Evidence map›Paper›PMID 40896769›Full record

ArticleRSC advances2025

Beyond drive cycles: mapping the intricacies of electric vehicle battery health in diverse environments and driving conditions.

Sai Krishna Mulpuri, Bikash Sah, Praveen Kumar

Abstract read
In one paragraph

Article in RSC advances, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Sai Krishna MulpuriDepartment of Electronics and Electrical Engineering, Indian Institute of Technology Guwahati Assam 781039 India m.sai@iitg.ac.in.ORCID https://orcid.org/0000-0001-5175-0272
Bikash SahChair of Interconnected Automation Systems, University of Siegen Hölderlinstrasse 3, Building A Siegen 57076 Germany Bikash.Sah@uni-siegen.de.ORCID https://orcid.org/0000-0002-0307-6727
Praveen KumarDepartment of Electronics and Electrical Engineering, Indian Institute of Technology Guwahati Assam 781039 India m.sai@iitg.ac.in.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

As electric two-wheelers become increasingly prevalent in emerging markets, understanding the real-world degradation of their batteries is crucial for ensuring reliability, longevity, and cost-effectiveness. Traditional battery aging studies are heavily based on standard drive cycles that do not capture the variability introduced by diverse user behaviors, regional conditions, and charging habits. This study proposes a comprehensive and adaptable framework for evaluating the end-of-life (EOL) of EV batteries in realistic usage scenarios. The methodology incorporates key metrics of the drive cycle, including acceleration patterns, rest periods, charging frequency, and rates, in multiple daily driving scenarios, three distinct geographic regions, and varying climatic conditions. By linking these operational parameters to electrochemical degradation phenomena, this work reveals the critical influence of user-specific behavior on capacity fade. The insights generated are not only scientifically grounded but also practically relevant to stakeholders across the EV ecosystem. For cell and vehicle OEMs, the findings support region-specific cell design and lifecycle prediction; for EV users, the study offers a clearer understanding of how personal usage affects battery health. Ultimately, this work bridges the gap between lab-scale testing and real-world degradation, paving the way for smarter battery design, personalized usage strategies, and sustainable EV adoption.

Identifiers

PMID40896769
PMCPMC12395550

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.