Cylindrical Battery Fault Detection Under Extreme Fast Charging: A Physics-Based Learning Approach
Cylindrical Battery Fault Detection Under Extreme Fast Charging: A Physics-Based Learning Approach
复制标题
DOI：
10.1109/tec.2021.3112950
复制
发表时间：
2021-05
影响因子：
4.9
通讯作者：
Roya Firoozi;S. Sattarzadeh;Satadru Dey
中科院分区：
文献类型：
--
作者：
Roya Firoozi;S. Sattarzadeh;Satadru Dey
在极端快速充电中的高功率操作显著增加了电动汽车电池内部故障的风险，这可能导致电池加速失效。这些故障的早期检测对于电池安全和快速充电的广泛部署至关重要。在这种情况下，我们提出了一个实时检测框架的电池电压和热故障。电池故障检测中的一个主要挑战来自于传感器不准确、标称老化或未建模动态引起的不确定性的影响。受基于物理的学习的启发，我们探索了一种检测范式，该范式结合了基于物理的模型，基于模型的检测观察器和数据驱动的学习技术来应对这一挑战。具体而言，我们构造的检测观测器的基础上，实验确定的电化学-热模型，并随后设计观测器的调整参数，李雅普诺夫的稳定性理论。此外，我们利用高斯过程回归技术来学习模型和测量的不确定性，这反过来又帮助检测观测器区分故障和不确定性。这种不确定性学习本质上有助于抑制它们的影响，从而可能实现故障的早期检测。我们进行模拟和实验的情况下，所提出的故障检测方案验证潜在的基于物理的学习在早期检测电池故障。
High power operation in extreme fast charging significantly increases the risk of internal faults in Electric Vehicle batteries which can lead to accelerated battery failure. Early detection of these faults is crucial for battery safety and widespread deployment of fast charging. In this setting, we propose a real-time detection framework for battery voltage and thermal faults. A major challenge in battery fault detection arises from the effect of uncertainties originating from sensor inaccuracies, nominal aging, or unmodelled dynamics. Inspired by physics-based learning, we explore a detection paradigm that combines physics-based models, model-based detection observers, and data-driven learning techniques to address this challenge. Specifically, we construct the detection observers based on an experimentally identified electrochemical-thermal model, and subsequently design the observer tuning parameters following Lyapunov’s stability theory. Furthermore, we utilize Gaussian Process Regression technique to learn the model and measurement uncertainties which in turn aid the detection observers in distinguishing faults and uncertainties. Such uncertainty learning essentially helps suppressing their effects, potentially enabling early detection of faults. We perform simulation and experimental case studies on the proposed fault detection scheme verifying the potential of physics-based learning in early detection of battery faults.