Issue

Vol. 18 (2026)

Flexible Sensors for Battery Health Monitoring

https://doi.org/10.1007/s40820-025-01999-4
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 Qi
School of Electronic Science and Engineering (National Model Microelectronics College), Xiamen University, Xiamen 361102, People’s Republic of China
Sheng Chen
School of Electronic Science and Engineering (National Model Microelectronics College), Xiamen University, Xiamen 361102, People’s Republic of China
Zekai Huang
Pen‑Tung Sah Institute of Micro‑Nano Science and Technology, Xiamen University, Xiamen 361102, People’s Republic of China
Jinwei Zhao
Pen‑Tung Sah Institute of Micro‑Nano Science and Technology, Xiamen University, Xiamen 361102, People’s Republic of China
Yihang Wang
Pen‑Tung Sah Institute of Micro‑Nano Science and Technology, Xiamen University, Xiamen 361102, People’s Republic of China
Dezhi Wu
Pen‑Tung Sah Institute of Micro‑Nano Science and Technology, Xiamen University, Xiamen 361102, People’s Republic of China
Gaofeng Zheng
Pen‑Tung Sah Institute of Micro‑Nano Science and Technology, Xiamen University, Xiamen 361102, People’s Republic of China
Chenyang Xue
Pen‑Tung Sah Institute of Micro‑Nano Science and Technology, Xiamen University, Xiamen 361102, People’s Republic of China
Jianlin Zhou
Shenzhen Modulus Technology Co., Shenzhen 518054, People’s Republic of China
Hailong Wang
Innovation Laboratory for Sciences and Technologies of Energy Materials of Fujian Province (IKKEM), Xiamen 361102, People’s Republic of China
Zongyou Yin
Research School of Chemistry, Australian National University, Canberra, ACT 2601, Australia
Libo Gao
Pen‑Tung Sah Institute of Micro‑Nano Science and Technology, Xiamen University, Xiamen 361102, People’s Republic of China

Corresponding Author: Libo Gao

lbgao@xmu.edu.cn

Nano-Micro Letters, Vol. 18 (2026), Article Number: 154

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.


Highlights:
1 Flexible sensing technology enables battery health monitoring under complex operating conditions, overcoming the limitations of traditional monitoring methods.
2 Artificial intelligence (AI) -powered data processing facilitates the construction of a "sensing–AI–control" framework, enhancing monitoring efficiency.

Keywords

Lithium battery Battery health monitoring Flexible sensing technology Safety Artificial intelligence
Wang, Xin, Haiyan Zhang, Xinyi Qi, Sheng Chen, Zekai Huang, Jinwei Zhao, Yihang Wang, Dezhi Wu, Gaofeng Zheng, Chenyang Xue, Jianlin Zhou, Hailong Wang, Zongyou Yin, and Libo Gao. 2026. “Flexible Sensors for Battery Health Monitoring”. Nano-Micro Letters 18 (January):154. https://doi.org/10.1007/s40820-025-01999-4.
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