SBIR-STTR Award

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.

The broader impact/commercial potential of this Small Business Innovation Research (SBIR) project is to enable greater electric vehicle use; furthermore, the technology's potential to double battery life will reduce the environmental impact of disposed batteries. This project accelerates electric car adoption by enabling use of 100% of battery operating ranges and maximize usable energy capacity, increasing ongoing driving ranges by 50-100x. This project is a key enabler for expected growth in the global lithium-ion battery market (expected to grow to $68 B by 2022) and the annual hybrid and electric car market (forecast to exceed 10 million vehicles annually by 2025).This SBIR Phase II project proposes to optimize the technology for battery fast charge and capacity retention targets. Battery performance advancements are most often limited by chemistry and materials improvements to electrodes, electrolytes, or cell structure limiting the trade space (i.e., requiring power vs. energy tradeoffs). The proposed charging technology and associated software will selectively optimize cell design for various performance metrics by controlling electrode surface phenomena, such as lithium plating and dendrite formation, that otherwise cause permanent capacity loss during normal use and accelerate internal physical processes limiting charge rate. Technical tasks include: 1) Demonstration of performance improvements to commercial Li-Ion and fabricated Li-metal battery cells; 2) Adaptation of the process from small cells and modules to electric vehicle battery packs; 3) Development of refined sensing and feedback-based control algorithms using Predictive Learning (PL) and Machine Learning (ML) systems; 4) Verification and validation for Field Programmable Gate Array (FPGA) and System on a Chip (SoC) formats.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.