CAREER: Identifiability Optimization in Electrochemical Battery Systems

职业：电化学电池系统的可识别性优化

• 批准号: 2026348
• 负责人: Hosam Fathy
• 金额: $ 2.63万
• 依托单位: University of Maryland, College Park
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-08-09 至 2020-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2026348&HistoricalAwards=false
• 关键词: CAREER; Identifiability; Optimization; Electrochemical; Battery

This Faculty Early Career Development (CAREER) Program award is to examine the identifiability of electrochemical battery model parameters, defined as the degree to which one can quickly, uniquely, and accurately determine these parameters from experimental data. The research will build on previous research by the PI in battery parameter identification. The first goal is to explore different metrics for quantifying identifiability. The research will also develop algorithms for estimating both the internal state variables (e.g., degree to which a given battery is full or empty) and parameters (e.g., degree to which a battery is healthy or unhealthy) of lithium-ion batteries from experimental data. Together, these two contributions will serve as foundations for identifiability optimization. Critical information to be acquired in identifiability optimization include: the type of of sensors to be install on a lithium-ion battery to improve the detection speed and accuracy of its health degradation and the type of laboratory cycling experiments that can yield the greatest amount of information about battery model parameters with minimal time and cost.The research will have broader impact on multiple societal stakeholders. Society at large will benefit from improvements in online battery diagnostics enabled by improvements in battery parameter identifiability. Battery experimentalists will be able to obtain more accurate parameters from shorter, less expensive laboratory tests. The scientific community will benefit from the efforts to bridge current gaps among electrochemistry, online estimation, and optimal control literature. Finally, the effort will focus on outreach and education activities including: (i) the engagement of undergraduate students, especially women and underrepresented minorities through Penn State University's Schreyer Honors College; (ii) the creation of web-based courses in the areas of battery system dynamics and control; (iii) the engagement of industrial partners through complementary research efforts funded by agencies such as ARPA-E; and (iv) the creation of battery-focused educational materials for K-12 STEM outreach.

这个教师早期职业发展（CAREER）计划奖是检查电化学电池模型参数的可识别性，定义为人们可以快速，独特和准确地从实验数据确定这些参数的程度。 本研究将建立在PI在电池参数识别方面的研究基础上。 第一个目标是探索不同的度量量化可识别性。 该研究还将开发用于估计内部状态变量（例如，给定电池充满或空的程度）和参数（例如，电池健康或不健康的程度）。 总之，这两个贡献将作为可识别性优化的基础。 在可识别性优化中需要获取的关键信息包括：安装在锂离子电池上的传感器类型，以提高其健康退化的检测速度和准确性，以及可以以最少的时间和成本产生最大量的电池模型参数信息的实验室循环实验类型。该研究将对多个社会利益相关者产生更广泛的影响。 整个社会将受益于通过电池参数可识别性的改进而实现的在线电池诊断的改进。 电池实验人员将能够从更短、更便宜的实验室测试中获得更准确的参数。 科学界将受益于努力弥合电化学，在线估计和最佳控制文献之间的差距。 最后，工作重点将放在外联和教育活动上，包括：㈠通过宾夕法尼亚州立大学的Schreyer荣誉学院吸引本科生，特别是妇女和代表性不足的少数民族; ㈡在电池系统动力学和控制领域开设网络课程; ㈢通过ARPA-E等机构资助的补充研究工作吸引工业伙伴;以及（iv）为K-12 STEM外展创建以电池为重点的教育材料。