Smart Health Evaluation for Lithium-Ion Battery With Super-Short-Segment Charging.

Qinghua Li, Zhongbao Wei, Hongwen He, Jun Shen, Yang Li, Xiaoguang Yang, Mahinda Vilathgamuwa

Abstract read

In one paragraph

Article in Advanced science (Weinheim, Baden-Wurttemberg, Germany), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it

0cells of the map it votes in

1citing 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

1 citing paper in PubMed.

Smart Health Evaluation for Lithium-Ion Battery With Super-Short-Segment Charging.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025
Article

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

7 authors.

Qinghua LiNational Engineering Research Centre for Electric Vehicles, School of Mechanical Engineering, Beijing Institute of Technology, Beijing, 100081, China.

Zhongbao WeiNational Engineering Research Centre for Electric Vehicles, School of Mechanical Engineering, Beijing Institute of Technology, Beijing, 100081, China.ORCID https://orcid.org/0000-0003-0051-5648

Hongwen HeNational Engineering Research Centre for Electric Vehicles, School of Mechanical Engineering, Beijing Institute of Technology, Beijing, 100081, China.

Jun ShenNational Engineering Research Centre for Electric Vehicles, School of Mechanical Engineering, Beijing Institute of Technology, Beijing, 100081, China.

Yang LiDepartment of Electrical Engineering, Chalmers University of Technology, Gothenburg, SE-41296, Sweden.

Xiaoguang YangNational Engineering Research Centre for Electric Vehicles, School of Mechanical Engineering, Beijing Institute of Technology, Beijing, 100081, China.

Mahinda VilathgamuwaSchool of Electrical Engineering and Robotics, Queensland University of Technology, Brisbane, 4000, Australia.

Funding

National Key R&D Program of China 2022YFB2405700

6 · The paper itself

Abstract

Accurate state of health estimation is crucial for the reliable operation of lithium-ion batteries in electric vehicles. The charging curve contains valuable features for health evaluation, but real-world charging often lacks sufficient data due to the users' early recharging habits. A smart method is proposed for accurate battery health estimation using super-short charging segments. This method combines a degradation mechanism-guided Scale-Invariant Feature Transform for smart health feature identification with machine learning for health evaluation. Validation with 87 batteries with various chemistries, formats, and capacities from 6 manufacturers demonstrates its efficacy. Regardless of battery specifications, health features can be identified automatically from the charging data. The method promises high accuracy (estimation error as low as 1.97%) even with super-short charging covering 10% state of charge span, where all the existing health feature extraction approaches fail. This method provides new avenues for battery health evaluation in uncertain real-world electric vehicle applications.

Indexed as

batterycycle lifefeatures extractionmachine learning modelSOH estimation

Identifiers

PMID40716039

DOI10.1002/advs.202503583

PMCPMC12463020

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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.