Battery Pack Management System
PI: Simona Onori, Energy Resources Engineering
Large-scale battery packs are composed of numerous interconnected cells characterized by electrical and thermal interactions. Efficient usage of such battery packs requires estimation and control algorithms that can account for the heterogeneities between cells and the large thermal and aging dissimilarities that can be exacerbated, for example under fast charging conditions. This research is aimed at modeling the complex explicit and implicit interactions between cells in a large battery pack through the use of electrochemistry, machine learning, and an experimental campaign.
Climate change and health impacts from air pollutions from current and future battery production
Storage technologies are needed to move the world towards a sustainable, low carbon energy system. The social benefits of using storage, whether in the transportation or electricity sectors, depend on how they are used and manufactured. This project uses an integrated assessment framework to study the monetized health, environmental and climate change effects of battery manufacturing and transport to the end use application for electricity and transportation applications globally, including dependency on supply-chain, location of manufacturing and production, and local electricity grid.
Design and analysis of a flexible solid-state electrolyte
PI: Christian Linder, Civil and Environmental Engineering
Advanced battery systems with flexibility and high electrochemical performance are essential for powering next generation high-performance stretchable electronics. Solid-state electrolytes are a promising replacement for conventional inflammable and toxic liquid electrolytes and enable the use of Lithium metal as anode, thereby increasing cell voltage and energy density of the battery. However, current solid-state electrolytes cannot undergo large deformation due to its brittle nature. Therefore, it is significant to discover an intrinsically flexible solid-state electrolyte with high electrochemical performance. We will develop machine learning algorithms for the discovery of solid-state electrolyte materials with flexibility and high electrochemical performance and will study the performance and safety of the electrolyte under mechanical deformation using physics-based numerical methods.
Designing a Circular Lithium Economy: Li-Selective Materials and Processes for Recovery from Batteries
PI: William Tarpeh, Chemical Engineering
While lithium demand for batteries is rapidly increasing, surplus lithium discharges from battery production, storage, and disposal can pollute the environment. Because less than 1% of lithium is currently recycled from batteries, we reimagine lithium as a resource by designing novel membrane materials and electrochemical devices that selectively recover lithium from batteries.
Identifying Compatible Pairings of new Solid Electrolytes and Cathodes for Li-ion Batteries
This research aims to holistically innovate battery materials in novel materials spaces. Machine learning and computational evaluation of material candidates will enable guided high throughput experimental synthesis.
Polymer batteries: Towards the recycling of batteries
The efficient recovery of redox-active components from end-of-life energy storage devices is challenging due to the utilization of hazardous electrolytes and the requirement of mixed-phase electrode design. With funding from the Storage X Initiative, we will develop recyclable electrodes based on solution-process redox-active polymers. The electrode materials are designed to function in safe electrolytes to establish a facile, safe, and cost-efficient recycling process.
Techno-Economic Analysis for Valuing Battery Second Life
This project will develop a valuation model and warranty pricing approach for used BEV batteries deployed in 2nd-life stationary storage applications, based on lifecycle cost, pack performance degradation profile and degradation profile prediction error. We will investigate degradation behaviors using high throughput testing and quantify uncertainties in 2nd life performance using advanced data-driven diagnostics. We will provide a techno-economic assessment of alternative repurposing strategies, thus identifying the potential for EV pack reuse and target re-application.
Theory-guided data science for optimizing electrolyte and interphase in lithium metal battery
PI: Jian Qin, Chemical Engineering
The ever-growing demand for energy-dense storage necessitates an accelerated search for novel electrolytes and protective electrode coatings that enhance the safety and lifetime of lithium metal batteries. We aim at developing a data-driven, physics-informed correlation between fundamental material properties and battery performance based on information gathered from high-throughput molecular simulations and continuum transport analyses. By iteratively training the model, beneficial properties of electrolytes will be identified, facilitating the development of safe, reliable lithium metal batteries.