State-of-Health Diagnosis and Early Fault Detection for Lithium-Ion Battery Systems

锂离子电池系统的健康状态诊断和早期故障检测

• 批准号: 1509824
• 负责人: Jaber Abu Qahouq
• 金额: $ 18.15万
• 依托单位: University of Alabama Tuscaloosa
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-08-01 至 2022-02-28
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1509824&HistoricalAwards=false
• 关键词: State; Health; Diagnosis; Early; Fault

The main goal of the project is to investigate and develop methods for State-of-Health (SOH) diagnosis and early fault detection for Lithium-Ion (Li-Ion) batteries and systems, which are becoming increasingly important and critical for the performance, safety and efficiency in the growing number of applications where Li-Ion battery systems are utilized. To mention a few, these applications include Electric and Hybrid-Electric Vehicles (EVs and HEVs), Consumer Portable Electronics, More Electric Aircraft (MEA), Aerospace Systems, and the large-scale integration of renewable energy into the power grid, among others. As a battery ages, its SOH slowly degrades, resulting in capacity and power degradation. Compared to the slow aging process, battery faults such as short-circuiting and overheating are faster processes that might cause catastrophic failure of the battery system, such as thermal runaway and catching fire. Moreover, the research and development of batteries with high energy densities is expected to make catastrophic failures of batteries a larger issue. This project aims at developing methods for smart energy storage battery systems which allow for online real-time diagnosis and estimation of the health of the battery system, and provide early fault detection in order to alleviate failures. Related control technology, algorithms, and architectures will be devised and developed in the course of the project. The project will (1) conduct thorough experimental study and analysis on the online real-time behavior of battery system parameters including the behavior of the electrochemical AC impedance of Li-Ion batteries and under different loading conditions as a function of upcoming faults; (2) develop online real-time adaptive algorithms and control schemes that utilize the online real-time parameters of Li-Ion batteries for SOH diagnosis and early fault detection; and (3) investigate methods that potentially can delay/alleviate faults. This might partially be facilitated by: (1) practical methods that allow for online real-time AC impedance estimation through power converter control and other parameters without the interruption of system operation and performance; and (2) adaptive utilization of each cell or module based on its health by utilizing energy sharing control as a function of real-time battery SOH. The project will make significant contributions to the management of energy storage systems and their safety, health diagnosis, and early fault detection. Advances in energy storage management and safety impact many critical applications including many that are important for our daily lives such as in consumer electronics, aerospace, medical, military, electric and hybrid vehicles, and power grid energy storage applications, among others. Safe and reliable battery systems reduce the risk of catastrophic failure that can cause inconvenience and/or injury and can be costly. On the other hand, advances in energy storage systems can enable increased utilization of renewable energy sources and therefore reduction in greenhouse gas emissions, reduction in dependence on foreign oil imports and resources, and support U.S. economic and environmental security. The project results will be disseminated through refereed journal and conference publications, classroom educational components, seminars, lectures and public demonstrations.

该项目的主要目的是调查和开发锂离子（锂离子）电池和系统的诊断和早期故障检测的方法，这些方法变得越来越重要，对于使用锂离子电池系统的越来越多的应用中的性能，安全性和效率至关重要。需要提到的一些应用包括电动和混合动力车（EV和HEVS），消费者便携式电子设备，更多的电动飞机（MEA），航空航天系统以及将可再生能源大规模整合到电网等。随着电池的增加，它的SOH慢慢降解，导致容量和功率降解。与缓慢的老化过程相比，短路和过热等电池故障是更快的过程，可能会导致电池系统的灾难性故障，例如热失控和烧火。此外，预计具有高能量密度的电池的研究和开发将使电池的灾难性故障成为更大的问题。该项目旨在开发用于智能能源电池系统的方法，以便在线实时诊断和估算电池系统的健康状况，并提供早期故障检测以减轻故障。相关控制技术，算法和体系结构将在项目过程中设计和开发。 该项目将（1）对电池系统参数的在线实时行为进行彻底的实验研究和分析，包括锂离子电池的电化学AC阻抗的行为，以及在不同的负载条件下与即将发生故障的关系； （2）开发在线实时自适应算法和控制方案，该方案利用锂离子电池的在线实时参数进行SOH诊断和早期故障检测； （3）研究可能延迟/减轻断层的方法。这可能会通过以下方式部分促进：（1）实用方法，这些方法允许通过电源转换器控制和其他参数进行在线实时AC阻抗估算，而不会中断系统操作和性能； （2）每个单元或模块的自适应利用基于其健康状况，通过利用能量共享控制作为实时电池SOH的函数。该项目将为储能系统的管理及其安全性，健康诊断和早期故障检测做出重大贡献。能源管理和安全的进步影响了许多关键应用，包括许多对我们日常生活重要的重要应用，例如消费电子，航空航天，医疗，军事，电动和混合动力汽车以及电网储能应用程序等。安全可靠的电池系统降低了灾难性故障的风险，这可能会导致不便和/或受伤，并且可能是昂贵的。另一方面，储能系统的进步可以增加对可再生能源的利用，从而减少温室气体排放，减少对外国石油进口和资源的依赖，并支持美国的经济和环境安全。该项目的结果将通过被指导的期刊和会议出版物，课堂教育组成部分，研讨会，讲座和公开演示来传播。