SBIR-STTR Award

The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase I project will be the acceleration and improvement of battery manufacturing and production. Forecasting battery safety and lifetime is largely an unsolved problem in the battery industry. For manufacturers, this uncertainty increases cell cost through control measures during production as well as the precautions taken to avoid warranty events. This project proposes "data science-as-a-service" for battery formation to address both issues. By streamlining the battery formation, test, and grading process, manufacturers benefit from reduced work-in-progress (WIP) inventory waiting for final inspection, reducing facility space requirement to store WIP cell, and reducing scrap rates and increasing manufacturing yields. The impact of these improvements will potentially enable wider spread adoption of electric vehicle applications, a major driver for battery demand.This Small Business Innovation Research (SBIR) Phase I project focuses on developing information technology infrastructure and algorithms for the prediction of battery performance during cell production. By combining state-of-the-art machine learning techniques with data management and manufacturing execution systems, battery cell manufacturers will greatly reduce the cost to operate and manage cell formation and test - an environment which has been largely underserved for innovation. The proposed project objectives will be achieved through two developing battery classification and prediction machine learning algorithms to improve early detection of battery failures. Novel implementation of the proof-of-concept algorithms in battery production environments will improve the key performance indicators of these battery manufacturers. Regression and clustering models will be used as often as possible, and the bulk of the technical work will be dedicated to the feature engineering required to elucidate changes in the change and discharge voltage profile during the first few cycles. New features will be developed by a) modelling physical processes (e.g. growth of the solid-electrolyte interphase layer) expected for a given cluster group or b) employing dynamical systems techniques like time-delay embeddings.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 of this Small Business Innovation Research (SBIR) Phase II project includes enhancing US economic competitiveness, improving the health and welfare of the American public, and developing the US technical workforce. The success of this project will have a direct impact on the manufacturers, integrators, and operators of battery-powered assets. Empowering battery engineering teams with predictive analytics across their product life cycle will be a crucial competitive advantage to accelerating the scale-up of domestic battery technology development and deployment. Bringing better battery technology to market faster and ensuring a long, safe operating life will, in turn, catalyze the transition away from fossil fuels and towards electric vehicles, grid-scale energy storage, and other clean technologies. The social and economic implications include clean energy jobs, improved environmental quality, and ubiquitous low-cost energy. The potential commercial impact of this project will help accelerate the development and deployment of new battery-powered vehicles, energy storage systems, and other assets. It will allow the company to serve the wider battery industry by de-risking operation and extending service life of battery assets, thereby increasing customer revenue and avoiding costly warranty events. This Small Business Innovation Research (SBIR) Phase II project's goal is to de-risk the deployment, operation, and maintenance of battery energy storage systems. It will combine results from the Phase I with data from partners to forecast system maintenance and inform warranty design, thereby lowering the total cost of ownership and minimizing liability. Access to cell testing, outgoing quality control, and field data will allow for a deep dive across the product life cycle to identify how known degradation mechanisms manifest in the real-world battery data. Physics-informed feature engineering will be used to extend models to incorporate these insights and then implement these models at scale in the cloud. Criteria for success include: 1) correlating real-world operating conditions with known Lithium-ion battery degradation pathways, 2) engineering new features that are correlated with physics- and electrochemical-based insights, 3) accurately estimating remaining useful life to within 5% of total cycle life, and 4) implementing data-driven model in a scalable cloud environment.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.