Collaborative Research: Model Reformulation for Lithium-ion Batteries -- Parameter Estimation and Dynamic Optimization

合作研究：锂离子电池模型重构——参数估计和动态优化

• 批准号: 0828002
• 负责人: Venkat Subramanian
• 金额: --
• 依托单位: Tennessee Technological University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2008
• 资助国家: 美国
• 起止时间: 2008-09-01 至 2010-02-28
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0828002&HistoricalAwards=false
• 关键词: Collaborative; Research; Model; Reformulation; Lithium

CBET-0828002SubramanianThe PIs will pursue a wide range of activities in modeling, parameter estimation and dynamic optimization of Lithium-ion batteries. While physics-based models have been widely developed and studied for these systems, the rigorous models have not been employed for parameter estimation or dynamic optimization of operating conditions. This is an unexplored area requiring model reformulation and approximations for efficient simulation of coupled partial differential equations. Along these lines, this is an in-depth analysis of model reformulation to facilitate (1) parameter estimation for understanding capacity fade of Lithium-ion batteries and (2) dynamic optimization technique to optimize the usability and efficiency of future power sources.Research ObjectivesSpecific research objectives include:* Reformulated efficient physics-based models for Lithium-ion batteries: reformulate and develop efficient models by careful analysis and analytical/approximate methods for rigorous numerical models with the aid of various advanced mathematical methods including analytical solution of banded matrix equations, decoupling coupled equations, etc.* Prediction of capacity fade in Lithium-ion batteries by keeping track of the change of transport and kinetic parameters with cycle numbers.* Optimum operating conditions: develop, validate, and implement ideal operating conditions to minimize utilization loss and maximize energy efficiency (and hence reduce the specific weight) of Lithium-ion batteries by performing dynamic optimization on reformulated-efficient models.Intellectual Merit* The reformulated physics-based models for Lithium-ion batteries will have CPU times two orders-of-magnitude lower than the current state-of-the-art.* The reformulated models are more amenable for parameter estimation and dynamicoptimization while the current state-of-the-art transport phenomena models are computationally inefficient.* The work will help predict and understand capacity fade in Lithium-ion batteries by tracking changes in the parameters with cycles. This will help design better batteries for the future. The proposed work will optimize the operating conditions of batteries for high energy and utilization efficiency.Broader Impacts* The model reformulation technique will be applicable for a wide range of engineering problems like monolith reactors, fuel cells, bioreactors, etc. Training and development of a wide range of battery/fuel cell professionals, ranging from engineers and researchers to educators. Crucial to maintaining national preeminence in the field.* Increased participation of under-represented groups in engineering research. Dissemination of results through peer-reviewed research articles, presentations and website for research modules will have an impact on student and research community worldwide.* Develop user-friendly modules to aid experimental researchers in modeling electrochemical power sources.* A new addition to the existing graduate course that trains the students in model reformulation of electrochemical power sources.

CBET-0828002 Subramanian PI将在锂离子电池的建模、参数估计和动态优化方面开展广泛的活动。虽然基于物理的模型已被广泛开发和研究，这些系统，严格的模型还没有被用于参数估计或动态优化的操作条件。这是一个未开发的领域，需要模型重新制定和近似耦合偏微分方程的有效模拟。 沿着这些路线，这是一个深入的分析模型重新制定，以促进（1）参数估计，了解容量衰减的锂离子电池和（2）动态优化技术，以优化未来电源的可用性和效率。研究目标具体的研究目标包括：* 重新制定有效的基于物理的模型，为锂离子电池：通过仔细分析和分析/近似方法，借助各种先进的数学方法，包括带状矩阵方程的解析解，解耦耦合方程，等等 *通过跟踪输运和动力学参数随循环次数的变化来预测锂离子电池的容量衰减。*最佳操作条件：开发、验证和实施理想的运行条件，通过对重新制定的高效模型进行动态优化，最大限度地降低锂离子电池的利用率损失，并最大限度地提高其能效（从而降低电池的比重）。智力优势 * 重新制定的基于物理的锂离子电池模型的CPU时间将比当前最先进的技术水平低两个数量级。*新的模型更适合于参数估计和动态优化，而目前最先进的传输现象模型计算效率低。这项工作将有助于通过跟踪参数随循环的变化来预测和理解锂离子电池的容量衰减。这将有助于为未来设计更好的电池。建议的工作将优化电池的运行条件，以获得高能量和利用效率。更广泛的影响 * 模型重构技术将适用于广泛的工程问题，如整体反应器，燃料电池，生物反应器等。培训和发展广泛的电池/燃料电池专业人员，从工程师和研究人员到教育工作者。 这对保持国家在该领域的领先地位至关重要。增加代表性不足的群体参与工程研究。通过同行评审的研究文章，演示文稿和研究模块的网站传播结果将对世界各地的学生和研究社区产生影响。开发用户友好的模块，帮助实验研究人员对电化学电源进行建模。*对现有研究生课程的新补充，培训学生重新制定电化学电源的模型。