PFI:AIR - TT: Prototyping a Smart Battery Gauge Technology for Stationary Energy Storage of Renewable Energy Resources

This PFI: AIR Technology Translation project focuses on developing a novel Smart Battery Gauge technology to fill the increasing need for accurate battery state of charge (SOC) and remaining useful life (RUL) estimations for stationary energy storage systems of renewable energy resources. There is a growing demand for stationary energy storage driven by the increasing interest in the large-scale integration of renewable energy into the power grid. However, major barriers preventing widespread stationary energy storage deployment are safety and reliability concerns. By providing more accurate state of charge and remaining useful life estimates, the Smart Battery Gauge technology will improve safety and reliability and enable the widespread use of stationary battery systems within the emerging renewable energy market. This will drive wider deployment of renewable energy systems, which will help meet the renewable portfolio standards targets imposed by many states. This project will result in a software prototype of the Smart Battery Gauge technology to demonstrate its real-time adaptive battery SOC and RUL estimations with market-leading accuracy and reliability, and its flexible customization for multiple different battery chemistries. As compared to the existing battery monitoring methods in the market, the estimation data generated by this technology will provide systems management and operations with the advantages of improved energy storage system efficiency, reliability, cost-effectiveness, longer lifespan, and reduced capital and operation/maintenance costs.This project addresses the following shortcomings of existing battery monitoring solutions: 1) State-of-the-art battery SOC estimation methods lack accuracy because of non-updating parameters, 2) State-of-the-art battery RUL estimation methods either do not exist or lack accuracy because of unreliable energy consumption and battery degradation predictions, and 3) State-of-the-art battery SOC and RUL estimation methods is tailored to specific battery chemistry. This project addresses these limitations through research efforts in the following areas: 1) Extraction of the relevant data and models that are needed for accurate RUL estimation; 2) Design of the adaptive predictive RUL estimation algorithm that can adjust the battery parameters with real-time measurement feedback; 3) The development of flexible battery SOC and RUL estimates using a configurable battery model; and 4) Benchmark the Smart Battery Gauge prototype with existing approaches and competing technologies. This project plans to establish collaborations with domestic and international renewable energy companies, as well as provide outreach to other institutions performing renewables and battery related research. In addition, the graduate students involved in this project will receive technology translation and entrepreneurship experiences through the prototype development and commercialization activities.