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.

电能设备（例如电池）在现代社会中起着重要的作用，其广泛的传播应用程序跨越了各个部门，例如运输，医疗保健，通信和可再生能源生产。当前，在运行时评估此类设备的寿命是一项极具挑战性的任务。结果，在关键任务和/或情况下，这种设备不合时宜地失败的风险很大，导致安全问题和经济损失。当前，在运行期间，尚无一般的，有效的方法来在线识别内部弱点和监测不同电能设备的衰老过程和健康状况。 该项目为低成本的在线识别和条件监测电能设备的内部弱点提供了新的通用动态建模和系统识别方法，并使用其包装表面温度监测。 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，预测状况的意识，并提高对电能设备的老化和故障机制的理解，这将有助于设计更可靠的设备。 增强的状况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，并为K-12学校学生提供专注于动态系统建模和能源系统应用的识别的STEM教育。该项目将创建一个新的动态建模和基于系统识别的通用数学框架，用于使用热能设备的电力设备的内部弱点，用于非感知性的在线识别和调理。 该框架通过通过测量包装表面温度估计的设备的三维电热动力学的变化来结合内部弱点复杂老化过程的新通用解释。 基于这种解释，将开发一种新的数学建模方法，以通过高保真，基于物理学的建模，可量化的误差界限，在线参数识别降低订单模型和高级模型重新构造。 确定的参数将包含有关内部弱点的位置，老化过程和健康状况的信息，因此可用于对电能设备进行条件监测。 该框架将通过计算机模拟和实验研究来验证，以识别和监视功率半导体设备的内部弱点。 除了电能设备外，该研究还将为其他复杂物理系统的建模和诊断提供有利的功能。

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.