Recent battery failure incidents of Boeing 787 Dreamliner, Tesla Model S and Samsung Galaxy Note 7 strongly emphasize the need for smarter battery safety solutions. This project aims to enhance battery safety by developing a novel real-time diagnostics framework. The proposed framework intends to provide battery safety solutions by designing real-time algorithms that diagnose and control various battery faults. Besides enhancing the safety of battery-powered systems such as electric aircrafts, medical equipment, and consumer electronics, safer batteries will accelerate penetration of electrified vehicles and renewable energy systems, leading to environmental benefits, energy security, and economic stability. The results of this project will also advance the understanding of real-time battery failures, providing insights to the battery scientists/designers to create better batteries. The theoretical control/diagnostics tools developed for the project can be readily extended to other multi-physics systems such as fuel cells, buildings, and micro electro-mechanical systems. The results will be disseminated through principal investigator's course and publications, enhancing the knowledge of next generation technological workforce in automotive/energy sectors. Finally, this project will create collaborative/mentoring opportunities with the National Renewable Energy Laboratory, non-PhD granting institutions such as the Community College of Denver, and underrepresented student
The goal of this project is to develop battery safety solutions by designing multi-physics model-based real-time algorithms that (i) diagnose electrochemical, mechanical, electrical and thermal faults at early stages, and (ii) take corrective control action to minimize the fault effect/propagation. These algorithms will add fault-tolerance to battery management systems, besides maximizing energy utilization, cycle life and reliability. From real-time diagnosis and control viewpoint, this project will address two key issues in batteries: lack of measurements (weak observability) and limited actuation. The following research tasks will be performed: (i) multi-physics-based battery fault modeling, (ii) analysis to determine ability to diagnose faults, (iii) distinguishing faults from modeling and measurement uncertainties, (iv) real-time detection, isolation and estimation of faults, and (v) real-time control of fault propagation. These research tasks will produce the following tools: (i) offline tools for analysis to determine ability to diagnose faults, and (ii) online algorithms for fault diagnostics and fault-tolerant control. Such tools/algorithms will be developed utilizing a combination of decentralized and interconnected systems theory, multi-objective optimal control theory, and data-driven uncertainty modeling techniques. The performance of the algorithms will be validated and quantified on real-world battery failure data.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
