Evidence map›Paper›PMID 41486324›Full record

ReviewNano-micro letters2026

Flexible Sensors for Battery Health Monitoring.

Xin Wang, Haiyan Zhang, Xinyi Qi, Sheng Chen, Zekai Huang, Jinwei Zhao, Yihang Wang, Dezhi Wu, Gaofeng Zheng, Chenyang Xue and 4 more

Abstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in Nano-micro letters, 2026. 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.

  1. 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

14 authors.

Xin Wang *Pen-Tung Sah Institute of Micro-Nano Science and Technology, Xiamen University, Xiamen, 361102, People's Republic of China.
Haiyan Zhang *Science and Technology On Vacuum Technology and Physics Laboratory, Lanzhou Institute of Physics, Chinese Academy of Space Technology, Lanzhou, 730000, People's Republic of China.
Xinyi QiSchool of Electronic Science and Engineering (National Model Microelectronics College), Xiamen University, Xiamen, 361102, People's Republic of China.
Sheng ChenSchool of Electronic Science and Engineering (National Model Microelectronics College), Xiamen University, Xiamen, 361102, People's Republic of China.
Zekai HuangPen-Tung Sah Institute of Micro-Nano Science and Technology, Xiamen University, Xiamen, 361102, People's Republic of China.
Jinwei ZhaoPen-Tung Sah Institute of Micro-Nano Science and Technology, Xiamen University, Xiamen, 361102, People's Republic of China.
Yihang WangPen-Tung Sah Institute of Micro-Nano Science and Technology, Xiamen University, Xiamen, 361102, People's Republic of China.
Dezhi WuPen-Tung Sah Institute of Micro-Nano Science and Technology, Xiamen University, Xiamen, 361102, People's Republic of China.
Gaofeng ZhengPen-Tung Sah Institute of Micro-Nano Science and Technology, Xiamen University, Xiamen, 361102, People's Republic of China.
Chenyang XuePen-Tung Sah Institute of Micro-Nano Science and Technology, Xiamen University, Xiamen, 361102, People's Republic of China.
Jianlin ZhouShenzhen Modulus Technology Co., Shenzhen, 518054, People's Republic of China.
Hailong WangInnovation Laboratory for Sciences and Technologies of Energy Materials of Fujian Province (IKKEM), Xiamen, 361102, People's Republic of China. wanghl@xmu.edu.cn.
Zongyou YinResearch School of Chemistry, Australian National University, Canberra, ACT, 2601, Australia. zongyou.yin@anu.edu.cn.
Libo GaoPen-Tung Sah Institute of Micro-Nano Science and Technology, Xiamen University, Xiamen, 361102, People's Republic of China. lbgao@xmu.edu.cn.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

With the widespread application of lithium batteries in electric vehicles and energy storage systems, battery-related safety and reliability issues have become increasingly prominent. Conventional monitoring methods often struggle to address dynamic changes under complex operando. In recent years, flexible sensing technology has emerged as a promising solution for battery health monitoring due to its high adaptability and conformability to complex structures. Meanwhile, empowered by artificial intelligence (AI) for data analysis, the collected data enables efficient and accurate state assessment, offering robust support for accident prevention. Against this background, this paper first explores the integrated applications of flexible sensors in battery health monitoring and their unique advantages in addressing complex battery operating conditions, while analyzing the potential of AI in battery state analysis. Subsequently, it systematically reviews mainstream flexible sensing technologies (e.g., film sensors, thermocouples, and optical fiber sensors), elucidating their mechanisms for revealing intricate internal battery processes during operation. Finally, the paper discusses AI's role in enhancing monitoring efficiency and accuracy, and envisions future research directions and application prospects. This work aims to provide technical references for the battery health monitoring field as well as promote the application of flexible sensing technologies in improving battery system safety and reliability.

Indexed as

Artificial intelligenceBattery health monitoringFlexible sensing technologyLithium batterySafety

Identifiers

PMID41486324
PMCPMC12765797

What OpenQuestion holds

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Read underepoch 390

Registered trials

None linked

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.