SBIR Phase I: In Situ Mitigation of Dendrite Formation in Lithium Metal Batteries Using Software and Electronics

点击翻译按钮获取中文摘要

The broader impact/commercial potential of this Small Business Innovation Research (SBIR) project is the creation of a control system that enables the next generation of high capacity lithium metal batteries to replace current lithium ion battery technology. The global lithium-ion battery market is expected to grow to $67.70 billion USD by end of 2022 from $31.17 billion USD in 2016. However, lithium ion batteries cannot store sufficient energy required by the applications contributing most to that growth (i.e., electric vehicles) as demonstrated by the slow adoption within the largest battery powered product markets today. Lithium metal batteries were conceived decades ago and are capable of storing three times the energy of lithium ion batteries. Yet inherent chemical instability renders them extremely dangerous to recharge, preventing their use. This project is the next phase of work to develop a system that monitors and maintains the stability of lithium metal batteries during charging, enabling safe and reliable use by consumers, businesses, and government. The complete solution will consist of licensable hardware and software which can be tailored to specific battery powered applications, integrating with battery cells or charging systems for consumer electronics, long range electric vehicles, medical devices, and grid storage systems. This SBIR Phase I project funds the continued development of a new paradigm in battery healing: maintaining battery electrode health from the outside in. The system uses software and electronics that control surface issues on battery electrodes which otherwise cause permanent loss of capacity and life during normal use. As an important part of the overall solution being developed, the key technical hurdles addressed by this proposed SBIR project are focused on real-time electrode surface sensing and mapping capabilities and control strategies to suppress dendrites, as well as advanced characterization methods to monitor and share electrode health information with other components to ensure safety, reliability and durability of the overall energy storage system. The R&D plan will include development of live mapping of electrochemically active surfaces, control software to develop an algorithm and feedback system, and machine learning to improve sensing-mapping-control strategies. The most promising set of solutions will be demonstrated and validated in an operando visualization test cell that allows observation of the formation and suppression of dendrites on lithium metal electrodes.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.