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STTR Phase I: Predictive Control Systems for Nickel Zinc Flow Assisted Systems

Award Information
Agency: National Science Foundation
Branch: N/A
Contract: 1332030
Agency Tracking Number: 1332030
Amount: $224,901.00
Phase: Phase I
Program: STTR
Solicitation Topic Code: AS
Solicitation Number: N/A
Solicitation Year: 2013
Award Year: 2013
Award Start Date (Proposal Award Date): 2013-07-01
Award End Date (Contract End Date): 2014-06-30
Small Business Information
Greeley Sq Station, 39 W 31st St PO Box 20144
New York, NY 10001-9994
United States
DUNS: 078522589
HUBZone Owned: Yes
Woman Owned: No
Socially and Economically Disadvantaged: No
Principal Investigator
 Valerio DeAngelis
 (650) 450-2680
Business Contact
 Valerio DeAngelis
Phone: (650) 450-2680
Research Institution
 CUNY Energy Institute
 Sanjoy Banerjee
Convent Avenue at 140 Street
New York, NY 10031-
United States

 () -
 Nonprofit College or University

This Small Business Technology Transfer Research (STTR) Phase I project aims to improve predictive battery models and control systems for grid-scale energy storage applications. A typical grid-scale battery system is composed of thousands of individual batteries that may have initial material and manufacturing variations that tend to increase over time and reduce overall string efficiency, with the result that energy storage installations must be significantly over-specified, making them too expensive for many customers. There is a great opportunity to develop an integrated predictive battery modeling and control system that can determine the exact performance of each battery in operation and optimize string function. The research objectives of this SBIR project are to develop Neural Network (NN) based predictive models of battery performance using information gathered early in the life of each cell like Electrochemical Impedance Spectroscopy measurements in addition to current, voltage, and temperature measurements that can be taken throughout the life of the battery, in order to accurately estimate the state of charge and state of health of each battery in the battery string. The NN models will be incorporated into the control strategy to operate the battery string safely but aggressively, thereby decreasing the total system cost and required volume. The broader impact/commercial potential of this project is to enable grid-scale energy storage by reducing system costs. The main barrier to the adoption of energy storage on the grid is its high cost. Recent advancements in energy storage technology have resulted in lower cost, longer-life batteries capable of meeting grid requirements, though there have not been the analogous transformative improvements to battery management systems to optimize system efficiency and cost-effectiveness. The innovations supported by this SBIR will enhance scientific and technical understanding of battery function and failure modes, resulting in improved battery performance and lifetimes. The addition of energy storage to the grid will have an enormous societal impact, as storage is required to firm zero-carbon renewable sources such as wind and solar and can reduce energy prices by time-shifting energy loads. While the market for these types of stationary battery systems is currently less than $5 billion, this sector is expected to surge to approximately $100 billion in the next ten years. The dominant battery management system technology for these systems has not yet been established. Commercial advanced battery controls are the key to unlocking this market and represent the next step toward a lower carbon, more sustainable energy future.

* Information listed above is at the time of submission. *

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