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.

该项目的主要目标是研究和开发锂离子电池和系统的健康状态（SOH）诊断和早期故障检测方法，这些方法对于越来越多的使用锂离子电池系统的应用中的性能，安全性和效率变得越来越重要和关键。仅举几例，这些应用包括电动和混合动力汽车（EV和HEV），消费便携式电子产品，更多电动飞机（MEA），航空航天系统以及可再生能源大规模集成到电网中等。随着电池的老化，其SOH会缓慢下降，导致容量和功率下降。与缓慢的老化过程相比，电池故障（如短路和过热）是可能导致电池系统灾难性故障（如热失控和着火）的更快过程。此外，高能量密度电池的研究和开发预计将使电池的灾难性故障成为一个更大的问题。该项目旨在开发智能储能电池系统的方法，允许在线实时诊断和估计电池系统的健康状况，并提供早期故障检测以减轻故障。相关的控制技术、算法和架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交流阻抗行为以及在不同负载条件下作为即将发生的故障的函数;（2）开发利用Li-的在线实时参数的在线实时自适应算法和控制方案用于SOH诊断和早期故障检测的离子电池;以及（3）研究可能延迟/减轻故障的方法。这可以部分地通过以下来促进：（1）允许通过功率转换器控制和其他参数进行在线实时AC阻抗估计而不中断系统操作和性能的实用方法;以及（2）通过利用作为实时电池SOH的函数的能量共享控制，基于每个电池或模块的健康状况来自适应地利用每个电池或模块。该项目将为储能系统的管理及其安全、健康诊断和早期故障检测做出重大贡献。储能管理和安全的进步影响着许多关键应用，包括许多对我们日常生活很重要的应用，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等。安全可靠的电池系统降低了灾难性故障的风险，灾难性故障可能导致不便和/或伤害，并且可能是昂贵的。另一方面，储能系统的进步可以提高可再生能源的利用率，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。项目成果将通过参考期刊和会议出版物、课堂教育部分、研讨会、讲座和公众演示进行传播。