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

STTR 第一阶段：镍锌流动辅助系统的预测控制系统

• 批准号: 1332030
• 负责人: Valerio DeAngelis
• 金额: $ 22.49万
• 依托单位: Urban Electric Power Inc.
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-07-01 至 2014-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1332030&HistoricalAwards=false
• 关键词: STTR; Phase; Predictive; Control; Systems

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.

小企业技术转移研究（STTR）第一阶段项目旨在改进电网规模储能应用的预测电池模型和控制系统。典型的电网规模电池系统由数千个单独的电池组成，这些电池可能具有初始材料和制造变化，这些变化往往会随着时间的推移而增加并降低整体串效率，结果是能量存储装置必须大大超过规定，使得它们对许多客户来说过于昂贵。开发集成的预测电池建模和控制系统是一个很好的机会，该系统可以确定每个电池在运行中的确切性能并优化串功能。该SBIR项目的研究目标是开发基于神经网络（NN）的电池性能预测模型，使用在每个电池寿命早期收集的信息，如电化学阻抗谱测量，以及在电池寿命期间可以进行的电流，电压和温度测量，以便准确地估计电池串中每个电池的充电状态和健康状态。NN模型将被纳入控制策略，以安全但积极地运行电池串，从而降低总系统成本和所需体积。该项目的更广泛影响/商业潜力是通过降低系统成本实现电网规模的储能。在电网上采用储能的主要障碍是其高成本。储能技术的最新进展已经导致能够满足电网要求的成本更低，寿命更长的电池，尽管电池管理系统还没有类似的变革性改进来优化系统效率和成本效益。该SBIR支持的创新将增强对电池功能和故障模式的科学和技术理解，从而提高电池性能和寿命。在电网中增加储能将产生巨大的社会影响，因为储能是固定风能和太阳能等零碳可再生能源所必需的，并且可以通过时移能源负荷来降低能源价格。虽然这些类型的固定电池系统的市场目前不到50亿美元，但预计该行业将在未来十年内激增至约1000亿美元。这些系统的主要电池管理系统技术尚未建立。商业先进的电池控制是打开这一市场的关键，代表着迈向低碳、更可持续能源未来的下一步。