Zeng Xia, Berecibar Maitane
Electromobility Research Centre (MOBI), Department of Electrical Engineering and Energy Technology, Vrije Universiteit Brussel, Brussels, Belgium.
Commun Eng. 2025 Mar 11;4(1):44. doi: 10.1038/s44172-025-00383-9.
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.
随着汽车行业向电动推进系统发生重大转变，对电池健康状况和潜在安全问题进行可靠评估至关重要。本综述涵盖了传感器技术的进展，从机械和气体传感器到超声成像技术，这些技术有助于深入了解锂离子电池的复杂结构和动态特性。此外，我们还探讨了将物理引导的机器学习方法与多传感器系统相结合，以提高电池建模和监测的准确性。文中讨论了这些多传感器系统在原型制作和规模化应用方面的挑战与机遇，突出了当前的局限性和未来的潜力。本研究的目的是全面概述将传感器与物理引导方法相结合应用于未来车辆电池管理系统的现状、挑战及未来发展方向。
