Convenient, affordable, and safe electric vehicles have an important role in moving the automotive sector beyond primary reliance on a single energy source. However, the lithium-ion batteries that are the dominant energy storage technology in modern electric vehicles suffer from long charging times, high cost, and thermal stability concerns. This Grant Opportunity for Academic Liaison with Industry (GOALI) project will model battery degradation mechanisms and produce a mathematical framework suitable for analysis, design, and control of battery management functions such as charging and cell balancing. The central innovation that will enable new capabilities in this area is the integration of micromechanical, electrochemical, and electrothermal behaviors across time and length scales to provide accurate prediction of battery degradation. The result can help achieve capacity, power, and lifetime improvements in energy storage systems for transportation applications, thereby dramatically changing the energy landscape of the United States. Beyond advancing the fundamental academic understanding of battery physics and control, the education/research-integrated activity will provide a broad range of opportunities for graduate, undergraduate, and K-12 students to develop an interest in and learn about cutting-edge scientific research.
The research plan addresses unsolved questions essential to optimum battery management for lithium-ion batteries; how mechanical failures in battery materials affect chemical degradation, and eventually battery performance and capacity fade. Integration of high fidelity degradation mechanisms into the electrolyte-particle model will be used for the control of battery systems to maximize their performance and lifetime. This research aims at: (1) gaining a fundamental understanding of mechanical and chemical degradation mechanisms and incorporating this knowledge into a complete battery electrochemical model, (2) constructing a control-oriented model that quantitatively predicts capacity fade due to mechanical and chemical degradation mechanisms, as well as their coupled effects, and (3) leveraging this dynamic model to estimate battery state-of-health during operation based on limited signals, and utilize these estimates to optimize battery management system functions such as charging and cell balancing. If successfully realized, the solution for the fundamental challenges in energy storage systems, i.e., understanding the linkages between microscopic and macroscopic material behavior, and between failure and its control, will be realized. Further, the quantitative information regarding battery material failure on the microscopic level will be of particular use to the computational and modeling community.
