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, Jianlin Zhou, Hailong Wang, Zongyou Yin, Libo Gao
TL;DR
This paper explores how flexible sensors and AI can improve battery health monitoring in complex conditions, enhancing safety and reliability.
Contribution
The paper introduces a 'sensing–AI–control' framework using flexible sensors and AI for efficient battery health monitoring.
Findings
Flexible sensors offer adaptability for monitoring batteries under complex operating conditions.
AI-powered data processing improves the accuracy and efficiency of battery state assessment.
Mainstream flexible sensing technologies like film sensors and optical fiber sensors are reviewed for battery monitoring.
Abstract
Flexible sensing technology enables battery health monitoring under complex operating conditions, overcoming the limitations of traditional monitoring methods.Artificial intelligence (AI) -powered data processing facilitates the construction of a "sensing–AI–control" framework, enhancing monitoring efficiency. Flexible sensing technology enables battery health monitoring under complex operating conditions, overcoming the limitations of traditional monitoring methods. Artificial intelligence (AI) -powered data processing facilitates the construction of a "sensing–AI–control" framework, enhancing monitoring efficiency. 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…
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Taxonomy
TopicsAdvanced Battery Technologies Research · Advanced Sensor and Energy Harvesting Materials · Innovative Energy Harvesting Technologies