Rechargeable battery systems represent a key enabler for several technologies such as electrified transportation, smart grid, renewables, and green buildings. These systems are increasingly recognized as a driver for moving the world forward into a sustainable energy era. Despite their ever-growing presence, the performance and safety of these systems are fundamentally constrained by how the battery cells are connected inside. Typically, these cells are hardwired. As a result, there is no flexibility to overcome battery failures caused by defects in individual cells, whether created during manufacturing or through malfunction of a cell. This has led to greater interest in reconfigurable battery systems, which use rapid reconfigurable connections between cells to overcome these problems. While there is rapid growth of power electronics architecture and circuit design, the management algorithms to control these devices are not improving at the same pace. This project aims to develop a new control paradigm, through which the system's input control and connection topology adjustment are achieved concurrently. This research, if successful, will significantly improve the reconfigurable battery systems and accelerate their use across diverse sectors. The research-integrated education program will engage K-12, undergraduate, and graduate students through curriculum enhancement, community outreach, and research mentorship. The PI's collaboration with the industry through this project will lead to practical algorithms and tools. As battery systems are central to key devices and equipment used in all sectors this research will not only promote the progress of science in battery technology but help improve technologies used in national health, prosperity, and national defense.
The scientific focus of this project is to develop a foundational mathematical framework for enabling simultaneous control and topology reconfiguration for reconfigurable battery systems from a network perspective and build a set of control algorithms that take the best advantage of reconfigurability. This research specifically includes: 1) creation of an algebraic graph-theoretic modeling methodology for reconfigurable battery systems, 2) synthesis of a predictive control framework that can optimize control input and reconfigure system-wide connections, and 3) application of the framework to key battery management tasks including optimal charging/discharging, thermal management and cell balancing. The models and algorithms will be subjected to principled evaluation through rigorous theoretical analysis, simulations and experiments.
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.
