SBIR Phase I: Cloud-Based Data-Driven Predictive Analytics for Battery Performance

This Small Business Innovation Research (SBIR) Phase I project represents a major advance in battery research, combining the use of large, comprehensive battery datasets with advanced data science techniques and cloud-based software architectures to bring an unprecedented level of analytical capability to the problem of modeling battery performance. Past efforts at developing & #8232;battery performance models have relied upon manufacturers? performance data and limited single-discharge studies. This innovation applies contemporary data science techniques to the analysis of a & #8232;large, normalized set of battery data including raw time-series data and aggregated per-cycle & #8232;performance from many battery cycling tests, as well as "lab notebook" data including cell & #8232;composition, dimensions, test methods, and observations. The result will be the first commercially-available tool for conducting comprehensive, multi-parameter empirical studies of battery performance. & #8232;This unique, data-driven analytical capability will suggest new and promising research paths & #8232;distilled from relationships hidden in the data, and will help to predict battery performance & #8232;and lifetime. The broader impact/commercial potential of this project is manifested in its potential to dramatically increase the pace of product innovation & #8232;and improvement in the battery sector. Organizations developing new batteries and those integrating batteries into their products will perform more targeted and effective battery tests, and & #8232;will gain deeper insights from the data more quickly. Furthermore, our predictive analytics & #8232;module will become more effective as the total volume of battery performance data stored in & #8232;the system increases, further accelerating the pace of development. As this innovation is & #8232;applicable to the entire spectrum of battery chemistries and designs, its successful implementation and commercialization will result in improvements in performance and reliability of batteries & #8232;and battery-powered devices across a wide range of applications; from smartphones and tablets, to medical devices, to electric vehicles, and grid-scale energy storage. Broad adoption of & #8232;this software will accelerate the development and deployment of energy storage and alternative energy technologies, promoting economic growth, energy independence, and environmental benefits. Market research suggests a total addressable market of up to $600M per year for this battery & #8232;data platform with advance predictive analytics.