The objective of this project is to create a dynamic reliability assessment platform for lithium-ion (Li-ion) battery. Real-time diagnostics/prognostics and predictive maintenance/control of Li-ion battery are essential for reliable and safe battery operation in a wide-range of battery-powered applications, from hybrid and electric vehicles (HEVs/EVs) and medical devices, to the emerging smart grid and all electric airplanes. The proposed platform enables a battery management system (BMS) to develop predictive maintenance/control of a Li-ion battery through concurrently analyzing degradation mechanisms and anticipating failure modes. Successful execution of this research will advance our understanding of how to extend life and prevent catastrophic failure of Li-ion batteries, and will potentially lead to development of battery-powered devices that are more durable and safer than current devices. This project will disseminate research findings to battery industry by demonstrating the platform with a Li-ion battery in an implantable application, through collaboration with a leading industry partner. The project will offer a wide range of education and outreach programs, including 1) incorporating research findings into the Reliability Engineering curriculum, 2) leveraging university research programs to attract undergraduate and K-12 students to engineering career, and 3) organizing paper sessions and panels on Design for Failure Prevention of Li-Ion Battery in major conferences.
To date, real-time diagnostics/prognostics of Li-ion battery has been exploited only empirically and largely in isolation with the underlying degradation mechanisms. This may be attributed to the lack of cognizance of the causal relationship between degradation mechanisms and failure modes. This project will bridge the gap between physical mechanisms and functional failures by creating a dynamic reliability assessment platform that facilitates a synergistic integration of physics-based modeling and sensor-based prognostics. The platform will allow for: 1) identification and quantitative analysis of multiple degradation mechanisms through online estimation of the degradation parameters; and 2) anticipation of the failure modes through online prediction of their remaining useful lives (RULs). The creation of the platform involves three research thrusts: 1) validation of multiphysics models, which updates multiphysics battery models using high precision charge-discharge cycling data; 2) training of health estimators, which adopts machine learning to quantitatively analyze multiple degradation mechanisms from a single measurement of charge curve; and 3) prognostics of failure modes, which leverages the quantitative degradation analysis for prediction of failure mode RULs. The platform provides the methods and tools needed to leverage prognostics and prognostics-informed predictive maintenance/control for achieving the failure prevention capability of BMS. Although this project focuses on the specific case of dynamically updating battery reliability with measured electrical data, the methodology will be applicable to other engineering cases in which measured data are used to dynamically update reliability estimates that support maintenance/control decision making.
