Flexible Sensors for Battery Health Monitoring - Wang, Xin; Zhang, Haiyan; Qi, Xinyi; Chen, Sheng; Huang, Zekai; Zhao, Jinwei; Wang, Yihang; Wu, Dezhi; Zheng, Gaofeng; Xue, Chenyang; Zhou, Jianlin; Wang, Hailong; et al. (Nano-Micro Letters, 2026)
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.