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.
