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：AIR技术翻译项目的重点是开发一种新型的智能电池测量技术，以满足对可再生能源固定储能系统的准确电池荷电状态（SOC）和剩余使用寿命（RUL）估计日益增长的需求。由于对可再生能源大规模集成到电网中的兴趣日益增加，对固定式储能的需求不断增长。然而，阻止广泛部署固定储能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使用寿命估计，智能电池测量技术将提高安全性和可靠性，并使固定电池系统在新兴的可再生能源市场中得到广泛使用。这将推动可再生能源系统的更广泛部署，这将有助于实现许多州实施的可再生能源组合标准目标。该项目将产生智能电池电量计技术的软件原型，以展示其实时自适应电池SOC和RUL估计，具有市场领先的准确性和可靠性，以及针对多种不同电池化学成分的灵活定制。与市场上现有的电池监测方法相比，该技术产生的估算数据将为系统管理和运营提供更高的储能系统效率、可靠性、成本效益、更长的寿命以及更低的资本和运营/维护成本。该项目解决了现有电池监测解决方案的以下缺点：1）现有技术的电池SOC估计方法由于不更新参数而缺乏准确性，2）现有技术的电池RUL估计方法不存在或由于不可靠的能量消耗和电池劣化预测而缺乏准确性，以及3）现有技术的电池SOC和RUL估计方法适合于特定的电池化学。本项目通过以下领域的研究工作来解决这些限制：1）提取精确RUL估计所需的相关数据和模型; 2）设计自适应预测RUL估计算法，该算法可以通过实时测量反馈来调整电池参数; 3）使用可配置的电池模型开发灵活的电池SOC和RUL估计;以及4）用现有方法和竞争技术对智能电池电量计原型进行基准测试。该项目计划与国内和国际可再生能源公司建立合作关系，并向其他从事可再生能源和电池相关研究的机构提供推广服务。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。