Battery Capacity Estimation

Naranhvwar Tvlg

October 13, 2025article en

Abstract

Mongolian University of Science and Technology (MN)

Openalex Percentile: Top 69%

Advanced Battery Technologies Research

DOI Open Access PDF OpenAlex

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From Sparse Privacy-Compliant Data to Accurate Assessment: A Self-Supervised Paradigm for EV Battery Capacity Estimation — Naranhvwar Tvlg · (2025) | TGRS Research Map | TGRS