Online Nonintrusive Identification and Monitoring of Internal Weak Points of Electro Energy Devices Using Package Surface Temperature

利用封装表面温度在线非侵入式识别和监测电能设备的内部薄弱点

• 批准号: 1663562
• 负责人: Wei Qiao
• 金额: $ 33.79万
• 依托单位: University of Nebraska-Lincoln
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-06-15 至 2021-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1663562&HistoricalAwards=false
• 关键词: Online; Nonintrusive; Identification; Monitoring; Internal

Electro-energy devices (such as batteries) play an essential role in modern society with their wide spread applications that span various sectors such as transportation, healthcare, communication and renewable energy generation to name a few. Currently, assessing the longevity of such devices while they are in operation is an extremely challenging task. As a result, there exists a huge risk of untimely failure of such devices in critical missions and/or situations leading to safety issues and financial loss. Currently, there are no general, effective approaches to online identification of internal weak points and monitoring of the aging processes and health conditions of different electro-energy devices while in operation. This project formulates and demonstrates new universal dynamic modeling and system identification methodologies for low-cost online identification and condition monitoring of internal weak points of electro energy devices using their package surface temperatures. The methodologies do not intrude upon the devices or interrupt their operation. The results of this research will enhance real-time, predictive condition awareness and improve the understanding of aging and failure mechanisms of electro energy devices, which will help in designing more reliable devices. Enhanced condition awareness and design will greatly improve safety and reliability and reduce the cost and financial risk of using electro energy devices. This project will provide interdisciplinary research training for graduate and undergraduate students and STEM education for K-12 school students focusing on dynamic system modeling and identification for energy systems applications.This project will create a new dynamic modeling and system identification-based universal mathematical framework for nonintrusive online identification and condition monitoring of internal weak points of electro energy devices using their thermal signature. The framework incorporates a new universal interpretation for the complex aging processes of internal weak points via the changes in the three-dimensional electrothermal dynamics of the devices estimated by measuring package surface temperatures. Based on this interpretation, a new mathematical modeling approach will be developed to adaptively characterize the aging-related electrothermal dynamics of the devices via high-fidelity, physics-based modeling, automated model order reduction with quantifiable error bounds, online parameter identification for the reduced-order model, and high-order model reconstruction. The identified parameters will contain information on locations, aging processes, and health conditions of the internal weak points and therefore can be used for condition monitoring of the electro energy devices. The framework will be validated by computer simulation and experimental studies to identify and monitor internal weak points of power semiconductor devices. In addition to electro energy devices, the research will provide enabling capabilities for modeling and diagnostics of other complex physical systems.

电能源设备（例如电池）在现代社会中发挥着重要作用，其广泛的应用涵盖交通、医疗保健、通信和可再生能源发电等各个领域。目前，评估此类设备运行时的寿命是一项极具挑战性的任务。因此，在关键任务和/或情况下，此类设备存在过早失效的巨大风险，从而导致安全问题和财务损失。目前，还没有通用、有效的方法来在线识别内部薄弱环节并监测不同电能设备在运行过程中的老化过程和健康状况。 该项目制定并演示了新的通用动态建模和系统识别方法，用于利用封装表面温度对电能设备的内部弱点进行低成本在线识别和状态监测。 该方法不会侵入设备或中断其操作。 这项研究的结果将增强实时、预测性的状态意识，并提高对电能设备老化和故障机制的理解，这将有助于设计更可靠的设备。 增强的状态意识和设计将大大提高安全性和可靠性，并降低使用电能设备的成本和财务风险。 该项目将为研究生和本科生提供跨学科研究培训，为K-12学生提供STEM教育，重点关注能源系统应用的动态系统建模和识别。该项目将为能源系统应用创建一个新的基于动态建模和系统识别的通用数学框架利用热特征对电能设备的内部弱点进行非侵入式在线识别和状态监测。 该框架通过测量封装表面温度估计的器件三维电热动力学的变化，对内部弱点的复杂老化过程提出了新的通用解释。 基于这种解释，将开发一种新的数学建模方法，通过高保真、基于物理的建模、具有可量化误差范围的自动模型降阶、在线参数识别来自适应地表征器件与老化相关的电热动力学。 -阶模型和高阶模型重建。 识别出的参数将包含有关内部薄弱点的位置、老化过程和健康状况的信息，因此可用于电能设备的状态监测。 该框架将通过计算机模拟和实验研究进行验证，以识别和监控功率半导体器件的内部弱点。 除了电能设备之外，该研究还将提供其他复杂物理系统的建模和诊断的支持能力。

项目成果

An Enhanced Hybrid Battery Model

• DOI: 10.1109/tec.2019.2935700
• 发表时间: 2019-12-01
• 期刊: IEEE TRANSACTIONS ON ENERGY CONVERSION
• 影响因子: 4.9
• 作者: Kim, Taesic;Qiao, Wei;Qu, Liyan
• 通讯作者: Qu, Liyan

Enhanced Particle Filtering for Bearing Remaining Useful Life Prediction of Wind Turbine Drivetrain Gearboxes

• DOI: 10.1109/tie.2018.2866057
• 发表时间: 2019-06-01
• 期刊: IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS
• 影响因子: 7.7
• 作者: Cheng, Fangzhou;Qu, Liyan;Hao, Liwei
• 通讯作者: Hao, Liwei

Wind Turbine Drivetrain Gearbox Fault Diagnosis Using Information Fusion on Vibration and Current Signals

• DOI: 10.1109/tim.2021.3083891
• 发表时间: 2021
• 期刊: IEEE Transactions on Instrumentation and Measurement
• 影响因子: 5.6
• 作者: Yayu Peng;W. Qiao;Fangzhou Cheng;Liyan Qu

Compressive Sensing-Based Missing-Data-Tolerant Fault Detection for Remote Condition Monitoring of Wind Turbines

• DOI: 10.1109/tie.2021.3057039
• 发表时间: 2022-02
• 期刊: IEEE Transactions on Industrial Electronics
• 影响因子: 7.7
• 作者: Yayu Peng;W. Qiao;Liyan Qu

A High-Accuracy, Low-Order Thermal Model of SiC MOSFET Power Modules Extracted from Finite Element Analysis via Model Order Reduction

通过模型降阶从有限元分析中提取 SiC MOSFET 功率模块的高精度、低阶热模型

• DOI: 10.1109/ecce.2019.8912839
• 发表时间: 2019
• 期刊: 2019 IEEE Energy Conversion Congress and Exposition (ECCE
• 作者: Entzminger, Cameron;Qiao, Wei;Qu, Liyan;Hudgins, Jerry L.
• 通讯作者: Hudgins, Jerry L.