汽车工业
电池(电)
系统工程
计算机科学
推进
工程类
航空航天工程
功率(物理)
物理
量子力学
作者
Xia Zeng,Maitane Berecibar
标识
DOI:10.1038/s44172-025-00383-9
摘要
As the automotive industry undergoes a major shift to electric propulsion, reliable assessment of battery health and potential safety issues is critical. This review covers advances in sensor technology, from mechanical and gas sensors to ultrasonic imaging techniques that provide insight into the complex structures and dynamics of lithium-ion batteries. In addition, we explore the integration of physics-guided machine learning methods with multi-sensor systems to improve the accuracy of battery modeling and monitoring. Challenges and opportunities in prototyping and scaling these multi-sensor systems are discussed, highlighting both current limitations and future potential. The purpose of this study is to provide a comprehensive overview of the current status, challenges, and future directions of combining sensors with physically guided methods for future vehicle battery management systems.
科研通智能强力驱动
Strongly Powered by AbleSci AI