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

PFI:AIR - TT：用于可再生能源固定储能的智能电池电量计技术原型

• 批准号: 1500208
• 负责人: Mo-Yuen Chow
• 金额: $ 20万
• 依托单位: North Carolina State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-04-01 至 2020-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1500208&HistoricalAwards=false
• 关键词: PFI; AIR; TT; Prototyping; Smart

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.

该PFI：空气技术翻译项目着重于开发一种新型的智能电池量规技术，以满足对电池充电状态（SOC）的日益增长的需求以及对可再生能源资源的固定储能系统的剩余使用寿命（RUL）估计。人们对固定能量存储的需求不断增长，这是由于将可再生能源大规模整合到电网中的大规模集成驱动的驱动。但是，防止广泛固定能源存储部署的主要障碍是安全性和可靠性问题。通过提供更准确的充电状态和剩余的使用寿命估计，智能电池仪技术将提高安全性和可靠性，并在新兴可再生能源市场中广泛使用固定电池系统。这将推动更广泛的可再生能源系统的部署，这将有助于满足许多州施加的可再生投资组合标准目标。该项目将导致智能电池技术技术的软件原型，以通过市场领先的准确性和可靠性来展示其实时自适应电池SOC和RUL估算，以及用于多种不同电池化学的灵活定制。与市场上现有的电池监控方法相比，该技术生成的估计数据将为系统管理和操作提供提高的能源存储效率，可靠性，成本效益，更长的寿命以及减少资本和运营/运营/运营/维护成本。该项目解决了现有电池监控解决方案的以下缺点：1）由于不使用更新的参数，最先进的电池SOC估计方法缺乏准确性，2）最先进的电池统治估计方法由于不可靠的能源消耗和电池降解预测，并且不存在或缺乏准确性，3）最先进的电池SOC和RUL估计方法是针对特定的电池化学量身定制的。该项目通过以下领域的研究工作来解决这些局限性：1）提取准确的规则估计所需的相关数据和模型； 2）设计可以通过实时测量反馈来调整电池参数的自适应预测性规则估计算法； 3）使用可配置的电池模型的柔性电池SOC和RUL估算的开发； 4）用现有方法和竞争技术基准基准智能电池仪原型。该项目计划与国内和国际可再生​​能源公司建立合作，并向其他机构提供可再生能源和与电池相关的研究的机构。此外，参与该项目的研究生将通过原型开发和商业化活动获得技术翻译和创业经验。