Collaborative Research: An Integrated Multiscale Reduced-Order Modeling and Experimental Framework for Lithium-ion Batteries under Mechanical Abuse Conditions

About This Grant

This grant will focus on developing an integrated computational modeling and experimental framework for simulating lithium-ion batteries (LIBs) under mechanical abuse conditions, such as impact loading. LIBs are the most used power source for electric vehicles, which leads to an ever-increasing need to improve the safety of LIBs so that they can be used in mechanical abuse conditions. To improve the safety design and ultimately reliability of advanced long life and high energy LIBs, a recent trend is to use numerical simulations as an alternative to expensive and time-consuming real-world testing for LIB response prediction under mechanical abuse conditions. However, due to the multiscale nature of LIBs and the nonlinear response of LIB components, it is computationally expensive to directly model the LIBs by accounting for the complex microstructures and nonlinear responses of different LIB components. To address this issue, the PIs plan to develop a multiscale modeling framework that better balances accuracy and efficiency for LIB modeling. The characterization and testing of LIB components at different loading conditions are also planned, which will facilitate the model development and eventually validate the computational framework. The research will also be complemented by establishing a responsive and flexible educational and outreach program based on curriculum development and summer research programs for undergraduate and high-school students with an engineering focus, as well as K-12 outreach through STEM education centers at both participating institutes. The objective of this project is to develop an integrated multiscale reduced-order modeling and experimental framework for LIBs under mechanical abuse conditions by integrating physics­-based constitutive models for LIB components with a multiscale reduced order modeling technique. To achieve this goal, the research encompasses the following three aims and plans: 1) Determine the constitutive models of battery components with full coverage of low, intermediate, and high strain rates; 2) Develop a multiscale reduced-order computational model to predict the response of LIB cells by advancing the eigendeformation-based reduced ­order homogenization model (EHM); 3) Conduct dynamic testing of battery cells to validate the developed multiscale models and exercise the validated model for LIB design and safety evaluation. The multiscale modeling framework will achieve reakthroughs in designing optimal LIB systems, which will expand the conventional boundaries of LIB performance. This project will allow the PIs to advance their current computational modeling and experimental testing expertise for LIB modeling and design, which could potentially accelerate the discovery, innovation, and certification of state-of-the-art battery technologies, and establish their long-term career in modeling and testing of complex material systems and structures. 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.